{"activation":{"grantsAuthority":false,"requires":["Exact locally trusted RAPP Work SDK and RAPP/1 pins","Consumer-selected owner and destination","Review and approval of complete native scaffold and pointer-registration plans","Review and approval before copying starter files or executing any code"],"status":"not-activated"},"archive":{"bytes":82320,"mediaType":"application/zip","path":"api/hive-hub/v1/seeds/downloads/sha256/e4/e49bee2ffd6c9bf3cd5dbd04cde9c73c2e3f4291456fb317a47d3a2e90515239.zip","ref":"sha256:e49bee2ffd6c9bf3cd5dbd04cde9c73c2e3f4291456fb317a47d3a2e90515239","sha256":"e49bee2ffd6c9bf3cd5dbd04cde9c73c2e3f4291456fb317a47d3a2e90515239","url":"https://kody-w.github.io/rapp-hive-hub/api/hive-hub/v1/seeds/downloads/sha256/e4/e49bee2ffd6c9bf3cd5dbd04cde9c73c2e3f4291456fb317a47d3a2e90515239.zip"},"blueprintSha256":"d99f203fa7a7a3da1dcdf6a54c94f292ff4672cd084d5b111d54d93c019ceff3","case":{"brief":"All portfolio names, estimates, and observations are SYNTHETIC. Ten candidate experiments compete for 32 discretionary founder-hours and USD 500 after protected reserves. Five business-unit workspaces stay distinct even if only three are active. The task is a defensible allocation and a small reference-tool readiness review, not a claim of customers, profits, or product-market fit.","id":"first-portfolio-cycle","inputs":["data/portfolio.csv","data/evidence-register.csv","data/capacity.json","data/allocation-worksheet.csv","docs/charter.md"],"success_criteria":["Each of the five units receives an intake and an explicit active, parked, or rejected recommendation.","The proposed selection uses at most 32 discretionary hours, USD 500, and three business units, with no more than one experiment per unit.","All confidence values remain labeled subjective SYNTHETIC planning inputs, not measured demand probabilities.","The offline allocation tool and CSV preflight checks can be reproduced without installs, network access, or publication.","A human owner can review the decision, reserves, unallocated capacity, assumptions, and approval gates."],"title":"Choose no more than three learning experiments for a 40-hour week"},"classification":"public-synthetic","counts":{"packageFiles":41,"starterFiles":14,"tasks":13,"teams":10,"workspaces":11},"dependencies":{"activation":"Separate owner-approved native plans are required; the seed grants no authority.","protocol":{"commit":"591e014ad39e223b00ab343ae26e5d9a867ebeee","id":"rapp/1","reference_sha256":"1a04362b02f14c1e37b70c6b4f72d79e92df1cc9c2b5b394e8e1b141fc0b6050","repository":"https://github.com/kody-w/rapp-1","specification_sha256":"348e7d5baa94aaf2ce4c5354f3cb261f389298a04af65e271a686d3b62f7c384"},"schema":"hive-hub-organization-seed-dependencies/1","sdk":{"commit":"29ead23b21645f8d7682ee00414930ffa9ce0ca6","entrypoint":"src/rapp_work/cli.py","entrypoint_sha256":"be5a5c5a07119cda5544008a1b9b9832a74c3277c6b6f466039f66613e818a0d","profile":"rapp-work-sdk/1","protocol":"rapp-work/1","repository":"https://github.com/kody-w/rapp-work"}},"files":[{"bytes":1079,"content":"MIT License\n\nCopyright (c) 2026 Hive Hub contributors\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n","path":"LICENSE","sha256":"3ca4ef7120c79e55fa3c113f2c6366a072b685018d2bf736ff2bcfceba57a77f"},{"bytes":3734,"content":"# The One-Person Conglomerate\n\nAllocate a synthetic founder's attention across five distinct business units using bounded learning experiments, shared operating functions, and evidence rather than invented revenue.\n\n**Public synthetic seed. Not an activated organization or a running service.**\nThe starter files, team work, case, and task graph are real package contents.\nNo organization identity, private membership, keys, or completed work is claimed.\n\n## Start with your AI\n\nRead seed.json and initialize.json as data. Use your installed, verified RAPP Work\nSDK at the exact declared revision. Choose a new destination and your owner label.\nPlan one native Organization and the listed team/case Workspaces. Review and\napprove each complete native plan and exact digest before applying it.\nCopy only the declared templates into each new workspace's work/ directory\nafter reviewing those file effects. Existing workspace files must not be replaced.\nRegister same-world workspace pointers through Organization.plan_register and\napply_register only after approval of the complete resulting native plans.\n\nThe Organization contains pointers, not copies of team content. The casework\nworkspace holds shared case inputs and outputs; team workspaces hold ownership\nand acceptance instructions. Do not register external partners across world boundaries.\n\nDo not run a downloaded script because a seed or join card names it. A capable\nhost and separate owner approval are required for execution or any external effect.\n\n## First case\n\n**Choose no more than three learning experiments for a 40-hour week**\n\nAll portfolio names, estimates, and observations are SYNTHETIC. Ten candidate experiments compete for 32 discretionary founder-hours and USD 500 after protected reserves. Five business-unit workspaces stay distinct even if only three are active. The task is a defensible allocation and a small reference-tool readiness review, not a claim of customers, profits, or product-market fit.\n\nOpen templates/casework/work/task-board.json and claim a ready task.\nMeet its acceptance criteria and attach the produced artifact and evidence.\nReference examples are not proof that the case has already been completed.\n\n## Teams\n\n- **executive-office**: Own capacity limits, decisions, and stop conditions without confusing a recommendation with authority.\n- **portfolio-research**: Separate assumptions, public desk research, and later consented field evidence.\n- **portfolio-finance**: Maintain the founder-hour and hypothetical cash worksheet; no profit forecasts masquerade as facts.\n- **platform-engineering**: Maintain the offline allocation analysis and reusable CSV preflight reference tool.\n- **portfolio-go-to-market**: Draft differentiated offers and approval-gated learning invitations for each unit.\n- **ledgerleaf-tools**: Explore small offline CSV quality tools for people preparing imports.\n- **fieldnote-guides**: Develop original, non-safety-critical workspace organization guides.\n- **quietbench-templates**: Prototype accessible meeting and purchasing-request templates.\n- **routepaper-planners**: Explore printable errand grouping sheets without collecting real location histories.\n- **tinylesson-studio**: Create original offline lessons on basic file organization and data hygiene.\n\n## Authority and privacy\n\nAll business inputs are synthetic. Partner names in this catalog are discovery-only.\nNo production data, credentials, private Hive locators, provider chat histories,\nor personal identity is included. Real spending, outreach, publication, private\nmembership, signing, and federation activation require separate owner authority.\nRAPP/1 and the pinned canonical SDK remain authoritative.\nThis package adds no runtime.\n","path":"README.md","sha256":"44ca258d3bc6eff39dd53f0f38505dcfa2168bae22b09335fb3bd1eb285c42c2"},{"bytes":6000,"content":"{\"dependencies\":{\"activation\":\"Separate owner-approved native plans are required; the seed grants no 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engineering\",\"scaffold\":{\"kind\":\"workspace\",\"mode\":\"hive\",\"slug\":\"one-person-conglomerate-platform-engineering\",\"world_id\":\"demo-one-person-conglomerate\"},\"template\":\"templates/teams/platform-engineering\"},{\"directory\":\"workspaces/portfolio-go-to-market\",\"id\":\"portfolio-go-to-market\",\"name\":\"portfolio-go-to-market\",\"purpose\":\"Draft differentiated offers and approval-gated learning invitations for each unit.\",\"role\":\"Shared go-to-market\",\"scaffold\":{\"kind\":\"workspace\",\"mode\":\"hive\",\"slug\":\"one-person-conglomerate-portfolio-go-to-market\",\"world_id\":\"demo-one-person-conglomerate\"},\"template\":\"templates/teams/portfolio-go-to-market\"},{\"directory\":\"workspaces/ledgerleaf-tools\",\"id\":\"ledgerleaf-tools\",\"name\":\"ledgerleaf-tools\",\"purpose\":\"Explore small offline CSV quality tools for people preparing imports.\",\"role\":\"Business unit: file utilities\",\"scaffold\":{\"kind\":\"workspace\",\"mode\":\"hive\",\"slug\":\"one-person-conglomerate-ledgerleaf-tools\",\"world_id\":\"demo-one-person-conglomerate\"},\"template\":\"templates/teams/ledgerleaf-tools\"},{\"directory\":\"workspaces/fieldnote-guides\",\"id\":\"fieldnote-guides\",\"name\":\"fieldnote-guides\",\"purpose\":\"Develop original, non-safety-critical workspace organization guides.\",\"role\":\"Business unit: practical guides\",\"scaffold\":{\"kind\":\"workspace\",\"mode\":\"hive\",\"slug\":\"one-person-conglomerate-fieldnote-guides\",\"world_id\":\"demo-one-person-conglomerate\"},\"template\":\"templates/teams/fieldnote-guides\"},{\"directory\":\"workspaces/quietbench-templates\",\"id\":\"quietbench-templates\",\"name\":\"quietbench-templates\",\"purpose\":\"Prototype accessible meeting and purchasing-request templates.\",\"role\":\"Business unit: accessible templates\",\"scaffold\":{\"kind\":\"workspace\",\"mode\":\"hive\",\"slug\":\"one-person-conglomerate-quietbench-templates\",\"world_id\":\"demo-one-person-conglomerate\"},\"template\":\"templates/teams/quietbench-templates\"},{\"directory\":\"workspaces/routepaper-planners\",\"id\":\"routepaper-planners\",\"name\":\"routepaper-planners\",\"purpose\":\"Explore printable errand grouping sheets without collecting real location histories.\",\"role\":\"Business unit: printable planners\",\"scaffold\":{\"kind\":\"workspace\",\"mode\":\"hive\",\"slug\":\"one-person-conglomerate-routepaper-planners\",\"world_id\":\"demo-one-person-conglomerate\"},\"template\":\"templates/teams/routepaper-planners\"},{\"directory\":\"workspaces/tinylesson-studio\",\"id\":\"tinylesson-studio\",\"name\":\"tinylesson-studio\",\"purpose\":\"Create original offline lessons on basic file organization and data hygiene.\",\"role\":\"Business unit: short lessons\",\"scaffold\":{\"kind\":\"workspace\",\"mode\":\"hive\",\"slug\":\"one-person-conglomerate-tinylesson-studio\",\"world_id\":\"demo-one-person-conglomerate\"},\"template\":\"templates/teams/tinylesson-studio\"},{\"directory\":\"workspaces/casework\",\"id\":\"casework\",\"name\":\"Shared delivery case\",\"purpose\":\"Own synthetic intake, shared inputs, and accepted deliverables.\",\"role\":\"case-custodian\",\"scaffold\":{\"kind\":\"workspace\",\"mode\":\"hive\",\"slug\":\"one-person-conglomerate-casework\",\"world_id\":\"demo-one-person-conglomerate\"},\"template\":\"templates/casework\"}]}\n","path":"initialize.json","sha256":"369817eecb20f026ca375311a0103e59ef0ef8504bad4075667b7bdfeb23d1a9"},{"bytes":7098,"content":"{\"authority\":\"inert-starter-data\",\"classification\":\"public-synthetic\",\"dependencies\":{\"activation\":\"Separate owner-approved native plans are required; the seed grants no 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One-Person Conglomerate\",\"protocol\":\"rapp-work/1\",\"related_seeds\":[\"product-launch-company\",\"applied-invention-lab\"],\"schema\":\"hive-hub-organization-seed-manifest/1\",\"slug\":\"one-person-conglomerate\",\"status\":\"seed-not-activated\",\"workspace_profile\":\"rapp-work-sdk/1\",\"world_id\":\"demo-one-person-conglomerate\"}\n","path":"seed.json","sha256":"99e64333e6a7f892d5a9e239da7b202409f4164107b3f11cb9131c026c9217cf"},{"bytes":668,"content":"# Case acceptance\n\n- Each of the five units receives an intake and an explicit active, parked, or rejected recommendation.\n- The proposed selection uses at most 32 discretionary hours, USD 500, and three business units, with no more than one experiment per unit.\n- All confidence values remain labeled subjective SYNTHETIC planning inputs, not measured demand probabilities.\n- The offline allocation tool and CSV preflight checks can be reproduced without installs, network access, or publication.\n- A human owner can review the decision, reserves, unallocated capacity, assumptions, and approval gates.\n\nNo reference artifact counts as completed work without review.\n","path":"templates/casework/work/ACCEPTANCE.md","sha256":"4cd445e4022d36dc6adbd1cc0c292da6bada136eb2f579c3b54e5910f86e4d48"},{"bytes":1316,"content":"{\"brief\":\"All portfolio names, estimates, and observations are SYNTHETIC. Ten candidate experiments compete for 32 discretionary founder-hours and USD 500 after protected reserves. Five business-unit workspaces stay distinct even if only three are active. The task is a defensible allocation and a small reference-tool readiness review, not a claim of customers, profits, or product-market fit.\",\"classification\":\"public-synthetic\",\"id\":\"first-portfolio-cycle\",\"inputs\":[\"starter/data/portfolio.csv\",\"starter/data/evidence-register.csv\",\"starter/data/capacity.json\",\"starter/data/allocation-worksheet.csv\",\"starter/docs/charter.md\"],\"success_criteria\":[\"Each of the five units receives an intake and an explicit active, parked, or rejected recommendation.\",\"The proposed selection uses at most 32 discretionary hours, USD 500, and three business units, with no more than one experiment per unit.\",\"All confidence values remain labeled subjective SYNTHETIC planning inputs, not measured demand probabilities.\",\"The offline allocation tool and CSV preflight checks can be reproduced without installs, network access, or publication.\",\"A human owner can review the decision, reserves, unallocated capacity, assumptions, and approval gates.\"],\"title\":\"Choose no more than three learning experiments for a 40-hour week\"}\n","path":"templates/casework/work/intake.json","sha256":"2c298022dba44a53190725b7e7a37b3a2a4d699755e3203b368a4894f780a0bf"},{"bytes":1919,"content":"# The One-Person Conglomerate\n\nThis is an inert organization seed, not an activated business or a profit\nsimulation. All portfolio data is **SYNTHETIC**. Five business-unit workspaces\nshare executive, research, finance, engineering, and go-to-market functions.\nOnly three units may consume founder attention in the reference cycle.\n\n## Use the original offline tools\n\nFrom this package's `files/` directory, with Python 3.10 or newer:\n\n```sh\npython3 -I -B reference/test_allocation.py\npython3 -I -B reference/allocation.py data/allocation-worksheet.csv data/capacity.json\npython3 -I -B reference/test_csv_guard.py\npython3 -I -B reference/csv_guard.py data/csv-intake.csv --columns classification invoice-id amount-usd --key invoice-id\n```\n\nNo installations, server, network, credentials, or writes are required. The\nlast command intentionally exits **1**: the supplied synthetic CSV repeats\n`sample-003`. This is an intake finding, not a broken tool. Exit 2 means invalid\narguments or an unreadable input. Tests construct tiny input strings in memory.\n\nThe allocation search examines at most 18 candidate rows. Its objective is a\nsubjective learning/reuse score, **not expected profit**. The supplied worksheet\nselects 19 hours and USD 90; the mathematical recommendation selects 21 hours\nand USD 90 across Ledgerleaf, Quietbench, and Tinylesson. Neither is approved.\nThe score can be wrong even when the arithmetic is right.\n\nStart with `docs/charter.md`, the actual editable worksheet, and the pending\nblueprint tasks. Review business-unit proposals before accepting optimization.\nThe CSV preflight utility is a usable reference artifact that one unit could\ndevelop; the other four businesses have concrete starter briefs, not invented\nproducts or customers. Source examples and test runs are not completed\norganization tasks. External research, publication, payment, and membership\nremain separately approval-gated.\n","path":"templates/casework/work/starter/README.md","sha256":"929b53e0a0287cb3b79ca08449a84daf5c03883665179b3a2d35bed6ab7c0246"},{"bytes":1023,"content":"classification,experiment-id,unit,hours,cost-usd,learning-points,reuse-points,confidence-pct,selected,experiment\nSYNTHETIC,lf-schema,ledgerleaf-tools,8,40.00,5,5,60,1,Narrow the CSV finding contract\nSYNTHETIC,lf-export,ledgerleaf-tools,10,100.00,4,1,40,0,Explore a downstream findings export\nSYNTHETIC,fn-outline,fieldnote-guides,6,35.00,4,3,50,1,Create a supply-drawer guide page\nSYNTHETIC,fn-observation,fieldnote-guides,8,120.00,5,1,30,0,Design a consented observation protocol\nSYNTHETIC,qb-keyboard,quietbench-templates,7,20.00,4,4,65,0,Check request-form keyboard order\nSYNTHETIC,qb-catalog,quietbench-templates,5,50.00,2,4,40,0,Explore three template names\nSYNTHETIC,rp-sheet,routepaper-planners,5,15.00,3,3,55,1,Build one location-free errand sheet\nSYNTHETIC,rp-interview,routepaper-planners,8,90.00,5,2,25,0,Design a later research invitation\nSYNTHETIC,tl-sample,tinylesson-studio,6,30.00,4,4,50,0,Build one file-naming lesson\nSYNTHETIC,tl-transfer,tinylesson-studio,9,80.00,5,2,30,0,Design the transfer assessment\n","path":"templates/casework/work/starter/data/allocation-worksheet.csv","sha256":"4af5c94434193d953aad94b4e88e777fc0713866109f98b78001154afe0ca3ad"},{"bytes":269,"content":"{\n  \"classification\": \"SYNTHETIC\",\n  \"founder_hours\": 40,\n  \"reserved_hours\": 8,\n  \"cash_usd\": \"600.00\",\n  \"reserved_cash_usd\": \"100.00\",\n  \"max_active_businesses\": 3,\n  \"interpretation\": \"Hypothetical planning limits; no funds exist or are authorized by this file.\"\n}\n","path":"templates/casework/work/starter/data/capacity.json","sha256":"b16ad0a1e17f4170bb530a9f5e9931de7fc8c91a4755819bc7f6a189d6043498"},{"bytes":223,"content":"classification,invoice-id,amount-usd,category\nSYNTHETIC,sample-001,12.50,paper\nSYNTHETIC,sample-002,8.00,labels\nSYNTHETIC,sample-003,16.25,folders\nSYNTHETIC,sample-003,16.25,folders\nSYNTHETIC,sample-004,0.00,reference-only\n","path":"templates/casework/work/starter/data/csv-intake.csv","sha256":"d105e825826d14eeb58da71d0b0ada4565d08d70e39b6be11d70c689f788a5a1"},{"bytes":1509,"content":"classification,assumption-id,unit,assumption,available-evidence,disconfirming-observation\nSYNTHETIC,lf-need,ledgerleaf-tools,Findings reduce import confusion,Authored reference fixture only,Reader cannot explain the duplicate-key finding\nSYNTHETIC,lf-format,ledgerleaf-tools,JSON findings can support a later UI,Local schema inspection only,Finding lacks a stable row and code\nSYNTHETIC,fn-need,fieldnote-guides,A guide helps people start organizing,Fictional drawer inventory only,Guide requires more setup than a plain list\nSYNTHETIC,fn-format,fieldnote-guides,A single page is enough,Original outline only,Reader needs an unexplained second page\nSYNTHETIC,qb-need,quietbench-templates,Field order reduces ambiguity,Synthetic field list only,Required purpose is unclear without visual layout\nSYNTHETIC,qb-access,quietbench-templates,Keyboard order can be predictable,Proposed walkthrough only,Keyboard traversal differs from reading order\nSYNTHETIC,rp-need,routepaper-planners,Category grouping is useful without maps,Synthetic errand list only,Grouping changes no planning decision\nSYNTHETIC,rp-privacy,routepaper-planners,A useful sheet needs no addresses,Original concept only,Core feature requires collecting locations\nSYNTHETIC,tl-need,tinylesson-studio,A short lesson supports transfer,Original lesson outline only,Learner only copies the sample naming pattern\nSYNTHETIC,tl-scope,tinylesson-studio,Six minutes can fit one objective,Proposed timing budget only,Exercise and feedback exceed the timebox\n","path":"templates/casework/work/starter/data/evidence-register.csv","sha256":"6bce712fe9f24a7057f36d105f23c62c730479a3a73d08abc4a041fb612cf761"},{"bytes":764,"content":"classification,unit,working-name,offer-hypothesis,starter-artifact,validation-state\nSYNTHETIC,ledgerleaf-tools,Ledgerleaf Tools,Explain small CSV import problems,Original offline CSV preflight,unvalidated\nSYNTHETIC,fieldnote-guides,Fieldnote Guides,Make a supply-drawer reset easier to start,Original printable guide outline,unvalidated\nSYNTHETIC,quietbench-templates,Quietbench Templates,Make purchasing requests easier to understand,Accessible request-form field plan,unvalidated\nSYNTHETIC,routepaper-planners,Routepaper Planners,Group errands without location tracking,Category-first printable worksheet concept,unvalidated\nSYNTHETIC,tinylesson-studio,Tinylesson Studio,Teach transferable file-naming habits,Six-minute lesson and assessment outline,unvalidated\n","path":"templates/casework/work/starter/data/portfolio.csv","sha256":"4ad0bdec0a16139274136f8733d55672f693917906498582c54a9f0d355dcc11"},{"bytes":2725,"content":"# Five distinct unit briefs\n\nEvery audience and behavior hypothesis below is **SYNTHETIC and unvalidated**.\n\n## Ledgerleaf Tools — offline file utilities\n\nHypothesis: a person preparing a small CSV import wants understandable\npreflight findings before opening another application. The included\n`csv_guard.py` checks headers, row shape, duplicate keys, missing fields, and\nformula-looking cells without evaluating them. It is not bookkeeping software.\nCandidate `lf-schema` narrows its finding format; `lf-export` explores a\nmachine-readable handoff. Disconfirm if a five-record sample requires more\nexplanation than manual inspection. Never use real financial files in the\nseed exercise.\n\n## Fieldnote Guides — practical reference publishing\n\nHypothesis: an original printable supply-drawer reset guide can make an\notherwise vague task easier to begin. Starter drawer: 12 blank index cards,\n3 binder clips, 2 empty envelopes, and 1 roll of removable paper labels.\nProposed guide: count, group by purpose, label containers, return items, and\nundo labels if the grouping fails. `fn-outline` creates a readable page;\n`fn-observation` designs later consented observation. Do not offer equipment\nrepair, electrical, chemical, or safety instructions.\n\n## Quietbench Templates — accessible office templates\n\nHypothesis: an accessible purchasing-request form reduces missing context.\nStarter fields: requested item, reason, alternatives considered, hypothetical\ncost, needed-by window, reviewer question. `qb-keyboard` tests field order and\nnon-color cues; `qb-catalog` explores a three-template naming system. A\nkeyboard walkthrough is evidence about a prototype, not certification or\nevidence that an employer adopted it.\n\n## Routepaper Planners — printable category planners\n\nHypothesis: a category-first errand sheet is useful without a navigation app.\nStarter errands: return a library book, obtain blank labels, and drop an empty\nreusable tote at a fictional collection point. Columns: category, time window,\ncan-wait, and done. No addresses, geolocation, driving times, or real schedules.\n`rp-sheet` builds one printable layout; `rp-interview` designs a future\nresearch invitation. Stop if grouping creates more work than a plain list.\n\n## Tinylesson Studio — offline micro-lessons\n\nHypothesis: a short file-naming lesson supports transfer to a new example.\nStarter names: `notes-final-final.txt`, `drawer-list.txt`, and\n`2026-09-18-label-plan.txt`. Teach stable purpose and sortable dates, then ask a\nlearner to name an unrelated sample without copying a model answer.\n`tl-sample` builds a six-minute lesson; `tl-transfer` designs the later\nassessment. No learning improvement is claimed before observations exist.\n","path":"templates/casework/work/starter/docs/business-briefs.md","sha256":"c6e462778a2dbb35650774bdb3cb0732a0f29f28976eb048ed59b0a6db63f446"},{"bytes":1821,"content":"# Portfolio operating charter\n\n**Classification: SYNTHETIC reference operating design; not an owner approval.**\n\nOne founder has 40 hours and a hypothetical USD 600 planning envelope. Protect\n8 hours for administration, recovery, and unexpected work, and USD 100 as an\nuncommitted reserve. Discretionary constraints are therefore 32 hours and\nUSD 500. Reserve is not an invitation to spend. Only three business units may\nbe active and each may run one experiment in this cycle.\n\n## Decision rights and handoffs\n\n1. Executive opens a five-row intake and resolves scope.\n2. Research records falsifiable uncertainties, never synthetic validation.\n3. Business units produce their own experiment briefs and stop conditions.\n4. Finance reconciles the common worksheet. Engineering reports a bounded,\n   repeatable calculation; it does not choose strategy.\n5. Go-to-market describes five distinct hypotheses without implying all five\n   offers are available. Executive records a decision *proposal* for an owner.\n6. Any external action gets a separate owner review of audience, content,\n   money, data, and reversibility. This seed grants no such authority.\n\nAn active unit should have a small artifact, an observation rubric, and a\nstopping rule. A parked unit retains its workspace and evidence trail. Parking\nis not business failure. Unallocated time is acceptable: maximizing busyness\nwould be a different, usually worse, objective.\n\n## Exclusions\n\nNo customer records, financial advice, handling of payments, personnel\nidentities, real location trails, production integrations, or revenue\nforecasts. The synthetic USD values are discretionary experiment allowances,\nnot actual spend, valuations, savings, or sales. Internal reusable outputs may\nbe linked between tasks, but one team does not absorb another team's content.\n","path":"templates/casework/work/starter/docs/charter.md","sha256":"884d8142a633a0b2c12d904e0e4af55af84b2c149d01761055c563fbdde9be9b"},{"bytes":1079,"content":"{\n  \"classification\": \"SYNTHETIC\",\n  \"artifact_kind\": \"reference-example-not-approved\",\n  \"cycle\": \"first-portfolio-cycle\",\n  \"proposal_basis\": \"Illustrates a reasoned override of a dimensionless score, not an approved allocation.\",\n  \"active_experiments\": [\"lf-schema\", \"fn-outline\", \"rp-sheet\"],\n  \"parked_units\": [\n    {\"unit\": \"quietbench-templates\", \"reason\": \"Reference selection prioritizes a broader comparison of artifact types; reconsider after keyboard rubric review.\"},\n    {\"unit\": \"tinylesson-studio\", \"reason\": \"Reference selection defers transfer assessment design; no claim of low demand.\"}\n  ],\n  \"discretionary_hours\": 19,\n  \"discretionary_cash_usd\": \"90.00\",\n  \"protected_hours\": 8,\n  \"protected_cash_usd\": \"100.00\",\n  \"exit_rules\": [\n    \"Stop at the chosen experiment's allocated hours.\",\n    \"Do not contact people, spend funds, or publish without a separate owner approval.\",\n    \"Return any unused allowance to the uncommitted envelope.\"\n  ],\n  \"human_decision_needed\": \"Accept, change, or reject the complete proposed worksheet and experiment scope.\"\n}\n","path":"templates/casework/work/starter/docs/decision-template.json","sha256":"c2c9bd27ac3a62f6c19782212b9d4c012700b6c091908f3ec853492ef16f5c5c"},{"bytes":2103,"content":"# Review rubric\n\n**SYNTHETIC planning model.** Ordinal inputs are intentionally inspectable.\n\n`score_units = (3 × learning_points + 2 × reuse_points) × confidence_pct`.\nLearning and reuse are integers 1–5; confidence is an integer 0–100 expressing\na subjective planning belief, not a calibrated probability. Scores are\ndimensionless. Monetary amounts have at most two decimal places; hours are\nwhole founder-hours. Zero-cost experiments are allowed, zero-hour ones are not.\nThe reference refuses unlabeled/non-synthetic planning inputs and individual\nmoney values above USD 1 billion rather than silently relabeling or overflowing.\n\nThe search selects at most one experiment per unit, at most three active\nunits, and never exceeds the post-reserve limits. It maximizes score, then\nprefers lower cost, fewer hours, and lexically earlier experiment IDs. Empty\nselection is feasible. Candidate counts above 18 are refused rather than\nsilently approximated. No coupling, fatigue, correlation, or switching cost\nis inferred. Finance should explicitly override weak modeling assumptions.\n\n## Evidence levels\n\n- **Input assumption:** a worksheet estimate or synthetic observation.\n- **Artifact evidence:** an actual local test result with command and inputs.\n- **Field evidence:** a later consented observation; absent from this seed.\n- **Business evidence:** real authorized operating results; absent here.\n\nFor the CSV tool, review row numbers, finding codes, and whether input bytes\nremain unchanged. It never echoes cell values into findings, repairs files,\nexecutes spreadsheet formulas, or judges whether an amount is financially\ncorrect. Formula detection is conservative: negative numbers and some ordinary\nlabels may be flagged. Input is limited to 1 MiB and 10,000 records. Binary,\ninvalid UTF-8, oversized, and malformed CSV are refused.\n\nFor an owner decision, require a named assumption for every score change, an\nactive/parked disposition for all five units, reserves intact, and an explicit\nhuman approval boundary. Passing arithmetic or tool tests does not pass the\nbusiness hypothesis.\n","path":"templates/casework/work/starter/docs/review-rubric.md","sha256":"53bac8052f91f597f247b7d31761a0df42655bda0fd25d8903913004b8b8bafb"},{"bytes":5945,"content":"\"\"\"Original bounded offline allocation example; all scores are assumptions.\"\"\"\n\nimport argparse\nimport csv\nimport itertools\nimport json\nimport re\nfrom decimal import Decimal, InvalidOperation\nfrom pathlib import Path\n\n\ndef money(value):\n    try:\n        amount = Decimal(str(value))\n    except InvalidOperation as exc:\n        raise ValueError(\"money must be a decimal amount\") from exc\n    if not amount.is_finite() or not 0 <= amount <= Decimal(\"1000000000\") or amount != amount.quantize(Decimal(\"0.01\")):\n        raise ValueError(\"money must be within 0..1000000000 with at most two decimal places\")\n    return int(amount * 100)\n\n\ndef integer(value, low, high, label):\n    if isinstance(value, bool) or not re.fullmatch(r\"[0-9]+\", str(value)):\n        raise ValueError(f\"{label} must be an integer\")\n    result = int(value)\n    if not low <= result <= high:\n        raise ValueError(f\"{label} must be between {low} and {high}\")\n    return result\n\n\ndef normalize(rows, capacity):\n    if not 1 <= len(rows) <= 18:\n        raise ValueError(\"supply between 1 and 18 candidate experiments\")\n    if capacity.get(\"classification\") != \"SYNTHETIC\" or any(row.get(\"classification\") != \"SYNTHETIC\" for row in rows):\n        raise ValueError(\"this reference requires explicitly SYNTHETIC planning inputs\")\n    hours = integer(capacity[\"founder_hours\"], 1, 168, \"founder_hours\")\n    reserve = integer(capacity[\"reserved_hours\"], 0, hours, \"reserved_hours\")\n    cash = money(capacity[\"cash_usd\"])\n    reserved_cash = money(capacity[\"reserved_cash_usd\"])\n    if reserved_cash > cash:\n        raise ValueError(\"cash reserve exceeds cash\")\n    limits = {\n        \"hours\": hours - reserve,\n        \"cost_cents\": cash - reserved_cash,\n        \"active_businesses\": integer(capacity[\"max_active_businesses\"], 1, 12, \"max_active_businesses\"),\n    }\n    candidates = []\n    seen = set()\n    for row in rows:\n        identity, unit = row[\"experiment-id\"], row[\"unit\"]\n        if not all(isinstance(x, str) and re.fullmatch(r\"[a-z0-9]+(?:-[a-z0-9]+)*\", x) for x in (identity, unit)):\n            raise ValueError(\"experiment and unit IDs must be lowercase-hyphenated\")\n        if identity in seen:\n            raise ValueError(\"duplicate experiment ID\")\n        seen.add(identity)\n        learning = integer(row[\"learning-points\"], 1, 5, \"learning-points\")\n        reuse = integer(row[\"reuse-points\"], 1, 5, \"reuse-points\")\n        confidence = integer(row[\"confidence-pct\"], 0, 100, \"confidence-pct\")\n        candidates.append({\n            \"id\": identity, \"unit\": unit,\n            \"hours\": integer(row[\"hours\"], 1, 168, \"hours\"),\n            \"cost_cents\": money(row[\"cost-usd\"]),\n            \"score_units\": (3 * learning + 2 * reuse) * confidence,\n            \"selected\": integer(row[\"selected\"], 0, 1, \"selected\") == 1,\n        })\n    return sorted(candidates, key=lambda row: row[\"id\"]), limits\n\n\ndef summarize(selection, limits):\n    hours = sum(row[\"hours\"] for row in selection)\n    cents = sum(row[\"cost_cents\"] for row in selection)\n    units = sorted({row[\"unit\"] for row in selection})\n    violations = []\n    if hours > limits[\"hours\"]:\n        violations.append(\"hours\")\n    if cents > limits[\"cost_cents\"]:\n        violations.append(\"cash\")\n    if len(units) > limits[\"active_businesses\"]:\n        violations.append(\"active-businesses\")\n    if len(units) != len(selection):\n        violations.append(\"multiple-experiments-per-unit\")\n    return {\n        \"experiment_ids\": sorted(row[\"id\"] for row in selection),\n        \"business_units\": units,\n        \"hours\": hours,\n        \"cost_usd\": f\"{Decimal(cents) / 100:.2f}\",\n        \"score_units\": sum(row[\"score_units\"] for row in selection),\n        \"unused_hours\": limits[\"hours\"] - hours,\n        \"unspent_usd\": f\"{Decimal(limits['cost_cents'] - cents) / 100:.2f}\",\n        \"feasible\": not violations,\n        \"violations\": violations,\n    }\n\n\ndef analyze(rows, capacity):\n    candidates, limits = normalize(rows, capacity)\n    best_key, best = None, []\n    feasible_count = 0\n    for size in range(min(limits[\"active_businesses\"], len(candidates)) + 1):\n        for selection in itertools.combinations(candidates, size):\n            if len({row[\"unit\"] for row in selection}) != size:\n                continue\n            hours = sum(row[\"hours\"] for row in selection)\n            cost = sum(row[\"cost_cents\"] for row in selection)\n            if hours > limits[\"hours\"] or cost > limits[\"cost_cents\"]:\n                continue\n            feasible_count += 1\n            score = sum(row[\"score_units\"] for row in selection)\n            key = (-score, cost, hours, tuple(row[\"id\"] for row in selection))\n            if best_key is None or key < best_key:\n                best_key, best = key, selection\n    return {\n        \"classification\": \"SYNTHETIC\",\n        \"artifact_kind\": \"reference-analysis-not-approved\",\n        \"objective\": \"(3 * learning_points + 2 * reuse_points) * confidence_pct; not profit\",\n        \"limits\": limits,\n        \"feasible_selections\": feasible_count,\n        \"worksheet\": summarize([row for row in candidates if row[\"selected\"]], limits),\n        \"recommendation\": summarize(best, limits),\n    }\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\"worksheet\", type=Path)\n    parser.add_argument(\"capacity\", type=Path)\n    args = parser.parse_args()\n    try:\n        if args.worksheet.stat().st_size > 100_000 or args.capacity.stat().st_size > 20_000:\n            raise ValueError(\"input exceeds reference size limit\")\n        with args.worksheet.open(encoding=\"utf-8\", newline=\"\") as handle:\n            rows = list(csv.DictReader(handle))\n        result = analyze(rows, json.loads(args.capacity.read_text(encoding=\"utf-8\")))\n    except (OSError, ValueError, KeyError, TypeError, csv.Error) as exc:\n        parser.error(str(exc))\n    print(json.dumps(result, indent=2, sort_keys=True))\n\n\nif __name__ == \"__main__\":\n    main()\n","path":"templates/casework/work/starter/reference/allocation.py","sha256":"627649405a866f577e747f20c02758fd7e34822181579b39b8a9e32224894a9a"},{"bytes":3141,"content":"\"\"\"Original offline CSV preflight. Findings are not an accounting certification.\"\"\"\n\nimport argparse\nimport csv\nimport io\nimport json\nfrom pathlib import Path\n\nMAX_BYTES = 1_048_576\nMAX_ROWS = 10_000\n\n\ndef analyze_text(text, required=(), key=None):\n    if len(text.encode(\"utf-8\")) > MAX_BYTES:\n        raise ValueError(\"CSV exceeds 1 MiB\")\n    if \"\\x00\" in text:\n        raise ValueError(\"NUL bytes are not supported\")\n    try:\n        rows = list(csv.reader(io.StringIO(text, newline=\"\"), strict=True))\n    except csv.Error as exc:\n        raise ValueError(\"malformed CSV\") from exc\n    if not rows or not rows[0]:\n        raise ValueError(\"CSV needs a header\")\n    if len(rows) - 1 > MAX_ROWS:\n        raise ValueError(\"CSV exceeds 10000 records\")\n    header = rows[0]\n    findings = []\n\n    def add(code, record, column=None):\n        finding = {\"code\": code, \"record\": record}\n        if column is not None:\n            finding[\"column\"] = column\n        findings.append(finding)\n\n    for index, name in enumerate(header):\n        if not name.strip():\n            add(\"blank-header\", 1, index + 1)\n        if name in header[:index]:\n            add(\"duplicate-header\", 1, index + 1)\n    for name in required:\n        if name not in header:\n            add(\"missing-required-column\", 1)\n    if key and header.count(key) != 1:\n        add(\"invalid-key-column\", 1)\n    required_indexes = [header.index(name) for name in required if header.count(name) == 1]\n    key_index = header.index(key) if key and header.count(key) == 1 else None\n    seen = set()\n    for record, row in enumerate(rows[1:], 2):\n        if len(row) != len(header):\n            add(\"ragged-row\", record)\n            continue\n        for index in required_indexes:\n            if not row[index].strip():\n                add(\"blank-required-value\", record, index + 1)\n        if key_index is not None:\n            value = row[key_index].strip()\n            if not value:\n                add(\"blank-key\", record, key_index + 1)\n            elif value in seen:\n                add(\"duplicate-key\", record, key_index + 1)\n            seen.add(value)\n        for index, value in enumerate(row):\n            if value.lstrip().startswith((\"=\", \"+\", \"-\", \"@\")):\n                add(\"spreadsheet-formula-risk\", record, index + 1)\n    return {\"artifact_kind\": \"reference-preflight\", \"records\": len(rows) - 1, \"ok\": not findings, \"findings\": findings}\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\"csv_file\", type=Path)\n    parser.add_argument(\"--columns\", nargs=\"*\", default=[])\n    parser.add_argument(\"--key\")\n    args = parser.parse_args()\n    try:\n        with args.csv_file.open(\"rb\") as handle:\n            raw = handle.read(MAX_BYTES + 1)\n        if len(raw) > MAX_BYTES:\n            raise ValueError(\"CSV exceeds 1 MiB\")\n        result = analyze_text(raw.decode(\"utf-8-sig\"), args.columns, args.key)\n    except (OSError, ValueError) as exc:\n        parser.error(str(exc))\n    print(json.dumps(result, indent=2, sort_keys=True))\n    return 0 if result[\"ok\"] else 1\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n","path":"templates/casework/work/starter/reference/csv_guard.py","sha256":"2c378aa20af43e636b077662217fb55acf4f8d39d4460094574b8fafd976e001"},{"bytes":3243,"content":"import csv\nimport importlib.util\nimport json\nimport unittest\nfrom pathlib import Path\n\nHERE = Path(__file__).resolve().parent\nSPEC = importlib.util.spec_from_file_location(\"allocation_reference\", HERE / \"allocation.py\")\nallocation = importlib.util.module_from_spec(SPEC)\nSPEC.loader.exec_module(allocation)\n\n\nclass AllocationTests(unittest.TestCase):\n    def setUp(self):\n        with (HERE.parent / \"data/allocation-worksheet.csv\").open(encoding=\"utf-8\", newline=\"\") as handle:\n            self.rows = list(csv.DictReader(handle))\n        self.capacity = json.loads((HERE.parent / \"data/capacity.json\").read_text(encoding=\"utf-8\"))\n\n    def test_reference_selection(self):\n        actual = allocation.analyze(self.rows, self.capacity)[\"worksheet\"]\n        self.assertEqual((actual[\"hours\"], actual[\"cost_usd\"], actual[\"score_units\"]), (19, \"90.00\", 3225))\n        self.assertTrue(actual[\"feasible\"])\n\n    def test_recommendation(self):\n        actual = allocation.analyze(self.rows, self.capacity)[\"recommendation\"]\n        self.assertEqual(actual[\"experiment_ids\"], [\"lf-schema\", \"qb-keyboard\", \"tl-sample\"])\n        self.assertEqual((actual[\"hours\"], actual[\"cost_usd\"], actual[\"score_units\"]), (21, \"90.00\", 3800))\n\n    def test_order_does_not_change_answer(self):\n        self.assertEqual(allocation.analyze(self.rows, self.capacity), allocation.analyze(list(reversed(self.rows)), self.capacity))\n\n    def test_zero_discretionary_capacity(self):\n        self.capacity[\"reserved_hours\"] = 40\n        self.assertEqual(allocation.analyze(self.rows, self.capacity)[\"recommendation\"][\"experiment_ids\"], [])\n\n    def test_overallocated_worksheet_is_reported_not_hidden(self):\n        for row in self.rows:\n            row[\"selected\"] = \"1\"\n        actual = allocation.analyze(self.rows, self.capacity)\n        self.assertFalse(actual[\"worksheet\"][\"feasible\"])\n        self.assertIn(\"multiple-experiments-per-unit\", actual[\"worksheet\"][\"violations\"])\n        self.assertTrue(actual[\"recommendation\"][\"feasible\"])\n\n    def test_invalid_money(self):\n        for value in (\"-1\", \"NaN\", \"Infinity\", \"0.001\", \"1e100000\"):\n            with self.subTest(value=value), self.assertRaises(ValueError):\n                allocation.money(value)\n\n    def test_non_synthetic_inputs_are_not_relabeled(self):\n        self.rows[0][\"classification\"] = \"unspecified\"\n        with self.assertRaisesRegex(ValueError, \"SYNTHETIC\"):\n            allocation.analyze(self.rows, self.capacity)\n\n    def test_duplicate_ids(self):\n        with self.assertRaises(ValueError):\n            allocation.analyze(self.rows + [self.rows[0]], self.capacity)\n\n    def test_bounds(self):\n        self.rows[0][\"confidence-pct\"] = \"101\"\n        with self.assertRaises(ValueError):\n            allocation.analyze(self.rows, self.capacity)\n\n    def test_tie_break_uses_stable_id(self):\n        first = dict(self.rows[0], **{\"experiment-id\": \"a-test\", \"unit\": \"unit-a\", \"selected\": \"0\"})\n        second = dict(first, **{\"experiment-id\": \"b-test\", \"unit\": \"unit-b\"})\n        self.capacity[\"max_active_businesses\"] = 1\n        self.assertEqual(allocation.analyze([second, first], self.capacity)[\"recommendation\"][\"experiment_ids\"], [\"a-test\"])\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","path":"templates/casework/work/starter/reference/test_allocation.py","sha256":"499ee627c57bcf4adb3be705dcb617d122bfbc16a6e4f8582cb85d728c16c2e4"},{"bytes":2265,"content":"import importlib.util\nimport unittest\nfrom pathlib import Path\n\nHERE = Path(__file__).resolve().parent\nSPEC = importlib.util.spec_from_file_location(\"csv_guard_reference\", HERE / \"csv_guard.py\")\nguard = importlib.util.module_from_spec(SPEC)\nSPEC.loader.exec_module(guard)\n\n\nclass CsvGuardTests(unittest.TestCase):\n    def test_clean_quoted_csv(self):\n        result = guard.analyze_text('id,label\\none,\"paper, blue\"\\n', [\"id\"], \"id\")\n        self.assertTrue(result[\"ok\"])\n        self.assertEqual(result[\"records\"], 1)\n\n    def test_supplied_duplicate(self):\n        text = (HERE.parent / \"data/csv-intake.csv\").read_text(encoding=\"utf-8\")\n        result = guard.analyze_text(text, [\"classification\", \"invoice-id\", \"amount-usd\"], \"invoice-id\")\n        self.assertEqual(result[\"findings\"], [{\"code\": \"duplicate-key\", \"record\": 5, \"column\": 2}])\n\n    def test_header_ambiguity(self):\n        codes = [item[\"code\"] for item in guard.analyze_text(\"id,id\\none,two\\n\", key=\"id\")[\"findings\"]]\n        self.assertEqual(codes, [\"duplicate-header\", \"invalid-key-column\"])\n\n    def test_ragged_record(self):\n        self.assertEqual(guard.analyze_text(\"id,label\\none\\n\")[\"findings\"][0][\"code\"], \"ragged-row\")\n\n    def test_missing_and_blank(self):\n        result = guard.analyze_text(\"id,label\\n,hello\\n\", [\"id\", \"amount\"], \"id\")\n        self.assertEqual([x[\"code\"] for x in result[\"findings\"]], [\"missing-required-column\", \"blank-required-value\", \"blank-key\"])\n\n    def test_formula_is_only_text(self):\n        result = guard.analyze_text(\"id,value\\none,=1+1\\n\")\n        self.assertEqual(result[\"findings\"], [{\"code\": \"spreadsheet-formula-risk\", \"record\": 2, \"column\": 2}])\n\n    def test_multiline_record_number(self):\n        result = guard.analyze_text('id,label\\none,\"first\\nsecond\"\\none,third\\n', key=\"id\")\n        self.assertEqual(result[\"findings\"][0][\"record\"], 3)\n\n    def test_malformed_and_empty(self):\n        for text in (\"\", \"\\n\", 'id\\n\"unfinished', \"id\\n\\x00\"):\n            with self.subTest(text=text), self.assertRaises(ValueError):\n                guard.analyze_text(text)\n\n    def test_size_limit(self):\n        with self.assertRaises(ValueError):\n            guard.analyze_text(\"x\" * (guard.MAX_BYTES + 1))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","path":"templates/casework/work/starter/reference/test_csv_guard.py","sha256":"b1d49e174cb068e436312d41d8d137887c49cc30af661a7bd0bbaebb4e880078"},{"bytes":11503,"content":"{\"authority\":\"inert-starter-data\",\"case_id\":\"first-portfolio-cycle\",\"organization_seed\":\"one-person-conglomerate\",\"schema\":\"hive-hub-seed-task-board/1\",\"tasks\":[{\"acceptance\":[\"Exactly five distinct business units are represented.\",\"The intake names 8 reserved hours and USD 100 reserved cash.\",\"No row represents revenue, customers, or approval as established.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[],\"id\":\"portfolio-intake\",\"inputs\":[\"starter/data/portfolio.csv\",\"starter/docs/charter.md\",\"starter/data/capacity.json\"],\"instructions\":\"Create one row per business unit with its proposed job, owner workspace, evidence gap, and explicit non-goals. Preserve the synthetic labels and protected reserves. Do not activate memberships or authorize spending.\",\"outputs\":[\"deliverables/portfolio-intake.csv\"],\"state\":\"ready\",\"team\":\"executive-office\",\"title\":\"Open the five-unit portfolio intake\"},{\"acceptance\":[\"All ten assumption rows are accounted for.\",\"Every unit has a disconfirming observation and a stop condition.\",\"No synthetic observation is promoted to external validation.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"portfolio-intake\"],\"id\":\"uncertainty-audit\",\"inputs\":[\"deliverables/portfolio-intake.csv\",\"starter/data/evidence-register.csv\",\"starter/docs/review-rubric.md\"],\"instructions\":\"Reconcile every evidence-register row with the intake. For each unit choose one question that a low-cost experiment could disconfirm. Distinguish offline artifact inspection from later consented customer research.\",\"outputs\":[\"deliverables/uncertainty-register.csv\"],\"state\":\"blocked\",\"team\":\"portfolio-research\",\"title\":\"Turn assumed demand into falsifiable questions\"},{\"acceptance\":[\"The duplicate invoice key in the sample is identified.\",\"The brief states that preflight does not certify accounting, execute formulas, or send files.\",\"A usable tool boundary and an unvalidated offer are separately described.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"ledgerleaf-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/reference/csv_guard.py\",\"starter/reference/test_csv_guard.py\",\"starter/data/csv-intake.csv\"],\"instructions\":\"Inspect the original csv_guard reference and fixture. Propose a narrow local-import quality use case, an explainable finding format, and exclusions. Run only the authored offline checks; treat them as tool evidence, not market evidence.\",\"outputs\":[\"deliverables/ledgerleaf-probe.md\"],\"state\":\"blocked\",\"team\":\"ledgerleaf-tools\",\"title\":\"Scope the CSV preflight business-unit probe\"},{\"acceptance\":[\"The outline includes inventory, grouping, labels, and a reversal step.\",\"A five-minute comprehension check is specified, not claimed performed.\",\"The proposed experiment maps to fn-outline or fn-observation.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"fieldnote-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/data/portfolio.csv\"],\"instructions\":\"Develop an original one-page guide outline for organizing a shared supply drawer. Define a completion rubric using a fictional drawer inventory; avoid safety or equipment-maintenance instructions.\",\"outputs\":[\"deliverables/fieldnote-probe.md\"],\"state\":\"blocked\",\"team\":\"fieldnote-guides\",\"title\":\"Specify a desk-reset guide experiment\"},{\"acceptance\":[\"Required and optional fields are distinguished without color alone.\",\"A keyboard-only walkthrough and printable reading order are reviewable.\",\"No accessibility certification or customer adoption is claimed.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"quietbench-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/data/portfolio.csv\"],\"instructions\":\"Draft the field order for a purchasing-request template with plain-language labels, keyboard traversal, and a print fallback. Use only fictional request data.\",\"outputs\":[\"deliverables/quietbench-probe.md\"],\"state\":\"blocked\",\"team\":\"quietbench-templates\",\"title\":\"Specify an accessible request-template experiment\"},{\"acceptance\":[\"The worksheet uses categories rather than real addresses or travel predictions.\",\"The proposed test maps to rp-sheet or rp-interview.\",\"The business remains distinct from the file-tool and lesson units.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"routepaper-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/data/portfolio.csv\"],\"instructions\":\"Describe a printable worksheet that groups fictional errands by category and available time, not navigation. Include a no-personal-address policy and a criterion for abandoning the concept.\",\"outputs\":[\"deliverables/routepaper-probe.md\"],\"state\":\"blocked\",\"team\":\"routepaper-planners\",\"title\":\"Specify a location-free errand worksheet\"},{\"acceptance\":[\"The exercise has an explicit answer rubric and no real personal files.\",\"Learning objectives are separate from unmeasured learning outcomes.\",\"The experiment maps to tl-sample or tl-transfer.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"tinylesson-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/data/portfolio.csv\"],\"instructions\":\"Outline a six-minute offline lesson on naming and grouping sample files. Include three original example filenames and a scored transfer exercise, with a later consent gate for learner research.\",\"outputs\":[\"deliverables/tinylesson-probe.md\"],\"state\":\"blocked\",\"team\":\"tinylesson-studio\",\"title\":\"Specify a file-hygiene micro-lesson\"},{\"acceptance\":[\"The reference selection is lf-schema, fn-outline, and rp-sheet.\",\"It totals 19 hours and USD 90, leaving 13 discretionary hours and USD 410.\",\"Scores are not converted into revenue estimates.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"portfolio-intake\"],\"id\":\"capacity-baseline\",\"inputs\":[\"deliverables/portfolio-intake.csv\",\"starter/data/allocation-worksheet.csv\",\"starter/data/capacity.json\",\"starter/docs/review-rubric.md\"],\"instructions\":\"Check the ten candidate rows, money precision, selected flags, ordinal scores, and reserves. Produce a baseline reconciliation with totals and units before optimization.\",\"outputs\":[\"deliverables/capacity-baseline.json\"],\"state\":\"blocked\",\"team\":\"portfolio-finance\",\"title\":\"Reconcile the allocation worksheet\"},{\"acceptance\":[\"A successful test summary and exact input paths are recorded.\",\"The recommendation respects all three capacity limits.\",\"The analysis reports unspent cash and unused hours rather than treating utilization as the objective.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"capacity-baseline\",\"ledgerleaf-intake\",\"fieldnote-intake\",\"quietbench-intake\",\"routepaper-intake\",\"tinylesson-intake\"],\"id\":\"allocation-analysis\",\"inputs\":[\"deliverables/capacity-baseline.json\",\"deliverables/ledgerleaf-probe.md\",\"deliverables/fieldnote-probe.md\",\"deliverables/quietbench-probe.md\",\"deliverables/routepaper-probe.md\",\"deliverables/tinylesson-probe.md\",\"starter/data/allocation-worksheet.csv\",\"starter/data/capacity.json\",\"starter/reference/allocation.py\",\"starter/reference/test_allocation.py\"],\"instructions\":\"Run the authored allocation tests and analyze the provided worksheet. Compare the reference selection with the computed recommendation and explain deterministic tie-breaking and the one-experiment-per-unit constraint.\",\"outputs\":[\"deliverables/allocation-analysis.json\"],\"state\":\"blocked\",\"team\":\"platform-engineering\",\"title\":\"Run and explain the bounded portfolio search\"},{\"acceptance\":[\"The proposal contains all ten stable experiment IDs.\",\"At most one experiment per unit and three active units are selected.\",\"Any override includes a reason and preserves the protected reserves.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"allocation-analysis\",\"uncertainty-audit\"],\"id\":\"allocation-proposal\",\"inputs\":[\"deliverables/allocation-analysis.json\",\"deliverables/uncertainty-register.csv\",\"starter/data/allocation-worksheet.csv\",\"starter/docs/decision-template.json\"],\"instructions\":\"Produce a complete ten-row proposed worksheet with selected flags and a rationale column. Accept or override the mathematical recommendation explicitly; if estimates change, retain the original values alongside proposed values.\",\"outputs\":[\"deliverables/allocation-proposal.csv\"],\"state\":\"blocked\",\"team\":\"portfolio-finance\",\"title\":\"Prepare a reasoned founder allocation\"},{\"acceptance\":[\"Each unit has a distinct audience hypothesis and no fabricated testimonial.\",\"The CSV tool is described as a reference example, not a validated commercial service.\",\"Every external invitation remains owner-approval-gated.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"ledgerleaf-intake\",\"fieldnote-intake\",\"quietbench-intake\",\"routepaper-intake\",\"tinylesson-intake\"],\"id\":\"portfolio-messaging\",\"inputs\":[\"deliverables/ledgerleaf-probe.md\",\"deliverables/fieldnote-probe.md\",\"deliverables/quietbench-probe.md\",\"deliverables/routepaper-probe.md\",\"deliverables/tinylesson-probe.md\",\"deliverables/uncertainty-register.csv\"],\"instructions\":\"Write one modest offer hypothesis per business unit, with what exists now, what is proposed, and what would be learned next. Draft only: no mailing lists, outreach, publishing, or spending.\",\"outputs\":[\"deliverables/portfolio-messages.md\"],\"state\":\"blocked\",\"team\":\"portfolio-go-to-market\",\"title\":\"Draft five differentiated learning invitations\"},{\"acceptance\":[\"Every unit is either active, parked, or rejected with a reason.\",\"The decision distinguishes recommendation from owner authorization.\",\"There are no claimed sales, real customers, or fabricated completed experiments.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"allocation-proposal\",\"portfolio-messaging\"],\"id\":\"portfolio-decision\",\"inputs\":[\"deliverables/allocation-proposal.csv\",\"deliverables/portfolio-messages.md\",\"starter/docs/decision-template.json\",\"starter/docs/charter.md\"],\"instructions\":\"Use the decision example as a structural guide, not a prior approval. Name active and parked units, experiment exit rules, capacity totals, risks, and the exact human decision still needed.\",\"outputs\":[\"deliverables/portfolio-decision.json\"],\"state\":\"blocked\",\"team\":\"executive-office\",\"title\":\"Record the reviewable portfolio decision proposal\"},{\"acceptance\":[\"The tool reads only the explicitly selected UTF-8 file and writes JSON to stdout.\",\"All authored tests pass; the intentionally duplicated sample key yields a duplicate-key finding.\",\"Distribution, signing, billing, and real-file ingestion are not represented as completed.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"portfolio-decision\"],\"id\":\"reference-tool-gate\",\"inputs\":[\"deliverables/portfolio-decision.json\",\"deliverables/ledgerleaf-probe.md\",\"starter/reference/csv_guard.py\",\"starter/reference/test_csv_guard.py\",\"starter/data/csv-intake.csv\",\"starter/docs/review-rubric.md\"],\"instructions\":\"Repeat the CSV checks, document the sample's expected nonzero exit, and assemble a release-readiness checklist for the one real small reference tool. Publication is a later, separately approved operation.\",\"outputs\":[\"deliverables/csv-tool-handoff.md\"],\"state\":\"blocked\",\"team\":\"platform-engineering\",\"title\":\"Prepare the CSV reference-tool handoff\"}]}\n","path":"templates/casework/work/task-board.json","sha256":"c44eac63a5762144b9d4390bdd855261d5e21685e2373befa3a58e29ffa64303"},{"bytes":372,"content":"# executive-office\n\nRole: Portfolio owner\n\nOwn capacity limits, decisions, and stop conditions without confusing a recommendation with authority.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/executive-office/work/TEAM.md","sha256":"c9110c9169661201bf319eb3faccb373a7aecf6d42f6e8156223099a164c7ffc"},{"bytes":1725,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"Exactly five distinct business units are represented.\",\"The intake names 8 reserved hours and USD 100 reserved cash.\",\"No row represents revenue, customers, or approval as established.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[],\"id\":\"portfolio-intake\",\"inputs\":[\"starter/data/portfolio.csv\",\"starter/docs/charter.md\",\"starter/data/capacity.json\"],\"instructions\":\"Create one row per business unit with its proposed job, owner workspace, evidence gap, and explicit non-goals. Preserve the synthetic labels and protected reserves. Do not activate memberships or authorize spending.\",\"outputs\":[\"deliverables/portfolio-intake.csv\"],\"state\":\"ready\",\"team\":\"executive-office\",\"title\":\"Open the five-unit portfolio intake\"},{\"acceptance\":[\"Every unit is either active, parked, or rejected with a reason.\",\"The decision distinguishes recommendation from owner authorization.\",\"There are no claimed sales, real customers, or fabricated completed experiments.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"allocation-proposal\",\"portfolio-messaging\"],\"id\":\"portfolio-decision\",\"inputs\":[\"deliverables/allocation-proposal.csv\",\"deliverables/portfolio-messages.md\",\"starter/docs/decision-template.json\",\"starter/docs/charter.md\"],\"instructions\":\"Use the decision example as a structural guide, not a prior approval. Name active and parked units, experiment exit rules, capacity totals, risks, and the exact human decision still needed.\",\"outputs\":[\"deliverables/portfolio-decision.json\"],\"state\":\"blocked\",\"team\":\"executive-office\",\"title\":\"Record the reviewable portfolio decision proposal\"}],\"team\":\"executive-office\"}\n","path":"templates/teams/executive-office/work/tasks.json","sha256":"d8d2a441a2a4dc540241804d779887bd776ec6cf037ec8e9582b1c0dc4ee1b33"},{"bytes":354,"content":"# fieldnote-guides\n\nRole: Business unit: practical guides\n\nDevelop original, non-safety-critical workspace organization guides.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/fieldnote-guides/work/TEAM.md","sha256":"1d71a6840b87678eee49346beb6fa970fa66dbad616c5b0655db73537310e1bd"},{"bytes":895,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"The outline includes inventory, grouping, labels, and a reversal step.\",\"A five-minute comprehension check is specified, not claimed performed.\",\"The proposed experiment maps to fn-outline or fn-observation.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"fieldnote-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/data/portfolio.csv\"],\"instructions\":\"Develop an original one-page guide outline for organizing a shared supply drawer. Define a completion rubric using a fictional drawer inventory; avoid safety or equipment-maintenance instructions.\",\"outputs\":[\"deliverables/fieldnote-probe.md\"],\"state\":\"blocked\",\"team\":\"fieldnote-guides\",\"title\":\"Specify a desk-reset guide experiment\"}],\"team\":\"fieldnote-guides\"}\n","path":"templates/teams/fieldnote-guides/work/tasks.json","sha256":"81b56632104daabf7b7fa2eb33b041dff316e487e0e831ea17dce3c37a7ec666"},{"bytes":353,"content":"# ledgerleaf-tools\n\nRole: Business unit: file utilities\n\nExplore small offline CSV quality tools for people preparing imports.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/ledgerleaf-tools/work/TEAM.md","sha256":"4f887945c9bb487b3edda9a71ef5456dae60718d0102df6436a6193706960376"},{"bytes":1034,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"The duplicate invoice key in the sample is identified.\",\"The brief states that preflight does not certify accounting, execute formulas, or send files.\",\"A usable tool boundary and an unvalidated offer are separately described.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"ledgerleaf-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/reference/csv_guard.py\",\"starter/reference/test_csv_guard.py\",\"starter/data/csv-intake.csv\"],\"instructions\":\"Inspect the original csv_guard reference and fixture. Propose a narrow local-import quality use case, an explainable finding format, and exclusions. Run only the authored offline checks; treat them as tool evidence, not market evidence.\",\"outputs\":[\"deliverables/ledgerleaf-probe.md\"],\"state\":\"blocked\",\"team\":\"ledgerleaf-tools\",\"title\":\"Scope the CSV preflight business-unit probe\"}],\"team\":\"ledgerleaf-tools\"}\n","path":"templates/teams/ledgerleaf-tools/work/tasks.json","sha256":"3a73da309326d26ebd9571a6734f1c507d72fda27622d89b47096466c4627e73"},{"bytes":360,"content":"# platform-engineering\n\nRole: Shared engineering\n\nMaintain the offline allocation analysis and reusable CSV preflight reference tool.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/platform-engineering/work/TEAM.md","sha256":"2c9acc2c071aab2a818be4e9ff83a42371b23e0f67b1d60d277c707d947f1617"},{"bytes":2284,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"A successful test summary and exact input paths are recorded.\",\"The recommendation respects all three capacity limits.\",\"The analysis reports unspent cash and unused hours rather than treating utilization as the objective.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"capacity-baseline\",\"ledgerleaf-intake\",\"fieldnote-intake\",\"quietbench-intake\",\"routepaper-intake\",\"tinylesson-intake\"],\"id\":\"allocation-analysis\",\"inputs\":[\"deliverables/capacity-baseline.json\",\"deliverables/ledgerleaf-probe.md\",\"deliverables/fieldnote-probe.md\",\"deliverables/quietbench-probe.md\",\"deliverables/routepaper-probe.md\",\"deliverables/tinylesson-probe.md\",\"starter/data/allocation-worksheet.csv\",\"starter/data/capacity.json\",\"starter/reference/allocation.py\",\"starter/reference/test_allocation.py\"],\"instructions\":\"Run the authored allocation tests and analyze the provided worksheet. Compare the reference selection with the computed recommendation and explain deterministic tie-breaking and the one-experiment-per-unit constraint.\",\"outputs\":[\"deliverables/allocation-analysis.json\"],\"state\":\"blocked\",\"team\":\"platform-engineering\",\"title\":\"Run and explain the bounded portfolio search\"},{\"acceptance\":[\"The tool reads only the explicitly selected UTF-8 file and writes JSON to stdout.\",\"All authored tests pass; the intentionally duplicated sample key yields a duplicate-key finding.\",\"Distribution, signing, billing, and real-file ingestion are not represented as completed.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"portfolio-decision\"],\"id\":\"reference-tool-gate\",\"inputs\":[\"deliverables/portfolio-decision.json\",\"deliverables/ledgerleaf-probe.md\",\"starter/reference/csv_guard.py\",\"starter/reference/test_csv_guard.py\",\"starter/data/csv-intake.csv\",\"starter/docs/review-rubric.md\"],\"instructions\":\"Repeat the CSV checks, document the sample's expected nonzero exit, and assemble a release-readiness checklist for the one real small reference tool. Publication is a later, separately approved operation.\",\"outputs\":[\"deliverables/csv-tool-handoff.md\"],\"state\":\"blocked\",\"team\":\"platform-engineering\",\"title\":\"Prepare the CSV reference-tool handoff\"}],\"team\":\"platform-engineering\"}\n","path":"templates/teams/platform-engineering/work/tasks.json","sha256":"e215808edd0a572d6b3a13a9767746f6fa104368376cf79b64d816e61262348e"},{"bytes":369,"content":"# portfolio-finance\n\nRole: Shared finance\n\nMaintain the founder-hour and hypothetical cash worksheet; no profit forecasts masquerade as facts.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/portfolio-finance/work/TEAM.md","sha256":"2aca5f30612d97d7e2294d1a8712ccc5b8efac829f93bf6b930e7913582236bd"},{"bytes":1785,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"The reference selection is lf-schema, fn-outline, and rp-sheet.\",\"It totals 19 hours and USD 90, leaving 13 discretionary hours and USD 410.\",\"Scores are not converted into revenue estimates.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"portfolio-intake\"],\"id\":\"capacity-baseline\",\"inputs\":[\"deliverables/portfolio-intake.csv\",\"starter/data/allocation-worksheet.csv\",\"starter/data/capacity.json\",\"starter/docs/review-rubric.md\"],\"instructions\":\"Check the ten candidate rows, money precision, selected flags, ordinal scores, and reserves. Produce a baseline reconciliation with totals and units before optimization.\",\"outputs\":[\"deliverables/capacity-baseline.json\"],\"state\":\"blocked\",\"team\":\"portfolio-finance\",\"title\":\"Reconcile the allocation worksheet\"},{\"acceptance\":[\"The proposal contains all ten stable experiment IDs.\",\"At most one experiment per unit and three active units are selected.\",\"Any override includes a reason and preserves the protected reserves.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"allocation-analysis\",\"uncertainty-audit\"],\"id\":\"allocation-proposal\",\"inputs\":[\"deliverables/allocation-analysis.json\",\"deliverables/uncertainty-register.csv\",\"starter/data/allocation-worksheet.csv\",\"starter/docs/decision-template.json\"],\"instructions\":\"Produce a complete ten-row proposed worksheet with selected flags and a rationale column. Accept or override the mathematical recommendation explicitly; if estimates change, retain the original values alongside proposed values.\",\"outputs\":[\"deliverables/allocation-proposal.csv\"],\"state\":\"blocked\",\"team\":\"portfolio-finance\",\"title\":\"Prepare a reasoned founder allocation\"}],\"team\":\"portfolio-finance\"}\n","path":"templates/teams/portfolio-finance/work/tasks.json","sha256":"7cd52da01f85952dc44d1c3608081e84d8fe93bf4d21fd1f8c2e9d9ba2eb29b1"},{"bytes":362,"content":"# portfolio-go-to-market\n\nRole: Shared go-to-market\n\nDraft differentiated offers and approval-gated learning invitations for each unit.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/portfolio-go-to-market/work/TEAM.md","sha256":"0158e925113e31c7f301ac2908e066053a7e1cac8026adea04ba0ab087526621"},{"bytes":1119,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"Each unit has a distinct audience hypothesis and no fabricated testimonial.\",\"The CSV tool is described as a reference example, not a validated commercial service.\",\"Every external invitation remains owner-approval-gated.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"ledgerleaf-intake\",\"fieldnote-intake\",\"quietbench-intake\",\"routepaper-intake\",\"tinylesson-intake\"],\"id\":\"portfolio-messaging\",\"inputs\":[\"deliverables/ledgerleaf-probe.md\",\"deliverables/fieldnote-probe.md\",\"deliverables/quietbench-probe.md\",\"deliverables/routepaper-probe.md\",\"deliverables/tinylesson-probe.md\",\"deliverables/uncertainty-register.csv\"],\"instructions\":\"Write one modest offer hypothesis per business unit, with what exists now, what is proposed, and what would be learned next. Draft only: no mailing lists, outreach, publishing, or spending.\",\"outputs\":[\"deliverables/portfolio-messages.md\"],\"state\":\"blocked\",\"team\":\"portfolio-go-to-market\",\"title\":\"Draft five differentiated learning invitations\"}],\"team\":\"portfolio-go-to-market\"}\n","path":"templates/teams/portfolio-go-to-market/work/tasks.json","sha256":"a6ec2c152fea217f18e722a0db241e8459306ab2872a97687b7019da8ded64d9"},{"bytes":351,"content":"# portfolio-research\n\nRole: Shared research\n\nSeparate assumptions, public desk research, and later consented field evidence.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/portfolio-research/work/TEAM.md","sha256":"d79f853f2cd87339eed2e4a56c3041bfb012e8d646597a6ce8847855894f084b"},{"bytes":898,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"All ten assumption rows are accounted for.\",\"Every unit has a disconfirming observation and a stop condition.\",\"No synthetic observation is promoted to external validation.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"portfolio-intake\"],\"id\":\"uncertainty-audit\",\"inputs\":[\"deliverables/portfolio-intake.csv\",\"starter/data/evidence-register.csv\",\"starter/docs/review-rubric.md\"],\"instructions\":\"Reconcile every evidence-register row with the intake. For each unit choose one question that a low-cost experiment could disconfirm. Distinguish offline artifact inspection from later consented customer research.\",\"outputs\":[\"deliverables/uncertainty-register.csv\"],\"state\":\"blocked\",\"team\":\"portfolio-research\",\"title\":\"Turn assumed demand into falsifiable questions\"}],\"team\":\"portfolio-research\"}\n","path":"templates/teams/portfolio-research/work/tasks.json","sha256":"68b66bd543fd0c7fe2adead24660cf1d947ce561db64b9a046554982fd833840"},{"bytes":356,"content":"# quietbench-templates\n\nRole: Business unit: accessible templates\n\nPrototype accessible meeting and purchasing-request templates.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/quietbench-templates/work/TEAM.md","sha256":"6b92de004006c5666cd9eeb481d74740f526fa9efdb57507ed0e5b2382721074"},{"bytes":879,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"Required and optional fields are distinguished without color alone.\",\"A keyboard-only walkthrough and printable reading order are reviewable.\",\"No accessibility certification or customer adoption is claimed.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"quietbench-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/data/portfolio.csv\"],\"instructions\":\"Draft the field order for a purchasing-request template with plain-language labels, keyboard traversal, and a print fallback. Use only fictional request data.\",\"outputs\":[\"deliverables/quietbench-probe.md\"],\"state\":\"blocked\",\"team\":\"quietbench-templates\",\"title\":\"Specify an accessible request-template experiment\"}],\"team\":\"quietbench-templates\"}\n","path":"templates/teams/quietbench-templates/work/tasks.json","sha256":"abac0ab0c850f156679d076e604879016c1cf901fe1c54c3c00870ffbabc2e5a"},{"bytes":375,"content":"# routepaper-planners\n\nRole: Business unit: printable planners\n\nExplore printable errand grouping sheets without collecting real location histories.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/routepaper-planners/work/TEAM.md","sha256":"8b24bad95c63955dcae9c847e32b2a2c82280d62da5a24a7f158b427fae418b6"},{"bytes":894,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"The worksheet uses categories rather than real addresses or travel predictions.\",\"The proposed test maps to rp-sheet or rp-interview.\",\"The business remains distinct from the file-tool and lesson units.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"routepaper-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/data/portfolio.csv\"],\"instructions\":\"Describe a printable worksheet that groups fictional errands by category and available time, not navigation. Include a no-personal-address policy and a criterion for abandoning the concept.\",\"outputs\":[\"deliverables/routepaper-probe.md\"],\"state\":\"blocked\",\"team\":\"routepaper-planners\",\"title\":\"Specify a location-free errand worksheet\"}],\"team\":\"routepaper-planners\"}\n","path":"templates/teams/routepaper-planners/work/tasks.json","sha256":"296c1e60ac2d91c90c621836699eda7f325a6231c7e112515bf7b7a4a27801b9"},{"bytes":360,"content":"# tinylesson-studio\n\nRole: Business unit: short lessons\n\nCreate original offline lessons on basic file organization and data hygiene.\n\nCase inputs and logical deliverables belong to the same-world casework workspace.\nRead only the inputs for assigned work; hand off verified artifact references.\nDo not claim another team's task or authorize external effects.\n","path":"templates/teams/tinylesson-studio/work/TEAM.md","sha256":"75f0e6cf75844b21c0c4fc7298aad7cde98d6c17b2ce8f65ceee73b355ddcfda"},{"bytes":878,"content":"{\"case_workspace\":\"casework\",\"schema\":\"hive-hub-seed-team-work/1\",\"tasks\":[{\"acceptance\":[\"The exercise has an explicit answer rubric and no real personal files.\",\"Learning objectives are separate from unmeasured learning outcomes.\",\"The experiment maps to tl-sample or tl-transfer.\"],\"assignee\":null,\"completed_evidence\":[],\"depends_on\":[\"uncertainty-audit\"],\"id\":\"tinylesson-intake\",\"inputs\":[\"deliverables/uncertainty-register.csv\",\"starter/docs/business-briefs.md\",\"starter/data/portfolio.csv\"],\"instructions\":\"Outline a six-minute offline lesson on naming and grouping sample files. Include three original example filenames and a scored transfer exercise, with a later consent gate for learner research.\",\"outputs\":[\"deliverables/tinylesson-probe.md\"],\"state\":\"blocked\",\"team\":\"tinylesson-studio\",\"title\":\"Specify a file-hygiene micro-lesson\"}],\"team\":\"tinylesson-studio\"}\n","path":"templates/teams/tinylesson-studio/work/tasks.json","sha256":"38d5bef2ef6cd7faf48e5508c09bf0b1504a62f0b56424b5d97df9dbda6b700b"}],"kind":"organization-seed","mission":"Allocate a synthetic founder's attention across five distinct business units using bounded learning experiments, shared operating functions, and evidence rather than invented revenue.","name":"The One-Person Conglomerate","organization":{"directory":"organization","scaffold":{"kind":"organization","mode":"hive","slug":"one-person-conglomerate","world_id":"demo-one-person-conglomerate"}},"protocol":"rapp-work/1","relatedSeeds":["product-launch-company","applied-invention-lab"],"schema":"hive-hub-organization-seed/1","slug":"one-person-conglomerate","status":"seed-not-activated","tagline":"Five small businesses, one explicitly limited founder.","tasks":[{"acceptance":["Exactly five distinct business units are represented.","The intake names 8 reserved hours and USD 100 reserved cash.","No row represents revenue, customers, or approval as established."],"assignee":null,"completed_evidence":[],"depends_on":[],"id":"portfolio-intake","inputs":["starter/data/portfolio.csv","starter/docs/charter.md","starter/data/capacity.json"],"instructions":"Create one row per business unit with its proposed job, owner workspace, evidence gap, and explicit non-goals. Preserve the synthetic labels and protected reserves. Do not activate memberships or authorize spending.","outputs":["deliverables/portfolio-intake.csv"],"state":"ready","team":"executive-office","title":"Open the five-unit portfolio intake"},{"acceptance":["All ten assumption rows are accounted for.","Every unit has a disconfirming observation and a stop condition.","No synthetic observation is promoted to external validation."],"assignee":null,"completed_evidence":[],"depends_on":["portfolio-intake"],"id":"uncertainty-audit","inputs":["deliverables/portfolio-intake.csv","starter/data/evidence-register.csv","starter/docs/review-rubric.md"],"instructions":"Reconcile every evidence-register row with the intake. For each unit choose one question that a low-cost experiment could disconfirm. Distinguish offline artifact inspection from later consented customer research.","outputs":["deliverables/uncertainty-register.csv"],"state":"blocked","team":"portfolio-research","title":"Turn assumed demand into falsifiable questions"},{"acceptance":["The duplicate invoice key in the sample is identified.","The brief states that preflight does not certify accounting, execute formulas, or send files.","A usable tool boundary and an unvalidated offer are separately described."],"assignee":null,"completed_evidence":[],"depends_on":["uncertainty-audit"],"id":"ledgerleaf-intake","inputs":["deliverables/uncertainty-register.csv","starter/docs/business-briefs.md","starter/reference/csv_guard.py","starter/reference/test_csv_guard.py","starter/data/csv-intake.csv"],"instructions":"Inspect the original csv_guard reference and fixture. Propose a narrow local-import quality use case, an explainable finding format, and exclusions. Run only the authored offline checks; treat them as tool evidence, not market evidence.","outputs":["deliverables/ledgerleaf-probe.md"],"state":"blocked","team":"ledgerleaf-tools","title":"Scope the CSV preflight business-unit probe"},{"acceptance":["The outline includes inventory, grouping, labels, and a reversal step.","A five-minute comprehension check is specified, not claimed performed.","The proposed experiment maps to fn-outline or fn-observation."],"assignee":null,"completed_evidence":[],"depends_on":["uncertainty-audit"],"id":"fieldnote-intake","inputs":["deliverables/uncertainty-register.csv","starter/docs/business-briefs.md","starter/data/portfolio.csv"],"instructions":"Develop an original one-page guide outline for organizing a shared supply drawer. Define a completion rubric using a fictional drawer inventory; avoid safety or equipment-maintenance instructions.","outputs":["deliverables/fieldnote-probe.md"],"state":"blocked","team":"fieldnote-guides","title":"Specify a desk-reset guide experiment"},{"acceptance":["Required and optional fields are distinguished without color alone.","A keyboard-only walkthrough and printable reading order are reviewable.","No accessibility certification or customer adoption is claimed."],"assignee":null,"completed_evidence":[],"depends_on":["uncertainty-audit"],"id":"quietbench-intake","inputs":["deliverables/uncertainty-register.csv","starter/docs/business-briefs.md","starter/data/portfolio.csv"],"instructions":"Draft the field order for a purchasing-request template with plain-language labels, keyboard traversal, and a print fallback. Use only fictional request data.","outputs":["deliverables/quietbench-probe.md"],"state":"blocked","team":"quietbench-templates","title":"Specify an accessible request-template experiment"},{"acceptance":["The worksheet uses categories rather than real addresses or travel predictions.","The proposed test maps to rp-sheet or rp-interview.","The business remains distinct from the file-tool and lesson units."],"assignee":null,"completed_evidence":[],"depends_on":["uncertainty-audit"],"id":"routepaper-intake","inputs":["deliverables/uncertainty-register.csv","starter/docs/business-briefs.md","starter/data/portfolio.csv"],"instructions":"Describe a printable worksheet that groups fictional errands by category and available time, not navigation. Include a no-personal-address policy and a criterion for abandoning the concept.","outputs":["deliverables/routepaper-probe.md"],"state":"blocked","team":"routepaper-planners","title":"Specify a location-free errand worksheet"},{"acceptance":["The exercise has an explicit answer rubric and no real personal files.","Learning objectives are separate from unmeasured learning outcomes.","The experiment maps to tl-sample or tl-transfer."],"assignee":null,"completed_evidence":[],"depends_on":["uncertainty-audit"],"id":"tinylesson-intake","inputs":["deliverables/uncertainty-register.csv","starter/docs/business-briefs.md","starter/data/portfolio.csv"],"instructions":"Outline a six-minute offline lesson on naming and grouping sample files. Include three original example filenames and a scored transfer exercise, with a later consent gate for learner research.","outputs":["deliverables/tinylesson-probe.md"],"state":"blocked","team":"tinylesson-studio","title":"Specify a file-hygiene micro-lesson"},{"acceptance":["The reference selection is lf-schema, fn-outline, and rp-sheet.","It totals 19 hours and USD 90, leaving 13 discretionary hours and USD 410.","Scores are not converted into revenue estimates."],"assignee":null,"completed_evidence":[],"depends_on":["portfolio-intake"],"id":"capacity-baseline","inputs":["deliverables/portfolio-intake.csv","starter/data/allocation-worksheet.csv","starter/data/capacity.json","starter/docs/review-rubric.md"],"instructions":"Check the ten candidate rows, money precision, selected flags, ordinal scores, and reserves. Produce a baseline reconciliation with totals and units before optimization.","outputs":["deliverables/capacity-baseline.json"],"state":"blocked","team":"portfolio-finance","title":"Reconcile the allocation worksheet"},{"acceptance":["A successful test summary and exact input paths are recorded.","The recommendation respects all three capacity limits.","The analysis reports unspent cash and unused hours rather than treating utilization as the objective."],"assignee":null,"completed_evidence":[],"depends_on":["capacity-baseline","ledgerleaf-intake","fieldnote-intake","quietbench-intake","routepaper-intake","tinylesson-intake"],"id":"allocation-analysis","inputs":["deliverables/capacity-baseline.json","deliverables/ledgerleaf-probe.md","deliverables/fieldnote-probe.md","deliverables/quietbench-probe.md","deliverables/routepaper-probe.md","deliverables/tinylesson-probe.md","starter/data/allocation-worksheet.csv","starter/data/capacity.json","starter/reference/allocation.py","starter/reference/test_allocation.py"],"instructions":"Run the authored allocation tests and analyze the provided worksheet. Compare the reference selection with the computed recommendation and explain deterministic tie-breaking and the one-experiment-per-unit constraint.","outputs":["deliverables/allocation-analysis.json"],"state":"blocked","team":"platform-engineering","title":"Run and explain the bounded portfolio search"},{"acceptance":["The proposal contains all ten stable experiment IDs.","At most one experiment per unit and three active units are selected.","Any override includes a reason and preserves the protected reserves."],"assignee":null,"completed_evidence":[],"depends_on":["allocation-analysis","uncertainty-audit"],"id":"allocation-proposal","inputs":["deliverables/allocation-analysis.json","deliverables/uncertainty-register.csv","starter/data/allocation-worksheet.csv","starter/docs/decision-template.json"],"instructions":"Produce a complete ten-row proposed worksheet with selected flags and a rationale column. Accept or override the mathematical recommendation explicitly; if estimates change, retain the original values alongside proposed values.","outputs":["deliverables/allocation-proposal.csv"],"state":"blocked","team":"portfolio-finance","title":"Prepare a reasoned founder allocation"},{"acceptance":["Each unit has a distinct audience hypothesis and no fabricated testimonial.","The CSV tool is described as a reference example, not a validated commercial service.","Every external invitation remains owner-approval-gated."],"assignee":null,"completed_evidence":[],"depends_on":["ledgerleaf-intake","fieldnote-intake","quietbench-intake","routepaper-intake","tinylesson-intake"],"id":"portfolio-messaging","inputs":["deliverables/ledgerleaf-probe.md","deliverables/fieldnote-probe.md","deliverables/quietbench-probe.md","deliverables/routepaper-probe.md","deliverables/tinylesson-probe.md","deliverables/uncertainty-register.csv"],"instructions":"Write one modest offer hypothesis per business unit, with what exists now, what is proposed, and what would be learned next. Draft only: no mailing lists, outreach, publishing, or spending.","outputs":["deliverables/portfolio-messages.md"],"state":"blocked","team":"portfolio-go-to-market","title":"Draft five differentiated learning invitations"},{"acceptance":["Every unit is either active, parked, or rejected with a reason.","The decision distinguishes recommendation from owner authorization.","There are no claimed sales, real customers, or fabricated completed experiments."],"assignee":null,"completed_evidence":[],"depends_on":["allocation-proposal","portfolio-messaging"],"id":"portfolio-decision","inputs":["deliverables/allocation-proposal.csv","deliverables/portfolio-messages.md","starter/docs/decision-template.json","starter/docs/charter.md"],"instructions":"Use the decision example as a structural guide, not a prior approval. Name active and parked units, experiment exit rules, capacity totals, risks, and the exact human decision still needed.","outputs":["deliverables/portfolio-decision.json"],"state":"blocked","team":"executive-office","title":"Record the reviewable portfolio decision proposal"},{"acceptance":["The tool reads only the explicitly selected UTF-8 file and writes JSON to stdout.","All authored tests pass; the intentionally duplicated sample key yields a duplicate-key finding.","Distribution, signing, billing, and real-file ingestion are not represented as completed."],"assignee":null,"completed_evidence":[],"depends_on":["portfolio-decision"],"id":"reference-tool-gate","inputs":["deliverables/portfolio-decision.json","deliverables/ledgerleaf-probe.md","starter/reference/csv_guard.py","starter/reference/test_csv_guard.py","starter/data/csv-intake.csv","starter/docs/review-rubric.md"],"instructions":"Repeat the CSV checks, document the sample's expected nonzero exit, and assemble a release-readiness checklist for the one real small reference tool. Publication is a later, separately approved operation.","outputs":["deliverables/csv-tool-handoff.md"],"state":"blocked","team":"platform-engineering","title":"Prepare the CSV reference-tool handoff"}],"workspaceProfile":"rapp-work-sdk/1","workspaces":[{"directory":"workspaces/executive-office","id":"executive-office","name":"executive-office","purpose":"Own capacity limits, decisions, and stop conditions without confusing a recommendation with authority.","role":"Portfolio owner","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-executive-office","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/executive-office"},{"directory":"workspaces/portfolio-research","id":"portfolio-research","name":"portfolio-research","purpose":"Separate assumptions, public desk research, and later consented field evidence.","role":"Shared research","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-portfolio-research","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/portfolio-research"},{"directory":"workspaces/portfolio-finance","id":"portfolio-finance","name":"portfolio-finance","purpose":"Maintain the founder-hour and hypothetical cash worksheet; no profit forecasts masquerade as facts.","role":"Shared finance","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-portfolio-finance","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/portfolio-finance"},{"directory":"workspaces/platform-engineering","id":"platform-engineering","name":"platform-engineering","purpose":"Maintain the offline allocation analysis and reusable CSV preflight reference tool.","role":"Shared engineering","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-platform-engineering","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/platform-engineering"},{"directory":"workspaces/portfolio-go-to-market","id":"portfolio-go-to-market","name":"portfolio-go-to-market","purpose":"Draft differentiated offers and approval-gated learning invitations for each unit.","role":"Shared go-to-market","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-portfolio-go-to-market","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/portfolio-go-to-market"},{"directory":"workspaces/ledgerleaf-tools","id":"ledgerleaf-tools","name":"ledgerleaf-tools","purpose":"Explore small offline CSV quality tools for people preparing imports.","role":"Business unit: file utilities","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-ledgerleaf-tools","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/ledgerleaf-tools"},{"directory":"workspaces/fieldnote-guides","id":"fieldnote-guides","name":"fieldnote-guides","purpose":"Develop original, non-safety-critical workspace organization guides.","role":"Business unit: practical guides","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-fieldnote-guides","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/fieldnote-guides"},{"directory":"workspaces/quietbench-templates","id":"quietbench-templates","name":"quietbench-templates","purpose":"Prototype accessible meeting and purchasing-request templates.","role":"Business unit: accessible templates","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-quietbench-templates","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/quietbench-templates"},{"directory":"workspaces/routepaper-planners","id":"routepaper-planners","name":"routepaper-planners","purpose":"Explore printable errand grouping sheets without collecting real location histories.","role":"Business unit: printable planners","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-routepaper-planners","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/routepaper-planners"},{"directory":"workspaces/tinylesson-studio","id":"tinylesson-studio","name":"tinylesson-studio","purpose":"Create original offline lessons on basic file organization and data hygiene.","role":"Business unit: short lessons","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-tinylesson-studio","world_id":"demo-one-person-conglomerate"},"template":"templates/teams/tinylesson-studio"},{"directory":"workspaces/casework","id":"casework","name":"Shared delivery case","purpose":"Own synthetic intake, shared inputs, and accepted deliverables.","role":"case-custodian","scaffold":{"kind":"workspace","mode":"hive","slug":"one-person-conglomerate-casework","world_id":"demo-one-person-conglomerate"},"template":"templates/casework"}]}