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Reusable contribute/distill/query team knowledgebase template — git-repo-based, model-agnostic, AI-readable

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Knowledgebase

The team's shared knowledgebase. Anyone onboarding, or curious about another domain, can ask questions here with the AI tool of their choice and get answers reflecting the team's most recent thinking — including what is settled vs. still open.

New to this repo? If you're setting it up for a project (not just reading it), start with SETUP.md instead — this README is written for people asking it questions, once it's live.

Contribute in 60 seconds

Finished a brief, summary, or decision memo? Add it — no format rules, no PR:

  • With an AI tool (Claude Code, Codex, ...): say "add this to the KB" — the tool follows playbooks/contribute.md.
  • Without one: upload your file to contributions/ via your git host's web UI and commit. Done.

Drops are folded into the domain pages by a distillation pass (scheduled or on request). Staleness is this KB's biggest risk — when in doubt, contribute.

Ask a question

Clone the repo, open your AI tool in it, and ask. The tool reads START-HERE.md, the domain pages, and recent contributions, and answers with citations. Works with any tool that reads CLAUDE.md or AGENTS.md.

What is

<One paragraph: what the project is, who it's for, what problem it solves. Replace this whole section — see SETUP.md step 1.>

Structure

Path What it holds
START-HERE.md Orientation + per-domain freshness table
domains/ Current thinking, open questions, and assumptions per domain
contributions/ Drop-box for new docs (LOG.md is the ledger)
sources/ Full original docs, filed by domain, linked from domain pages
playbooks/ Model-agnostic instructions: contribute, distill, query

domains/_product.md and domains/_research.md (plus their matching sources/ folders) currently hold real example content borrowed from another project's KB, purely to illustrate the format — replace them with your own domains per SETUP.md.

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Reusable contribute/distill/query team knowledgebase template — git-repo-based, model-agnostic, AI-readable

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