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Renn F
ecea593a51
The RAG knowledge base (indexed and queried by agents at runtime) described entire fictional MCP tool surfaces — roboco_task_*, roboco_journal_*, roboco_message_send, roboco_notify_send, roboco_agent_*, roboco_session_*, roboco_workspace_*, roboco_project_* — that don't exist, so agents searching the KB were handed invented tool names. Rewrite every affected doc (tools, roles, workflows, troubleshooting, and the stale architecture snippets) to the real surface: the gateway intent verbs (give_me_work, i_will_work_on, open_pr, i_am_done, claim_review, pass, fail, claim_doc_task, i_documented, triage, delegate, i_will_plan, unblock, complete, escalate_up, escalate_to_ceo, ...) and content tools (commit, note(scope=...), say, dm, evidence, notify*, open_session, channels). Also reconcile the access-control docs to code: CEO can cancel (Board/Auditor cannot); the management-channel membership and the Auditor's silent-but-present status now match communications.py.
32 lines
1.0 KiB
Markdown
32 lines
1.0 KiB
Markdown
# RAG Knowledge Base Documentation
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Optimized documentation for the RoboCo AI agent knowledge base. Each file is sized for effective RAG chunking.
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## Structure
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```
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docs/rag/
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├── roles/ # Agent role responsibilities
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├── workflows/ # Step-by-step task flows
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├── standards/ # Coding, security, testing rules
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├── architecture/ # System components
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├── tools/ # MCP tools reference
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└── troubleshooting/ # Common issues and fixes
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```
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## Organization Principles
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1. **One topic per file** - Each file covers a single concept
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2. **Chunk-friendly** - Content fits in 512-1536 token chunks
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3. **Self-contained** - Each file provides complete context
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4. **Actionable** - Focus on what agents need to DO
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## For Agents
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When searching the knowledge base:
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- Use `roboco_kb_search()` for semantic search
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- Use `roboco_rag_query()` for AI-synthesized answers
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- Use `roboco_ask_mentor()` for conversational help
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See `docs/rag/tools/kb-tools.md` for the full KB tool reference.
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