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* fix(board): LEARN decisions name the item, not its per-cycle index A cycle's reject reasons are rendered into the NEXT cycle's exploration prompt, but the ref recorded alongside each reason was the item's stored id (item-0/item-1) — a per-cycle index that means something different every cycle and appears nowhere the explorer can resolve. The reason survived the loop; what it was about did not. Record the item's title instead, via a shared learn_ref() helper (falls back to the id when title-less, and reads target_task_title for Scales, whose items name the live task they mutate). * chore(lint): satisfy ruff 0.16 — keyword-only signatures and markdown formatting The dev toolchain resolved ruff 0.16.0, which stabilises PLR0917 (too many positional arguments) and formats python code blocks inside markdown. Both fired repo-wide and neither had anything to do with the code they flagged. - 36 signatures gain a `*` so their tail arguments are keyword-only, and the 104 call sites that passed them positionally are converted. mypy was the safety net for the static ones; the full suite caught nine more that only bind at runtime (the MCP tool functions, whose real callers already pass named JSON arguments). - 28 markdown files reformatted by 0.16's code-block formatter. - One RUF036 (`None` mid-union) autofixed in the GitLab provider. * fix(gateway): log the reason when a verb rejects A rejected envelope rides an HTTP 200, its body is never logged, and there is no trace table — so in the access log a verb an agent could not satisfy looks identical to one that worked. On 2026-07-25 four Board Programs (Periscope, Sentinel, Scales, Barfly) each POSTed their propose verb three or four times, persisted nothing, and left their exploration tasks PENDING; the reason was unrecoverable afterwards, from the logs or from the agents' own transcripts. Log error/message/remediate/missing plus the calling agent at envelope_to_response — the one chokepoint every v1 flow and do route returns through. Success envelopes stay silent. --------- Co-authored-by: Renn F <rennf93@users.noreply.github.com>
RAG Knowledge Base Documentation
Optimized documentation for the RoboCo AI agent knowledge base. Each file is sized for effective RAG chunking.
Structure
docs/rag/
├── roles/ # Agent role responsibilities
├── workflows/ # Step-by-step task flows
├── standards/ # Coding, security, testing rules
├── architecture/ # System components
├── tools/ # MCP tools reference
└── troubleshooting/ # Common issues and fixes
Organization Principles
- One topic per file - Each file covers a single concept
- Chunk-friendly - Content fits in 512-1536 token chunks
- Self-contained - Each file provides complete context
- Actionable - Focus on what agents need to DO
For Agents
When searching the knowledge base:
- Use
roboco_kb_search()for semantic search - Use
roboco_rag_query()for AI-synthesized answers - Use
roboco_ask_mentor()for conversational help
See docs/rag/tools/kb-tools.md for the full KB tool reference.