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879afc14a4 Board Program LEARN context, ruff 0.16, and verb-rejection observability (#700 )

* 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>

2026-07-26 15:07:28 +02:00

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Knowledge Base Search

ALL agents have access to KB/RAG tools. These are automatically available.

Recommended: Ask Mentor

For most questions, use roboco_ask_mentor:

roboco_ask_mentor(question="How do I handle authentication?")

It searches ALL knowledge sources and supports follow-up questions.

Search Types

Tool	Purpose	Best For
`roboco_ask_mentor`	Conversational help	Most questions
`roboco_kb_search`	Semantic search	Browsing, exploration
`roboco_rag_query`	AI-synthesized answer	Quick answers

Semantic Search

roboco_kb_search(
    query="rate limiting redis implementation",
    top_k=5,  # Results to return
    project="roboco-api",  # Optional project filter
    index_types=["code", "docs"],  # Filter by type
)

Returns similar content - not just keyword matches.

RAG Query (AI Answer)

roboco_rag_query(query="How does authentication work in this codebase?", top_k=5)

Returns AI-synthesized answer with citations.

Good for:

"How does X work?"
"What pattern should I use?"
"What decisions were made about Y?"

Mentor (Conversational)

# First question
response = roboco_ask_mentor(
    question="How do I handle authentication?", domain="coding"
)

# Follow-up
roboco_ask_mentor(
    question="What about refresh tokens?", conversation_id=response["conversation_id"]
)

Index Types

Type	Content
`code`	Source files
`docs`	Documentation
`journals`	Agent journal entries
`errors`	Error patterns & fixes
`standards`	Coding rules
`decisions`	Architectural decisions
`reviews`	Code review patterns
`learnings`	Captured learnings

Before Starting a Task

Always search first:

roboco_kb_search(query="implementing rate limiter")
# Journal entries are part of the KB — filter to them with index_types:
roboco_kb_search(query="rate limit decisions", index_types=["journals", "decisions"])

This helps you:

Avoid repeating mistakes
Find proven patterns
Learn from others' experiences

Proactive Context

System auto-provides context when you claim:

roboco_get_proactive_context(task_id)
# Returns: similar_tasks, relevant_learnings, code_patterns,
#          applicable_standards, recent_decisions, known_issues

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