muninn

name	muninn
description	Odin-Loop's memory raven. Analyzes your Odin-Loop run history and raw Claude Code session transcripts to find where you skip stages, re-work, or loop back, then proposes concrete edits to a loop's YAML. Use for `/odin-loop:odin refine`, or when the user asks to improve/tune/refine a loop based on past sessions or history.

Muninn — the self-refining loop

Muninn ("memory") is the raven that flies over your past work and reports back. This skill closes Odin-Loop's outer loop: observe how you actually work → propose edits to your loop definition → you approve → the loop improves.

Triggered by /odin-loop:odin refine [loop-name]. If no loop name is given, default to the active run's loop, else spec-harness-tdd.

Pipeline

1. Gather signals (cheap, deterministic). Run the bundled analyzer — do NOT read raw transcripts into context (they can be megabytes):

   python3 "${CLAUDE_PLUGIN_ROOT}/scripts/analyze_sessions.py" --cwd "$(pwd)"
   

   It emits compact JSON: Odin-Loop run history (loopbacks, gate failures, abandoned runs) plus raw-session heuristics (tool usage, churn_ratio, avg_turns_before_first_edit, test_runs, heavily-reworked files). It reports aggregates only — never message content.

2. Read the target loop YAML (.odin-loop/loops/<name>.yaml or the built-in loops/<name>.yaml) so proposals reference real stage ids and gates.

3. Interpret signals → proposals. Map signals to concrete, minimal edits. Prefer tightening an earlier gate over adding stages. Honest mapping:

   Signal	Likely cause	Proposed edit
   most_looped_stage = implement (high loopbacks)	spec/plan/harness too weak; rework downstream	strengthen the interview, plan, or harness-design gate check; don't just raise max_iterations
   high churn_ratio (>2.5) + test_runs ≈ 0	building without a verifiable target	recommend adopting/strengthening the harness stages; flag missing test discipline
   high avg_turns_before_first_edit	lots of deliberation before code	the interview and plan stages already capture this — confirm the plan gate is doing its job (the default loop has one), or add a plan stage if a custom loop lacks one
   runs abandoned at interview	interview too long / fatiguing	add a question cap hint to the interview prompt; split into must-have vs nice-to-have
   a gate always passes first try (no loopbacks, all ai)	gate may be rubber-stamping	suggest a stricter check, or flip to ai+human for that stage
   gate_failures_by_stage concentrated on one stage	that stage's check is hard to satisfy as written	clarify the check or split the stage

   If found_run_history is false (no Odin-Loop runs yet), rely on raw-session heuristics only and SAY SO — they are softer signals.

4. Write a proposal, do not auto-apply. Save a refinement report to .odin-loop/refinements/<timestamp>.md (use date for the stamp) containing:

   • the signals that triggered each suggestion (cite the numbers),
   • a concrete unified-diff-style before/after of the loop YAML edits,
   • a one-line rationale per edit. Then present a summary and ask:

   🦅 무닌 제안 N건. 적용하려면 /odin-loop:odin refine apply, 개별 선택/수정은 말씀해 주세요.

5. Apply only on approval. When approved, edit the loop YAML in place (bump its version), and note in the report which edits were applied.

Principles

• Propose, never impose. Same hybrid philosophy as the engine — the human approves every change. Muninn observes; it does not rewrite your loop silently.
• Signals are heuristics, not verdicts. State confidence. Don't invent a pattern the numbers don't support.
• Privacy. Work from the analyzer's aggregates. Do not dump transcript contents into context or into the report.
• Minimal edits. The smallest change that addresses the signal. Resist redesigning the whole loop from a single run.