Rules Distill

This is an OMH rules-distill workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

rules-distill gives OMH a disciplined way to learn from large skill ecosystems like ECC without wholesale copying: extract principles, review them, then patch OMH only through explicit verified work.

Do Not Use When

• The user wants a single workflow route regression; use workflow-learning.
• The user wants durable factual project memory; use wiki or memory curation.
• The user already approved a concrete code/doc change; use the implementation workflow.
• The ask is writing or keeping current the file an agent reads at startup -- AGENTS.md, CLAUDE.md, a Cursor rule; use agent-instructions, which updates only its marked region.

Examples

Good example:

• Prompt: rules-distill 최근 실패 trace와 스킬들을 보고 OMH AGENTS에 넣을 만한 반복 원칙 후보만 뽑아줘.
• Expected behavior: Prepare principle_candidate_set/v1, duplication/conflict report, review queue, and approved patch handoff only after approval.
• Why: The request is meta-guidance learning and needs review before mutating rules.

Bad example:

• Prompt: rules-distill 한 번 본 실패를 바로 모든 스킬 규칙으로 써버려.
• Expected behavior: Keep it as a low-confidence candidate or regression case until repeated evidence and review approval exist.
• Why: Rule distillation should not turn one-off anecdotes into global behavior.

Completion Checklist

• The durable fact, source evidence, retrieval hint, and staleness risk are recorded.
• Uncertain or conflicting knowledge is marked as review-needed rather than permanent truth.
• Separate coding or docs tasks are extracted instead of buried in notes.

Recovery Notes

• If source evidence conflicts, route to memory or knowledge review before writing durable guidance.
• If the fact may be stale, record the staleness warning and next refresh action.

Use When

Use when Hermes should turn repeated workflow lessons, skill behavior, review comments, or failure traces into candidate rules that humans can review before docs or catalog changes.

Strong routing signals: `rules-distill`, `rules distill`, `distill rules`, `rule distillation`, `principle distill`, `skill principles`, `extract agent rules`, `turn traces into rules`, `policy distill`, `guidance distill`, `규칙 증류`, `원칙 추출`, `스킬 원칙`, `프롬프트 규칙`

Catalog Metadata

Category: knowledge Phase: rules-distillation Quality tier: rules-distillation-gated Reasoning demand: light

Quality bar:

• Collect repeated evidence before proposing a rule.
• Deduplicate against existing guidance and name conflicts or narrower scopes.
• Use imperative, testable wording and include non-goals for each candidate.
• Require review approval before any patch handoff or generated-skill update.

Required inputs:

• source corpus: skills, prompts, traces, reviews, failures, or docs
• destination boundary: AGENTS, skill catalog, prompt, docs, memory, or no-write review
• rule granularity and acceptance criteria
• reviewer or approval requirement

Expected outputs:

• rules_distillation_plan/v1
• principle_candidate_set/v1
• duplication_conflict_report/v1
• review_queue/v1
• approved_patch_handoff/v1 when approved
• not-evidence boundary

Artifact expectations:

• principle_candidate_set/v1 with source references, repeated pattern, candidate wording, scope, non-goals, and risk
• duplication_conflict_report/v1 with already-covered rules, conflicts, and stale guidance
• review_queue/v1 separating proposed, approved, rejected, deferred, and needs-evidence candidates

Safety rules:

• Do not silently mutate skills, prompts, AGENTS.md, docs, memory, or catalog data from a distillation result.
• Do not promote one-off preferences, weak anecdotes, or stale traces into global rules.
• Keep observed sources, inferred principles, candidate wording, review state, and implementation patches separate.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Record actual tool results, or not_observed / not_available, in the record; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Reply in the user's own words and the host's own voice: OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a stop condition or a decision the user owns ends the turn, offer the next action as a question rather than declaring what will not be done. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.