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Omh Rules Distill

CommunityPopular
rlaope
omh-rules-distill

[omh] Turn repeated lessons into written rules: extract repeated principles from skills, prompts, traces, reviews, and failures into reviewed rule candidates without auto-mutating guidance. Use when the user says: rules-distill, rules distill, distill rules, rule distillation, principle distill, skill principles, extract agent rules, turn traces into rules.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-rules-distill
Stars
3.6K
Forks
269
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Rules Distill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-rules-distill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-rules-distill .claude/skills/omh-rules-distill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Rules Distill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Omh Rules Distill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Omh Rules Distill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.
  • The user wants durable factual project memory; use omh-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 omh-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.

When to Use

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.
  • On Hermes, read a curator run report (logs/curator/<run>/REPORT.md and run.json under the active profile's Hermes home) as a source like a review or a trace: it records which skills the curator consolidated, archived, or created in that run, and one report is one observation, never repeated evidence by itself.

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.
  • On Hermes, when the destination is the user's own Hermes skills, an approved candidate lands through skill_manage: report it landed only from a result that succeeded without staged: true, with the matching skill audit ledger entry as its record while the ledger is on, and report a staged write as waiting in /skills pending for the person's own /skills approve <id> under their skills.write_approval setting. OMH-managed skills are generated from OMH's catalog and are never the destination. An OMH-managed skill is one OMH installed, under a label OMH's catalog renders such as omh-plan or ulw-work; a companion skill the person or an agent created is theirs, whatever its name.

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: the host's persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; 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 real unresolved user decision or a useful briefing discussion remains, offer one specific, content-linked question. Completed work needs no question; omit unrelated non-action footers and generic offers to continue. 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.

Frequently asked questions

What does the Omh Rules Distill AI skill do?

[omh] Turn repeated lessons into written rules: extract repeated principles from skills, prompts, traces, reviews, and failures into reviewed rule candidates without auto-mutating guidance. Use when the user says: rules-distill, rules distill, distill rules, rule distillation, principle distill, skill principles, extract agent rules, turn traces into rules.

Why use Omh Rules Distill on TypingMind?

Because you install it once and use it with any model. Omh Rules Distill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Omh Rules Distill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-rules-distill. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Omh Rules Distill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Omh Rules Distill?

As many as you like. As long as a model supports skills, you can use Omh Rules Distill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Omh Rules Distill AI skill free?

Yes. It is published on GitHub by rlaope under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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