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Refit

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Yeachan-Heo
refit

Cross-session environment retrospective driven by OMC's own instrumentation — trace timelines, friction reports, logs, and plan notepads. Every finding lands on the surface that owns the fix (deterministic check, steering volume, tooling, or information access); nothing is written without user approval.

Overview

PublisherYeachan-Heo
Repositoryoh-my-claudecode
Skill namerefit
Stars
39.8K
Forks
3.6K
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 Yeachan-Heo on GitHub. Read the source before you install it.

Installation

Install the Refit 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/Yeachan-Heo/oh-my-claudecode/tree/main/skills/refit
  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/Yeachan-Heo/oh-my-claudecode.git /tmp/oh-my-claudecode
mkdir -p .claude/skills
cp -r /tmp/oh-my-claudecode/skills/refit .claude/skills/refit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Refit 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 Refit 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 Refit 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.

Refit

Refit surveys the environment the agent worked in — not the code it produced (review and verify own that) and not one launch run's lessons (the sediment pass owns that). Every kind of work leaves friction behind: a ralph loop that kept tripping on the same check, an autopilot run that stalled waiting for a signal, a debugging session where the decisive output was never captured anywhere. OMC instruments all of it. Refit reads the instruments and converts recorded friction into environment changes, so the next run starts in a better yard.

Evidence first

The survey starts from OMC's instruments, not from a checklist. Read before asking the user anything:

  • trace_timeline / trace_summary — where turns stalled, repeated, or fanned out wastefully
  • omc session friction report — friction the session recorded about itself
  • .omc/logs/ and session state — errors, retries, swallowed failures
  • plan notepads .omc/notepads/*/issues.md and problems.md — problems that accumulated across a plan
  • the repo's own check surface (package.json scripts, CI workflows) — read before proposing any new check, because a check that exists but sits unwired or silently broken is the finding, not a reinvention

Findings emerge from what the data shows. An empty survey is a valid result and is stated plainly; an invented finding is the same violation as skipping the survey. A repo with no wired automated checks is itself a finding, not a neutral default.

Four fix-owner surfaces

Every finding names the surface that owns its fix. A finding that fits no surface is declined, explicitly and with the reason.

  1. Deterministic check — the failure was mechanical: a fixed syntactic pattern, a banned API, an import shape, a file-location rule. The fix is a lint rule, hook, or CI job — whichever the repo's existing guardrails make cheapest. Default to building the check over writing the rule.
  2. Steering surface — the failure was a judgement call no guardrail substitutes for: cross-file consistency, matches-surrounding-style. The fix lands in the matching docs/standards/ volume, or in CLAUDE.md/AGENTS.md within the thin-entry budget the launch sediment pass defines. Navigation pointers over prose.
  3. Tool surface — the drag was tooling itself: expensive or token-inefficient MCP calls, missing automation, checks too slow to be run when they matter. The fix lands in .mcp.json, scripts/, hooks, or OMC config.
  4. Information surface — the agent needed a signal it could not reach: dev-server logs, service state, third-party readonly access. The fix tees the log, exposes the state, or documents the access path.

The split rests on where enforcement pressure lives: implementation contexts carry the most of it (exploration, writing, and debugging all at once); review contexts receive a diff with none of it. Standards therefore belong to reviewers and checks — never to more instructions loaded onto the implementer.

Proposal and landing

  1. Present findings ranked by severity, each as finding → surface → intended change, with one line of instrument evidence (which instrument, what it showed).
  2. Stop for user approval. Each line is individually approvable or vetoable.
  3. Write approved findings to their surfaces. Before writing any steering prose, call the Skill tool with agent-doc-discipline and pass its verification checklist. A newly installed deterministic check must be proven to bite before it counts as landed: run it clean once, then demonstrate it failing on a deliberately introduced violation, then revert the violation. A check that cannot be shown failing goes back to the proposal — a guardrail nobody has seen fire is decoration, not protection.
  4. Record where each finding landed — one file location per line — so the next refit starts from the record, not from memory. A refit on a launch-run session starts from that run's sediment list; a launch run may defer a lesson to a later refit by naming it.

Headless refit (unattended invocation)

Refit is user-invoked, but its survey does not need the user at the keyboard to run — only to dispose. A host scheduler (cron, CI timer, an automation tool) may start a headless agent session that invokes this skill; the survey runs to its natural boundary under the same contract:

  • The survey reads the same instruments and lands nothing anywhere: headless refit produces the proposal list only — finding → surface → intended change, each with one line of instrument evidence — written to .omc/refit/pending-proposals.md and, when a notification channel is configured (configure-notifications), summarized there so the user learns a survey is waiting.
  • Nothing reaches a surface without the user's approval, exactly as in an interactive refit: proposals wait ranked, each independently vetoable, and the next interactive refit starts from the pending list instead of re-running the survey.
  • An empty survey is stated plainly ("no findings"), never padded — inventing findings is the same violation as skipping the survey.

Output

  • Findings ranked by severity, each with evidence, surface, and intended change — every line independently decidable
  • After approval: what landed and where
  • Declined findings, with reasons

Frequently asked questions

What does the Refit AI skill do?

Cross-session environment retrospective driven by OMC's own instrumentation — trace timelines, friction reports, logs, and plan notepads. Every finding lands on the surface that owns the fix (deterministic check, steering volume, tooling, or information access); nothing is written without user approval.

Why use Refit on TypingMind?

Because you install it once and use it with any model. Refit 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 Refit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Yeachan-Heo/oh-my-claudecode/tree/main/skills/refit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Refit?

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 Refit?

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

Is the Refit AI skill free?

Yes. It is published on GitHub by Yeachan-Heo 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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