Refire logo

Refire

Organization
escoffier-labs
refire

Use when anything misbehaves - a failing test, a production bug, a build break, flaky or unexpected behavior - before proposing or attempting any fix. Especially under pressure ("CI is blocking everyone", "just get it green") and after a previous fix didn't hold.

Overview

Publisherescoffier-labs
Repositorybrigade
Skill namerefire
Stars
82
Forks
9
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 escoffier-labs on GitHub. Read the source before you install it.

Installation

Install the Refire 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/escoffier-labs/brigade/tree/main/registry/skills/refire
  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/escoffier-labs/brigade.git /tmp/brigade
mkdir -p .claude/skills
cp -r /tmp/brigade/registry/skills/refire .claude/skills/refire
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Refire 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 Refire 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 Refire 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.

refire

A plate comes back to the kitchen. The line's instinct is to fire the same dish again, faster; the chef's job is to find out why it came back first, because a refire of the same mistake comes back twice and now the diner is angrier. This skill is the chef's version of debugging: no fix until the cause of the sendback is known.

Core principle: no fixes without root cause first. A fix that makes the symptom disappear without explaining it is not a fix, it is a scheduled second incident. Violating the letter of this process is violating its spirit.

Find out why it came back

Before any fix, in order:

  1. Read the error, all of it. The full traceback, the line numbers, the actual message. It frequently names the cause outright.
  2. Reproduce it. Trigger it reliably with the smallest input you can. Cannot reproduce? Gather more evidence; do not guess.
  3. Check what changed. git log and git diff around when it broke: recent commits, new dependencies, config or environment drift. Most bugs are young.
  4. Check the documented contract. Schema docs, interface comments, the spec. The difference between the documented behavior and the actual behavior usually points straight at the cause, and the fix must satisfy the contract, not just the failing test.
  5. Trace to the source. Where does the bad value originate? Who called this with it? Walk up the stack until the origin; the fix lands there, not at the crash site. In a multi-component system, instrument each boundary (what enters, what exits) and run once to see which layer breaks, rather than theorizing across all of them.
  6. Compare against what works. Find the nearest working sibling (same pattern, same codebase) and list every difference, including the ones that "can't matter".

Then fix it, once

  1. State the hypothesis. "X is the root cause because Y", written, specific. If you cannot finish that sentence, return to investigation; do not pretend.
  2. Pin it with a failing test first. The test reproduces the bug and asserts the violated contract, not just the absence of the crash. This is taste's loop applied to a bug; a fix without a test that failed is a fix that cannot prove itself and a regression nobody will catch.
  3. One minimal change. Test the hypothesis with the smallest change that fixes the cause. One variable. No bundled cleanups, no "while I'm here".
  4. Verify and read the output. The pinned test passes, the whole suite stays green, the original symptom is actually gone.

If the fix did not work: stop, do not stack a second fix on top. New evidence, new hypothesis, back to investigation. After three failed fixes, stop fixing entirely: three misses on one bug means the architecture or the pattern is wrong, and that conversation happens with the user before attempt four.

Rationalizations, all of them wrong

ExcuseReality
"Quick fix now, investigate later"Later never comes; the mask becomes load-bearing.
"CI is blocking everyone, no time for process"Investigation on a reproduced bug takes minutes. Thrashing takes hours and ships masks.
"Just try X and see"Guess-and-check is how one bug becomes three.
"It's probably X"Probably is not a hypothesis with a because.
"The test passes now, ship it"Green that you cannot explain is the most dangerous color.
"Several candidate fixes at once, to be safe"Now you cannot tell which one worked or what the others broke.
"Too simple to need the process"Simple bugs have causes too, and the process is fast on them.
"One more attempt" (after two failures)Third miss means the question is wrong, not the answer.

Red flags - stop and return to investigation

Proposing fixes before reproducing. A fix at the crash site when the bad value came from upstream. Catching-and-ignoring, widening a timeout, or default=-ing an error away to get green. A fix with no new failing test attached. Explaining the fix without being able to explain the bug. Any sentence starting "I don't fully understand it, but this works".

Common mistakes

  • Silencing the symptom (swallow the exception, loosen the assertion, skip the test) and calling CI green a fix.
  • Fixing the right cause but skipping the pinning test, so the contract that broke still has no guard and the next regression sails through.
  • Never opening the docs or schema, so the "fix" satisfies the failing test while quietly violating the documented contract.
  • Stacking fix on fix without reverting the misses, then being unable to say which change did what.
  • Treating a correct guess as a validated hypothesis. Luck is not process; it just hasn't cost you yet.
  • Skipping the "what changed recently" check and reverse-engineering from first principles what git log would have said in ten seconds.

Frequently asked questions

What does the Refire AI skill do?

Use when anything misbehaves - a failing test, a production bug, a build break, flaky or unexpected behavior - before proposing or attempting any fix. Especially under pressure ("CI is blocking everyone", "just get it green") and after a previous fix didn't hold.

Why use Refire on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/escoffier-labs/brigade/tree/main/registry/skills/refire. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Refire?

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 Refire?

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

Is the Refire AI skill free?

Yes. It is published on GitHub by escoffier-labs 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇