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Fuzz

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millionco
fuzz

Fuzz React Doctor rules for crashes, slowness, false positives, and mutation-sensitive diagnostics with @react-doctor/fuzz. Use after rule tests pass, when investigating a fuzz finding, or whenever an eval, review, or user report confirms a new false positive.

Overview

Publishermillionco
Repositoryreact-doctor
Skill namefuzz
Stars
14.9K
Forks
488
Bundled files
Instructions only
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 millionco on GitHub. Read the source before you install it.

Installation

Install the Fuzz 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/millionco/react-doctor/tree/main/.agents/skills/fuzz
  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/millionco/react-doctor.git /tmp/react-doctor
mkdir -p .claude/skills
cp -r /tmp/react-doctor/.agents/skills/fuzz .claude/skills/fuzz
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fuzz 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 Fuzz 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 Fuzz 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.

Fuzz rules

Use packages/fuzz after focused rule tests pass. Read its README.md for harness architecture and corpus setup.

Run the harness

Run the target rule first:

sh
FUZZ_RULE=<rule-id> FUZZ_STRICT=1 FUZZ_ITERATIONS=500 nr fuzz

Useful variants:

sh
nr fuzz
FUZZ_RULE=<rule-id> FUZZ_SEED=42 nr fuzz
FUZZ_INVARIANTS=1 nr fuzz
FUZZ_CORPUS_DIR=<repository-directory> nr fuzz
FUZZ_RULE=<rule-id> FUZZ_PRINT_SILENT=1 nr fuzz

Confirm that the target rule fires. A silent rule only exercises early exits. If it stays silent, add a triggering shape to packages/fuzz/src/snippet-pools.ts and rerun.

Use FUZZ_TAG=<tag> instead of FUZZ_RULE to exercise an entire registry cohort.

Recommended full sweep (e.g. before a rule-batch release), pointing the corpus at real checkouts. Ready-made corpora work on any machine — see the package README "Canonical corpora": bun scripts/sync-fuzz-corpus.ts materializes a pinned 48-repo sample (symlinking the RDE cache when present, cloning otherwise), and bun scripts/build-bench-corpus.ts extracts react-bench's diagnostic-dense RD-health target files:

sh
cd packages/fuzz
bun scripts/sync-fuzz-corpus.ts
FUZZ_INVARIANTS=1 FUZZ_ITERATIONS=100 FUZZ_CORPUS_DIR=tmp/corpus-repos nr fuzz

Run the direct false-positive check when a rule change affects common syntax:

sh
cd packages/fuzz
bun scripts/hunt-false-positives.ts

Triage findings

Reproducers live in packages/fuzz/tmp/fuzz-findings/.

  • Crash: minimize the program, add a no-throw regression test, fix the rule, and replay the seed
  • Slow case: profile the pathological shape, bound the walk, and keep the existing threshold
  • Verdict drop: fix detection that depends on incidental syntax, then add the rule to the robustness gate when appropriate
  • Invariant violation: decide whether the rule should react to the rewrite; fix and test unexpected changes
  • False positive: add the valid program to the rule tests and fuzz regression corpus

Replay one finding:

sh
FUZZ_RULE=<rule-id> FUZZ_SEED=<seed> FUZZ_ITERATIONS=1 nr fuzz

Preserve false positives

For every confirmed false positive:

  1. Add a minimal fixture to packages/fuzz/corpus/regressions/<rule-id>--<slug>.tsx.
  2. Include the rule ID, weakness class, and source in the fixture header.
  3. Add a focused valid case to the rule test suite.
  4. Add a generator snippet when the existing pools cannot produce the weakness.
  5. Run nr -C packages/fuzz test and replay the target rule.

Use a stable weakness name such as library-idiom, control-flow, wrapper-transparency, name-heuristic, alias-guard, cross-file, framework-gating, paren-shape, default-parameter, dynamic-computed, private-member, or copy-tracking.

Project-level dead-code and dependency findings belong in core tests, not the rule fuzzer.

Report results

Record the command, target-rule fire count, findings, replay seeds, corpus fixtures, and generator changes. Pass confirmed implementation findings to rule-validate.

Frequently asked questions

What does the Fuzz AI skill do?

Fuzz React Doctor rules for crashes, slowness, false positives, and mutation-sensitive diagnostics with @react-doctor/fuzz. Use after rule tests pass, when investigating a fuzz finding, or whenever an eval, review, or user report confirms a new false positive.

Why use Fuzz on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/millionco/react-doctor/tree/main/.agents/skills/fuzz. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Fuzz?

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 Fuzz?

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

Is the Fuzz AI skill free?

It is published on GitHub by millionco. Check the repository for licensing terms. 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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