Rule Validate

OrganizationPopular

millionco

rule-validate

Validate an implemented React Doctor rule before merge. Use after focused tests pass to review detector correctness, inspect open-source hits, run pull request parity, add regression coverage, prepare a changeset, write pull request copy, or address review findings.

Overview

Publisher	millionco
Repository	`react-doctor`
Skill name	`rule-validate`
Stars	14.9K
Forks	488
Bundled files	Instructions only
Links	Skill source Repository Homepage

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 Rule Validate AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/millionco/react-doctor/tree/main/.agents/skills/rule-validate
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.

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/rule-validate .claude/skills/rule-validate

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rule Validate 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 Rule Validate 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 Rule Validate 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.

Validate a rule

Verify that the implementation matches its rule-research contract on tests and real code.

Review the implementation

Check for:

false positives and missed claimed behavior
incorrect imports, aliases, or shadowed bindings
impossible control-flow path merges
nested functions treated as immediate execution
dynamic properties treated as static names
missed transparent wrappers
unsupported imported values
messages that overstate detection
missing valid and invalid tests

Use truffler before accepting a new helper:

sh
bunx @rayhanadev/truffler "<helper-name>" packages \
  --kind function,method,interface,type,constant --limit 20

Fix each implementation bug with a focused regression test.

Run validation

Run focused tests, package typecheck, and required lint and format checks. Run broader checks when the change affects shared behavior.

Use rde-eval for bounded local inspection of the target rule. Inspect all hits when counts are low and a representative sample when counts are high. Add every confirmed false positive to rule tests and the fuzz corpus.

Run run-parity for every new rule or detector behavior change after the pull request head is pushed. Skip it for documentation-only or test-only changes. If parity cannot run, report the exact blocker.

Do not claim parity unless both Daytona runs complete. Compare repository and project-root counts separately. Inspect target-rule deltas before classifying them.

Prepare release artifacts

Run nr changeset for user-visible changes to published packages. Use a patch changeset for rules, bug fixes, false-positive fixes, and diagnostic refinements unless release impact requires more. Skip only private, documentation, test, or tooling changes, and state why.

Write pull request copy after validation:

md
## Why

<specific runtime problem>

## What changed

- <detector behavior>
- <valid patterns preserved>
- <tests added>

## Eval results

| Check             | Result                |
| ----------------- | --------------------- |
| Projects compared | `<count>`             |
| Skipped projects  | `<count>`             |
| Added / removed   | `<added> / <removed>` |
| Target rule delta | `<added> / <removed>` |
| False positives   | `<count>`             |
| Artifacts         | `<paths>`             |

## Test plan

- `<command and result>`

Omit the eval table when parity did not run. State the reason instead.

Handle review findings

Fix correctness bugs, duplicated helpers, misleading names, and confusing code. Defer unsupported control flow only when it falls outside the contract. Reject requests that broaden the message or increase false positives.

Resolve review threads after the fix or explanation reaches the pull request.

Report validation

Report commands, review findings, local RDE evidence, parity results or blocker, false positives fixed, regression tests, changeset, pull request notes, and residual non-goals.

Frequently asked questions

What does the Rule Validate AI skill do?

Why use Rule Validate on TypingMind?

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

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

Which AI models can use Rule Validate?

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 Rule Validate?

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

Is the Rule Validate 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Validate a rule

Review the implementation

Run validation

Prepare release artifacts

Handle review findings

Report validation

Frequently asked questions

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