Rde Eval

OrganizationPopular

millionco

rde-eval

Run a targeted local React Doctor Evals loop against an uncommitted rule change. Use after focused rule tests pass, while inspecting real open-source hits, or when rule-validate needs local false-positive evidence before pull request parity.

Overview

Publisher	millionco
Repository	`react-doctor`
Skill name	`rde-eval`
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 Rde Eval 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/rde-eval
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/rde-eval .claude/skills/rde-eval

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

Use it in TypingMind

Enable Rde Eval 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 Rde Eval 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 Rde Eval 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.

Run a local rule evaluation

Use React Doctor Evals (RDE) for bounded local iteration. Use run-parity only after the change has a pushed pull request.

Prepare both checkouts

sh
export REACT_DOCTOR_CHECKOUT=/absolute/path/to/react-doctor
export RDE_CHECKOUT=/absolute/path/to/react-doctor-evals

git -C "$RDE_CHECKOUT" pull --ff-only
ni -C "$RDE_CHECKOUT"
nr -C "$RDE_CHECKOUT" build
nr -C "$REACT_DOCTOR_CHECKOUT" build

The path: spec reads uncommitted React Doctor changes. Run RDE commands from the eval checkout.

Run a bounded sample

sh
cd "$RDE_CHECKOUT"
node dist/cli.js run "path:$REACT_DOCTOR_CHECKOUT" --runner local --take 100
node dist/cli.js digest "path:$REACT_DOCTOR_CHECKOUT" --rule <rule-id>
node dist/cli.js digest "path:$REACT_DOCTOR_CHECKOUT" --json --rule <rule-id> > <artifact-directory>/hits.json

Increase --take only after tests and the first sample pass.

Inspect target-rule hits

For each hit, or a representative sample when counts are high:

Open the pinned repository at the reported location.
Compare the code with the rule contract.
Classify the hit as true positive, false positive, or unsupported.
Add a rule regression test for each false positive.
Add confirmed false positives to the fuzz regression corpus.
Rebuild and rerun the same sample.

Record repository count separately from project-root count. Do not treat error records as clean scans.

Report results

Report checkout revisions, target rule, repositories, project roots, diagnostics, inspected hits, fixed false positives, and the artifact path. State any setup error or skipped repository.

After local validation, return to rule-validate. That skill decides whether to invoke pull request parity.

Frequently asked questions

What does the Rde Eval AI skill do?

Why use Rde Eval on TypingMind?

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

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

Which AI models can use Rde Eval?

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 Rde Eval?

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

Is the Rde Eval 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

Run a local rule evaluation

Prepare both checkouts

Run a bounded sample

Inspect target-rule hits

Report results

Frequently asked questions

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