How do I install the Rde Eval skill?

Run npx skillmds@latest add millionco-react-doctor/rde-eval in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Rde Eval safe to use?

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Which AI agents work with Rde Eval?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Rde Eval free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Rde Eval?

millionco (@millionco-react-doctor) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Rde Eval by millionco-react-doctor — Agent Skill

Name: Rde Eval
Rating: 4.5 (4 reviews)
Author: 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.

millionco Updated Sep 9, 2026

File contents

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

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

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.

---name: rde-evaldescription: 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.--- # 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: 1. Open the pinned repository at the reported location. 2. Compare the code with the rule contract. 3. Classify the hit as true positive, false positive, or unsupported. 4. Add a rule regression test for each false positive. 5. Add confirmed false positives to the `fuzz` regression corpus. 6. 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