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Define a precise React Doctor rule contract before implementation. Use when validating a rule idea, collecting official or open-source evidence, identifying false-positive traps, choosing detector precision, or setting first-version boundaries.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/millionco/react-doctor --skill rule-research명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Compare React Doctor diagnostics for a GitHub pull request (PR) with Daytona. Use when asked to run parity, check a PR for diagnostic regressions, compare a PR with its base, or report added and removed diagnostics.
Audit React Doctor against ReactBench or similar diagnostic benchmark corpora for confirmed false positives, false negatives, taxonomy gaps, and verifier artifacts. Use when analyzing rd.log, rd-before.json, rd-after.json, model.patch, result.json, reward/test logs, rule distributions, or when asked to perform a second adversarial pass over React Doctor benchmark findings.
Survey a whole React codebase as a senior React engineer, using React Doctor's scan as evidence, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the React code", "audit this codebase", "make this app faster / more robust", or wants a roadmap of fixes rather than a review of a single diff. For a regression check or a fix-it-now pass, use the `react-doctor` skill instead.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | rule-research |
| description | Define a precise React Doctor rule contract before implementation. Use when validating a rule idea, collecting official or open-source evidence, identifying false-positive traps, choosing detector precision, or setting first-version boundaries. |
Produce a rule contract that rule-writing can implement without guessing.
Resolve only questions that affect correctness:
If the user requested implementation, make the contract concise and continue.
Define the rule in one sentence: This rule catches <pattern> that causes <problem>.
Explain the runtime reason.
Inspect nearby rules, tests, utilities, and the generated registry.
Use truffler before proposing a new detector or helper:
bunx @rayhanadev/truffler "<symbol-or-behavior>" \
packages/oxlint-plugin-react-doctor/src/plugin \
--kind function,interface,type,constant --limit 20
Gather official documentation, implementation notes, related linter behavior, and open-source examples.
Separate strong positives, adjacent patterns, valid traps, and unsupported cases.
Choose syntax-only, scope-aware, or path-aware detection.
Use rde-eval when a bounded open-source sample could change the contract. Leave final pull request parity to rule-validate.
Return:
Rule definition:
<pattern and specific problem>
Runtime reason:
<short explanation>
Detector precision:
<syntax-only, scope-aware, or path-aware>
Evidence:
- <source and implication>
Strong positives:
- <reportable examples>
False-positive traps:
- <valid examples>
In scope:
- <supported cases>
Out of scope:
- <explicit boundaries>
Test seeds:
- <invalid and valid fixtures>
Open questions:
- <correctness blockers only>
Treat false positives as correctness bugs. Keep the diagnostic narrower than or equal to the proven behavior. Split adjacent ideas into separate rules.