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rule-research

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.

70

Quality

85%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A tight, well-structured research workflow with concrete tooling and a clear output contract. It is lean and actionable, with only minor gaps in feedback-loop explicitness and file-based content split.

Suggestions

Add an explicit validation/feedback checkpoint in the Collect evidence or Write contract phase (e.g., re-run truffler or rde-eval if the contract changes) to strengthen workflow_clarity.

Consider extracting the fill-in contract template into a references/ file and linking to it, improving progressive_disclosure now that the template is substantial.

Include one concrete example of a completed contract (pattern + false-positive trap) so the output shape is unambiguous.

DimensionReasoningScore

Conciseness

Lean and efficient throughout: terse question lists, one concrete command, and a fill-in template with no padding or explanation of concepts Claude already knows; every line earns its place.

5 / 5

Actionability

Provides a copy-paste-ready truffler command and a concrete fill-in contract template, but most guidance is process direction rather than executable code, leaving minor gaps for a research skill.

4 / 5

Workflow Clarity

Clear three-phase sequence (Define behavior -> Collect evidence -> Write contract) with conditional checkpoints ('Use truffler before proposing...', 'Use rde-eval when...'), but no explicit validate-fix-retry feedback loop.

4 / 5

Progressive Disclosure

Well-organized into three clear sections with the contract template inline; no bundle files exist so structure is self-contained, though the sizable inline template could justify a separate reference file.

4 / 5

Total

17

/

20

Passed

Description

87%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, specific description that clearly states both the skill's purpose and its trigger conditions in third person. Minor room for more natural synonyms, but it is comprehensive and distinct.

DimensionReasoningScore

Specificity

Lists several concrete actions ('validating a rule idea', 'collecting official or open-source evidence', 'identifying false-positive traps', 'choosing detector precision', 'setting first-version boundaries'), though the primary verb 'Define a precise React Doctor rule contract' is somewhat abstract as the headline action.

4 / 5

Completeness

Explicitly answers both 'what' ('Define a precise React Doctor rule contract before implementation') and 'when' with a concrete 'Use when...' clause listing multiple trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

Good keyword coverage with natural domain phrases a rule author would say ('rule idea', 'false-positive traps', 'detector precision', 'first-version boundaries'), missing a few common synonyms like 'lint rule' or 'research a rule'.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (React Doctor rule contract phase) with distinct triggers referencing sibling skills, minimal overlap risk with unrelated skills.

5 / 5

Total

18

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
millionco/react-doctor
Reviewed

Table of Contents

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