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bootstrap-repo-analysis

First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the reviewer has accumulated finding outcomes.

73

Quality

90%

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SecuritybySnyk

Low

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SKILL.md
Quality
Evals
Security

Quality

Content

93%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, actionable bootstrap workflow with concrete gh commands, explicit gating before the save step, and well-organized sections. The only gap is a missing explicit validation pass on the synthesized prompt before saving.

Suggestions

Add an explicit verification checkpoint before save_review_style_prompt (e.g., 'Confirm the prompt names >=5 concrete repo-specific bug patterns and a calibrated do-not-flag list before saving').

State the expected failure/retry behavior when gh returns rate-limit or empty review arrays, so the sparse-batch guidance is complete.

DimensionReasoningScore

Conciseness

Lean throughout: assumes Claude knows what gh, PRs, and review feedback are, uses no padding, and every instruction earns its place (e.g. 'skip [bot] accounts and obvious automation like codecov / dependabot').

5 / 5

Actionability

Provides copy-paste-ready gh CLI commands, a concrete save_review_style_prompt call with named fields, and specific numeric targets (400-1200 words, 2-4 sentences, ~8 comments, ~25+ PRs) covering the common cases.

5 / 5

Workflow Clarity

A clear 3-step Research/Extract/Save sequence with explicit gating ('Only after real research, call save_review_style_prompt once') and retry guidance for sparse batches, but lacks a hard output-validation checkpoint on the synthesized prompt itself.

4 / 5

Progressive Disclosure

A single well-organized file with clearly numbered sections (## 1, 2, 3) and no nested references; under 50 lines with no need for external bundles, so the simple-skill exception applies.

5 / 5

Total

19

/

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, third-person description that concretely states what it does and when to use it, with an explicit boundary against the continual-learning sibling skill. Trigger phrasing is effective though slightly jargon-leaning.

DimensionReasoningScore

Specificity

Lists several concrete actions ('Crawl historical merged-PR review feedback with the gh CLI', 'extract the team's review norms', 'synthesize the initial per-repo review-style prompt') tied to a specific stack, with only minor coverage gaps.

4 / 5

Completeness

Explicitly answers both what (crawl, extract, synthesize) and when ('Use this for a cold-start repo; use continual-learning instead once the reviewer has accumulated finding outcomes') with concrete trigger phrasing.

5 / 5

Trigger Term Quality

Includes the natural-ish trigger phrase 'Use this for a cold-start repo' plus the contrastive 'continual-learning instead', giving good keyword coverage, though it leans on slightly internal jargon ('reviewer outcomes', 'finding outcomes') over common synonyms.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (cold-start) and explicitly draws the boundary against the continual-learning sibling skill, so it is unlikely to trigger for the wrong skill.

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
langchain-ai/open-swe
Reviewed

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