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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

Low-risk findings worth noting

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, highly actionable instruction skill: executable gh commands, quantified thresholds, and a well-gated three-step workflow. The only gap is the absence of an explicit post-save validation step before finishing.

Suggestions

Add a brief verify-before-finish step (e.g., confirm the saved prompt is within the 400–1200 word range and reflects patterns actually observed in the sampled PRs) to complete the feedback loop.

State what to do if gh API calls fail or rate-limit mid-research, mirroring the existing sparse-batch escalation guidance.

DimensionReasoningScore

Conciseness

The ~58-line body is lean and assumes Claude's competence: no generic concept explanations, no padding; every line carries instruction ('skip [bot] accounts', 'never run gh auth login', 'Do not call read_finding_outcomes in this mode — it will be empty').

5 / 5

Actionability

Provides copy-paste-ready gh commands (pr list with exact flags, three gh api endpoints) plus concrete thresholds: at least 8 substantive human comments, top ~5 reviewers, 400–1200 words, 2–4 sentences, ~25+ merged PRs fallback rule.

5 / 5

Workflow Clarity

Clear Research → Extract → Save sequence with explicit gates ('Only after real research', sparse-batch escalation, verify preloaded samples with gh), but there is no post-save verification loop, so it fits 'clear sequence with most checkpoints; minor validation gaps' rather than the full feedback-loop anchor.

4 / 5

Progressive Disclosure

A single-file skill with no bundle files (references/, scripts/, assets/ absent) and no nested references; well-organized numbered sections with no content that belongs in a separate file — meets the simple-skill top anchor.

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 description: third-person, concrete, and complete with an explicit 'Use when' clause and clear disambiguation against the related continual-learning skill. Trigger-term coverage is good but could add a couple more natural synonyms.

Suggestions

Add one or two common trigger synonyms (e.g., 'bootstrap' or 'new repo') to broaden natural-term coverage.

Slightly enumerate the concrete sub-actions (e.g., mention what the synthesized prompt captures: bug taxonomy, severity calibration, do-not-flag list) to reach comprehensive specificity.

DimensionReasoningScore

Specificity

Lists three concrete, tool-anchored actions (crawl merged-PR review feedback with the gh CLI, extract the team's review norms, synthesize the review-style prompt), but they are described at moderate granularity rather than fully enumerated, fitting 'several specific actions; minor gaps in coverage' rather than the comprehensive anchor.

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 phrases — matches the top anchor.

5 / 5

Trigger Term Quality

Includes natural phrases a user or system would say — 'First-time analysis', 'repository', 'review feedback', 'review norms', 'cold-start repo' — but misses a few common variations such as 'bootstrap', 'new repo', or 'baseline'.

4 / 5

Distinctiveness Conflict Risk

'no prior reviewer outcomes' plus explicit routing to the sibling continual-learning skill gives a clear niche with distinct cold-start triggers and minimal conflict risk.

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
langchain-ai/open-swe
Reviewed

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