Skill

ai-critic

Fetches AI-generated code review suggestions from pair-review, triages validity, applies fixes to actionable ones, and reports summary. Use to address AI feedback after pair-review analysis.

code-quality

npx claudepluginhub in-the-loop-labs/pair-review --plugin pair-review

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Parent forks

Invocation

How this skill is triggered — by the user, by Claude, or both

Slash command

/pair-review:ai-critic

User invocable

Model invocable

Inline context

Default effort

Context Preview

The summary Claude sees in its skill listing — used to decide when to auto-load this skill

Fetch AI-generated suggestions from pair-review and make code changes to address the valid ones.

SKILL.md

48 lines · ~518 tokens

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Addresses code review feedback by validating issues, fixing valid ones, and batch-committing changes. Handles local agent feedback or GitHub PR threads via /fix-code-review-feedback or auto-invocation.

iterative-engineering

Stats

LanguageJavaScript

Parent stars34

Parent forks9

MaintenanceExcellent

Last CommitApr 5, 2026

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AI Critic

Fetch AI-generated suggestions from pair-review and make code changes to address the valid ones.

Determine review context

Determine whether this is a local review or a PR review:

If the user explicitly says "local", use local mode.

Otherwise, determine the GitHub owner, repo, and PR number for the current branch. If a PR exists, use PR mode with repo and prNumber params.

If no PR exists, use local mode with path (absolute cwd) and headSha (git rev-parse HEAD) params.

Fetch AI suggestions

Call mcp__pair-review__get_ai_suggestions with the review context params. This returns suggestions from the latest analysis run by default.

If the user wants suggestions from a specific analysis run, call mcp__pair-review__get_ai_analysis_runs first to list available runs, then pass the appropriate runId to get_ai_suggestions.

Only active and adopted suggestions are included (dismissed ones are excluded).

If no suggestions are returned, tell the user there are no AI suggestions to address.

Triage and address suggestions

AI suggestions are not human-curated — apply judgment. For each suggestion:

Read the file at the referenced path and lines.

Evaluate the suggestion: is it a real issue, a false positive, or a stylistic preference?

If the suggestion is valid and actionable, make the code change.

If the suggestion is a false positive or not worth addressing, skip it and note why.

Use the ai_confidence field as a signal but not a hard threshold — low-confidence suggestions can still be valid.

Report

After processing all suggestions, provide a summary:

How many suggestions were reviewed

Which ones were addressed (and what changed)

Which ones were skipped (and why)

AI Critic

Fetch AI-generated suggestions from pair-review and make code changes to address the valid ones.

Determine review context

Determine whether this is a local review or a PR review:

If the user explicitly says "local", use local mode.
Otherwise, determine the GitHub owner, repo, and PR number for the current branch. If a PR exists, use PR mode with repo and prNumber params.
If no PR exists, use local mode with path (absolute cwd) and headSha (git rev-parse HEAD) params.

Fetch AI suggestions

Call mcp__pair-review__get_ai_suggestions with the review context params. This returns suggestions from the latest analysis run by default.

If the user wants suggestions from a specific analysis run, call mcp__pair-review__get_ai_analysis_runs first to list available runs, then pass the appropriate runId to get_ai_suggestions.

Only active and adopted suggestions are included (dismissed ones are excluded).

If no suggestions are returned, tell the user there are no AI suggestions to address.

Triage and address suggestions

AI suggestions are not human-curated — apply judgment. For each suggestion:

Read the file at the referenced path and lines.
Evaluate the suggestion: is it a real issue, a false positive, or a stylistic preference?
If the suggestion is valid and actionable, make the code change.
If the suggestion is a false positive or not worth addressing, skip it and note why.

Use the ai_confidence field as a signal but not a hard threshold — low-confidence suggestions can still be valid.

Report

After processing all suggestions, provide a summary:

How many suggestions were reviewed
Which ones were addressed (and what changed)
Which ones were skipped (and why)

ai-critic

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Invocation

Context Preview

SKILL.md

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ai-critic

Popularity

Invocation

Context Preview

SKILL.md

AI Critic

Determine review context

Fetch AI suggestions

Triage and address suggestions

Report

Similar Skills

Help us improve

AI Critic

Determine review context

Fetch AI suggestions

Triage and address suggestions

Report