# In The Loop Labs Pair Review AI Critic > AI Critic - Skill: `tomevault-io/in-the-loop-labs-pair-review-ai-critic` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/in-the-loop-labs-pair-review-ai-critic` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/in-the-loop-labs-pair-review-ai-critic/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/in-the-loop-labs-pair-review-ai-critic --- # 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: 1. If the user explicitly says "local", use local mode. 2. 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. 3. 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: 1. Read the file at the referenced path and lines. 2. Evaluate the suggestion: is it a real issue, a false positive, or a stylistic preference? 3. If the suggestion is valid and actionable, make the code change. 4. 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) --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/in-the-loop-labs) — claim your Tome and manage your conversions.