Worker that runs parallel external agent reviews (Codex + Gemini) on code changes. Reference-based prompts. Returns filtered suggestions with confidence scoring.
Scanned 9/4/2026
Install to Claude Code
npx -y skills add boisenoise/skills-collections --skill levn-ln-502-agent-reviewer --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: ln-502-agent-reviewer
description: "Worker that runs parallel external agent reviews (Codex + Gemini) on code changes. Reference-based prompts. Returns filtered suggestions with confidence scoring."
---
# Agent Reviewer (Code)
Runs parallel external agent reviews on code implementation, returns filtered suggestions.
## Purpose & Scope
- Worker in ln-500 quality gate pipeline (invoked by ln-501 Step 7)
- Run codex-review + gemini-review in parallel on code changes
- Return filtered, deduplicated suggestions with confidence scoring
- Health check + prompt execution in single invocation (minimal timing gap between availability check and actual API call)
## When to Use
- **Invoked by ln-501-code-quality-checker** Step 7 (Agent Review)
- All implementation tasks in Story status = Done
- Code quality analysis (Steps 1-6) already completed by ln-501
## Inputs (from parent skill)
- `storyId`: Linear Story identifier (e.g., "PROJ-123")
## Workflow
1) **Health check:** `python shared/agents/agent_runner.py --health-check`
- Filter output by `skill_groups` containing "502"
- If 0 agents available -> return `{verdict: "SKIPPED", reason: "no agents available"}`
- Display: `"Agent Health: codex-review OK, gemini-review UNAVAILABLE"` (or similar)
2) **Load Story:** `get_issue(storyId)` via MCP Linear -> get description, title, identifier, URL
3) **Load Tasks:** `list_issues(filter: {parent: {id: storyId}, status: "Done"})` via MCP Linear -> Done implementation tasks (exclude label "tests")
4) **Materialize content:** Create `.agent-review/` directory in project CWD
- Save Story markdown to `.agent-review/story-{identifier}.md`
- Save Tasks markdown (concatenated with headers) to `.agent-review/tasks-{identifier}.md`
- Ensure `.agent-review/` is in project `.gitignore` (add if missing)
5) **Build prompt:** Read template `shared/agents/prompt_templates/code_review.md`
- Replace `{story_url}` with Linear URL (e.g., `https://linear.app/team/PROJ-123`)
- Replace `{story_file}` with `story-{identifier}.md`
- Replace `{tasks_file}` with `tasks-{identifier}.md`
- Save expanded prompt to temp file (use `%TEMP%` on Windows, `/tmp` on Unix)
6) **Run agents in parallel** (two Bash calls simultaneously):
- `python shared/agents/agent_runner.py --agent codex-review --prompt-file {temp} --cwd {cwd}`
- `python shared/agents/agent_runner.py --agent gemini-review --prompt-file {temp} --cwd {cwd}`
7) **Aggregate:** Collect suggestions from all successful responses. Deduplicate by `(area, issue)` — keep higher confidence.
**Filter:** `confidence >= 90` AND `impact_percent > 2`
8) **Cleanup:** Delete `.agent-review/` directory. **Return** JSON with suggestions + agent stats to parent skill.
## Output Format
```yaml
verdict: CODE_ACCEPTABLE | SUGGESTIONS | SKIPPED
suggestions:
- area: "security | performance | architecture | correctness | best_practices"
issue: "What is wrong"
suggestion: "Specific fix"
confidence: 95
impact_percent: 15
agent_stats:
- name: "codex-review"
duration_s: 12.4
suggestion_count: 3
status: "success | failed | timeout"
```
## Fallback Rules
| Condition | Action |
|-----------|--------|
| Both agents succeed | Aggregate suggestions from both |
| One agent fails | Use successful agent's suggestions, log failure |
| Both agents fail | Return `{verdict: "SKIPPED", reason: "agents failed"}` |
| Parent skill (ln-501) | Falls back to Self-Review (native Claude) |
## Verdict Escalation
- Findings with `area=security` or `area=correctness` -> parent skill can escalate PASS -> CONCERNS
- This skill returns raw suggestions; escalation decision is made by ln-501
## Critical Rules
- Read-only review — agents must NOT modify files
- Same prompt to all agents (identical input for fair comparison)
- JSON output schema required from agents (via `--json` / `--output-format json`)
- Log all attempts for user visibility (agent name, duration, suggestion count)
- Always cleanup `.agent-review/` directory after agents complete (even on failure)
- Ensure `.agent-review/` is in project `.gitignore` before creating files
## Reference Files
- **Agent delegation pattern:** `shared/references/agent_delegation_pattern.md`
- **Prompt template:** `shared/agents/prompt_templates/code_review.md`
- **Agent registry:** `shared/agents/agent_registry.json`
- **Agent runner:** `shared/agents/agent_runner.py`
---
**Version:** 1.0.0
**Last Updated:** 2026-02-08
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