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ln-513-agent-reviewer

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npx machina-cli add skill levnikolaevich/claude-code-skills/ln-513-agent-reviewer --openclaw
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SKILL.md
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Paths: File paths (shared/, references/, ../ln-*) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root.

Agent Reviewer (Code)

Runs parallel external agent reviews on code implementation, critically verifies suggestions, returns filtered improvements.

Purpose & Scope

  • Worker in ln-510 quality coordinator pipeline (invoked by ln-510 Phase 4)
  • Run codex-review + gemini-review as background tasks in parallel
  • Process results as they arrive (first-finished agent processed immediately)
  • Critically verify each suggestion; debate with agent if Claude disagrees
  • Return filtered, deduplicated, verified suggestions with confidence scoring
  • Health check + prompt execution in single invocation

When to Use

  • Invoked by ln-510-quality-coordinator Phase 4 (Agent Review)
  • All implementation tasks in Story status = Done
  • Code quality (ln-511) and tech debt cleanup (ln-512) already completed

Parameters

ParameterValue
review_typecodereview
skill_group513
prompt_templateshared/agents/prompt_templates/code_review.md
verdict_acceptableCODE_ACCEPTABLE

Inputs (from parent skill)

  • storyId: Linear Story identifier (e.g., "PROJ-123")

Workflow

MANDATORY READ: Load shared/references/agent_review_workflow.md for Health Check, Ensure .agent-review/, Load Review Memory, Run Agents, Critical Verification + Debate, Aggregate + Return, Save Review Summary, Fallback Rules, Critical Rules, and Definition of Done. Load shared/references/agent_delegation_pattern.md for Reference Passing Pattern, Review Persistence Pattern, Agent Timeout Policy, and Debate Protocol.

Unique Steps (before shared workflow)

  1. Health check: per shared workflow, filter by skill_group = 513.

  2. Get references: Call Linear MCP get_issue(storyId) -> extract URL + identifier. Call list_issues(filter: {parent: {id: storyId}, status: "Done"}) -> extract Done implementation Task URLs/identifiers (exclude label "tests").

    • If project stores tasks locally (e.g., docs/tasks/) -> use local file paths instead of Linear URLs.
  3. Ensure .agent-review/: per shared workflow.

  4. Build prompt: Read template shared/agents/prompt_templates/code_review.md.

    • Replace {story_ref} with - Linear: {url} or - File: {path}
    • Replace {task_refs} with bullet list: - {identifier}: {url_or_path} per task
    • Save to .agent-review/{identifier}_codereview_prompt.md (single shared file -- both agents read the same prompt)

Shared Workflow Steps

5-8) Load Review Memory, Run agents, Critical Verification + Debate, Aggregate + Return: per shared workflow.

  • {review_type} = "Code Implementation" (for challenge template)
  1. Save Review Summary: per shared workflow "Step: Save Review Summary".

Output Format

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
    source: "codex-review"
    resolution: "accepted | accepted_after_debate | accepted_after_followup | rejected"

Agent stats and debate log per shared workflow output schema.

Fallback Rules

Per shared workflow, plus:

ConditionAction
Parent skill (ln-510)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 verified suggestions; escalation decision is made by ln-510

Critical Rules (additional)

  • MANDATORY INVOCATION: Parent skills MUST invoke this skill. Returns SKIPPED gracefully if agents unavailable. Parent must NOT pre-check and skip.

Reference Files

  • Shared workflow: shared/references/agent_review_workflow.md
  • Agent delegation pattern: shared/references/agent_delegation_pattern.md
  • Prompt template (review): shared/agents/prompt_templates/code_review.md
  • Challenge schema: shared/agents/schemas/challenge_review_schema.json

Version: 2.0.0 Last Updated: 2026-02-11

Source

git clone https://github.com/levnikolaevich/claude-code-skills/blob/master/ln-513-agent-reviewer/SKILL.mdView on GitHub

Overview

Agent Reviewer runs Codex and Gemini reviews on code changes in parallel, then critically verifies and debates suggestions. It returns filtered, deduplicated improvements with confidence scoring to drive downstream decisions in the quality pipeline.

How This Skill Works

The skill loads a health check and reference workflow, builds a shared prompt from the code_review template, and executes Codex and Gemini as background tasks in parallel. Results are processed as they arrive, then subjected to critical verification and debate; validated suggestions are deduplicated and returned with confidence scores and a summary report.

When to Use It

  • Invoked by ln-510-quality-coordinator Phase 4 (Agent Review)
  • All implementation tasks in Story status = Done
  • Code quality (ln-511) and tech debt cleanup (ln-512) already completed
  • Need parallel reviews with first-result processing for speed
  • Require debate-driven verification and confidence-scored suggestions

Quick Start

  1. Step 1: Build and save the codereview prompt from shared/agents/prompt_templates/code_review.md to .agent-review/{identifier}_codereview_prompt.md with the proper {story_ref} and {task_refs}.
  2. Step 2: Run Codex and Gemini in parallel as background tasks and collect results as they finish.
  3. Step 3: Perform critical verification with debate, filter/deduplicate suggestions, assign confidence scores, and save a Review Summary.

Best Practices

  • Ensure skill_group is set to 513 for Health Check filtering
  • Use the code_review.md template at shared/agents/prompt_templates/code_review.md
  • Construct task references from Linear/MCP outputs and exclude tests
  • Run codex-review and gemini-review in parallel and process earliest results
  • Apply critical verification and debate, then deduplicate and assign confidence scores

Example Use Cases

  • Reviewing a new feature PR for a logging enhancement to catch edge cases
  • Validating a bug fix in a critical path with potential side effects
  • Refactoring a module with performance implications and API changes
  • Cleaning up tech debt after a major feature delivery
  • Adding instrumentation with minimal behavioral changes while maintaining correctness

Frequently Asked Questions

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