--- name: dyad:multi-pr-review description: Multi-agent code review system that spawns three independent Claude sub-agents to review PR diffs. Each agent receives files in different randomized order to reduce ordering bias. One agent focuses specifically on code health and maintainability. Issues are classified as high/medium/low severity (sloppy code that hurts maintainability is MEDIUM). Results are aggregated using consensus voting - only issues identified by 2+ agents where at least one rated it medium or higher severity are reported. Automatically deduplicates against existing PR comments. Always posts a summary (even if no new issues), with low priority issues mentioned in a collapsible section. --- # Multi-Agent PR Review This skill creates three independent sub-agents to review code changes, then aggregates their findings using consensus voting. ## Overview 1. Fetch PR diff files and existing comments 2. Spawn 3 sub-agents, each receiving files in different randomized order - **Agent 1**: Code Health focus (maintainability, clarity, abstractions) - **Agents 2-3**: Default focus (correctness, bugs, security) 3. Each agent reviews and classifies issues (high/medium/low criticality) 4. Aggregate results: report issues where 2+ agents agree 5. Filter out issues already commented on (deduplication) 6. Post findings: summary table + inline comments for HIGH/MEDIUM issues ## Workflow ### Step 1: Fetch PR Diff ```bash # Get changed files from PR gh pr diff --repo > pr_diff.patch # Or get list of changed files gh pr view --repo --json files -q '.files[].path' ``` ### Step 2: Run Multi-Agent Review Execute the orchestrator script: ```bash python3 scripts/orchestrate_review.py \ --pr-number \ --repo \ --diff-file pr_diff.patch ``` The orchestrator: 1. Parses the diff into individual file changes 2. Creates 3 shuffled orderings of the files 3. Spawns 3 parallel sub-agent API calls 4. Collects and aggregates results ### Step 3: Review Prompt Templates Sub-agents receive role-specific prompts (see `references/review_prompt.md`): **Agent 1 (Code Health):** - Focuses on maintainability, code clarity, abstractions, debugging ease - Rates sloppy code that hurts maintainability as MEDIUM severity **Agents 2-3 (Default):** - Focus on correctness, bugs, security, edge cases - Also flag significant maintainability issues as MEDIUM ``` Severity levels: HIGH: Security vulnerabilities, data loss risks, crashes, broken functionality MEDIUM: Logic errors, edge cases, performance issues, AND sloppy code that significantly hurts maintainability (confusing logic, poor abstractions) LOW: Minor style issues, nitpicks, minor improvements Output JSON array of issues. ``` ### Step 4: Consensus Aggregation & Deduplication Issues are matched across agents by file + approximate line range + issue type. An issue is reported only if: - 2+ agents identified it AND - At least one agent rated it MEDIUM or higher **Deduplication:** Before posting, the script fetches existing PR comments and filters out issues that have already been commented on (matching by file, line, and issue keywords). This prevents duplicate comments when re-running the review. ### Step 5: Post PR Comments The script posts two types of comments: 1. **Summary comment**: Overview table with issue counts (always posted, even if no new issues) 2. **Inline comments**: Detailed feedback on specific lines (HIGH/MEDIUM only) ```bash python3 scripts/post_comment.py \ --pr-number \ --repo \ --results consensus_results.json ``` Options: - `--dry-run`: Preview comments without posting - `--summary-only`: Only post summary, skip inline comments #### Example Summary Comment ```markdown ## :mag: Multi-Agent Code Review Found **4** new issue(s) flagged by 3 independent reviewers. (2 issue(s) skipped - already commented) ### Summary | Severity | Count | | ---------------------- | ----- | | :red_circle: HIGH | 1 | | :yellow_circle: MEDIUM | 2 | | :green_circle: LOW | 1 | ### Issues to Address | Severity | File | Issue | | ---------------------- | ------------------------ | ---------------------------------------- | | :red_circle: HIGH | `src/auth/login.ts:45` | SQL injection in user lookup | | :yellow_circle: MEDIUM | `src/utils/cache.ts:112` | Missing error handling for Redis failure | | :yellow_circle: MEDIUM | `src/api/handler.ts:89` | Confusing control flow - hard to debug |

:green_circle: Low Priority Issues (1 items)

- **Inconsistent naming convention** - `src/utils/helpers.ts:23`

See inline comments for details. _Generated by multi-agent consensus review_ ``` ## File Structure ``` scripts/ orchestrate_review.py - Main orchestrator, spawns sub-agents aggregate_results.py - Consensus voting logic post_comment.py - Posts findings to GitHub PR references/ review_prompt.md - Sub-agent review prompt template issue_schema.md - JSON schema for issue output ``` ## Configuration Environment variables: - `GITHUB_TOKEN` - Required for PR access and commenting Note: `ANTHROPIC_API_KEY` is **not required** - sub-agents spawned via the Task tool automatically have access to Anthropic. Optional tuning in `orchestrate_review.py`: - `NUM_AGENTS` - Number of sub-agents (default: 3) - `CONSENSUS_THRESHOLD` - Min agents to agree (default: 2) - `MIN_SEVERITY` - Minimum severity to report (default: MEDIUM) - `THINKING_BUDGET_TOKENS` - Extended thinking budget (default: 128000) - `MAX_TOKENS` - Maximum output tokens (default: 128000) ## Extended Thinking This skill uses **extended thinking (interleaved thinking)** with **max effort** by default. Each sub-agent leverages Claude's extended thinking capability for deeper code analysis: - **Budget**: 128,000 thinking tokens per agent for thorough reasoning - **Max output**: 128,000 tokens for comprehensive issue reports To disable extended thinking (faster but less thorough): ```bash python3 scripts/orchestrate_review.py \ --pr-number \ --repo \ --diff-file pr_diff.patch \ --no-thinking ``` To customize thinking budget: ```bash python3 scripts/orchestrate_review.py \ --pr-number \ --repo \ --diff-file pr_diff.patch \ --thinking-budget 50000 ```

dyadswarm-to-plan

Scan your own AI components