openlovable-multi-pr-review

name

openlovable: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

IMPORTANT: Always save files to the current working directory (e.g. ./pr_diff.patch), never to /tmp/ or other directories outside the repo. In CI, only the repo working directory is accessible.

# Get changed files from PR (save to current working directory, NOT /tmp/)
gh pr diff <PR_NUMBER> --repo <OWNER/REPO> > ./pr_diff.patch

# Or get list of changed files
gh pr view <PR_NUMBER> --repo <OWNER/REPO> --json files -q '.files[].path'

Step 2: Run Multi-Agent Review

Execute the orchestrator script:

python3 scripts/orchestrate_review.py \
  --pr-number <PR_NUMBER> \
  --repo <OWNER/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)

python3 scripts/post_comment.py \
  --pr-number <PR_NUMBER> \
  --repo <OWNER/REPO> \
  --results consensus_results.json

Options:

• --dry-run: Preview comments without posting
• --summary-only: Only post summary, skip inline comments

Example Summary Comment

## :mag: Open-Lovablebot Code Review Summary

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   |

<details>
<summary>:green_circle: Low Priority Issues (1 items)</summary>

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

</details>

See inline comments for details.

_Generated by Open-Lovablebot code 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):

python3 scripts/orchestrate_review.py \
  --pr-number <PR_NUMBER> \
  --repo <OWNER/REPO> \
  --diff-file ./pr_diff.patch \
  --no-thinking

To customize thinking budget:

python3 scripts/orchestrate_review.py \
  --pr-number <PR_NUMBER> \
  --repo <OWNER/REPO> \
  --diff-file ./pr_diff.patch \
  --thinking-budget 50000