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CODINGOrchestrate a Claude Code Agent Team to Deliver a Complete Feature From GitHub Issue to Merged PR◆DESIGNShip a Complete Product Feature From Design to Production Code Using the Claude Design and Claude Code Closed Loop◆RESEARCHTurn Every Customer Call Into a Structured JTBD Signal and Generate a Quarterly Product Brief Automatically◆OPERATIONSAuto-Diagnose Failing Data Pipelines and Get a Root-Cause Brief Before You Open a Log◆RESEARCHRun an Autonomous Competitive Intelligence Agent That Detects Changes and Posts to Slack Weekly◆CUSTOMER SUPPORTBuild a Self-Correcting RAG Support Agent That Knows When to Escalate◆

Edition № 001 · Monday, July 13, 2026The signal-first AI publication for builders

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Run a Multi-Agent PR Review That Posts Inline GitHub Comments Automatically - Claude AI Workflow | UseCaseVault

Inputs Needed

GitHub repository with API access, GitHub personal access token or GitHub App credentials with pull_request read/write permissions, Claude API key, Python 3.10+ environment with LangGraph and PyGithub installed

Quality Check

Before enabling on your main branch, run the full agent loop on a draft PR with 3–5 known issues you planted deliberately. Confirm inline comments appear on the correct lines. Verify the orchestrator correctly deduplicates when two agents flag the same line. Check that the merge decision matches what a senior engineer would conclude from the same findings.

Limits / Watchouts

Works best on PRs under 2,000 lines of diff. Larger PRs should be chunked by file and aggregated. Cannot detect runtime vulnerabilities, timing attacks, race conditions, or logic flaws that only appear with specific data at runtime — this is static analysis. GitHub API rate limits apply (5,000 requests/hour for authenticated requests); large teams with high PR velocity may need request batching.

Additional Notes

Stack: LangGraph (agent orchestration), PyGithub or raw GitHub REST API (PR data and comment posting), Anthropic SDK (Claude Sonnet for all agent prompts). Each agent prompt is intentionally scoped to one responsibility — do not combine Security and Logic into a single agent call. The quality of the Context Agent's file retrieval directly determines the quality of the Logic Agent's findings.

The Problem

Code review is the highest-leverage quality gate in engineering and the most inconsistent one. The fifth PR a reviewer sees in a day gets a fraction of the attention of the first. Security gaps, missing edge cases, and test coverage holes slip through not because reviewers lack skill but because review fatigue is real. Automated linters catch syntax. This workflow catches the things linters miss.

Workflow Steps

1
Set up a LangGraph StateGraph with a ReviewState TypedDict: diff, context_files, security_findings, logic_findings, test_gaps, final_review
2
Fetch Agent: call GET /repos/{owner}/{repo}/pulls/{pull_number}/files for the diff and GET the linked issue for context — store both in state
3
Context Agent: retrieve full file content for the 5 most modified files via GitHub API to understand module intent before specialist review
4
Security Agent: pass the diff to Claude with a focused prompt covering OWASP Top 10, hardcoded secrets, missing auth checks, insecure direct object references — return JSON findings
5
Logic Agent: check for null/undefined access on new code paths, off-by-one errors, missing error handling, unawaited async calls, wrong assumptions about input — return JSON findings
6
Test Coverage Agent: identify every new function, branch, or conditional in the diff — for each, determine if a test exists in the PR; if not, write the minimal test case
7
Orchestrator Agent: deduplicate findings that flag the same line from multiple agents, resolve severity conflicts (take the higher), write a review in three sections: Critical / Needs Attention / Looks Good

Prompt Example

You are the lead reviewer synthesising three specialist reports for PR #{pr_number}. SECURITY FINDINGS: {security_findings} LOGIC FINDINGS: {logic_findings} TEST COVERAGE GAPS: {test_gaps} 1. Deduplicate: if two agents flagged the same line for related reasons, merge into one comment. 2. Resolve conflicts: if agents disagree on severity, pick the higher one and explain why in one sentence. 3. Write a final review in three labelled sections: Critical (must fix before merge), Needs Attention (should fix), Looks Good. 4. Keep each inline comment under 60 words. Be specific to the exact line and exact risk. 5. End with a one-paragraph overall assessment and a merge decision: Approve / Request Changes / Needs Discussion. Return JSON: {"summary": "...", "merge_decision": "...", "inline_comments": [{"file": "...", "line": 0, "body": "...", "severity": "..."}]}

Shared by AI Pulse contributor

Jun 22, 2026

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Community Use Cases

Community Use Cases

Run a Multi-Agent PR Review That Posts Inline GitHub Comments Automatically

Outcome

Inputs Needed

Quality Check

Limits / Watchouts

Additional Notes

The Problem

Workflow Steps

Prompt Example

Find Related Workflows