Chachamaru127/claude-code-harness

Claude Code Dedicated Development Harness - Achieving High-Quality Development Through an Autonomous Plan→Work→Review Cycle

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Chachamaru127/claude-code-harness has 3k+ stars and 300+ forks on GitHub, is written in Shell, and was last pushed on Oct 15, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 36 minutes ago
Added to GitGenius on May 28th, 2026
Created on December 12th, 2025
Open Issues & Pull Requests: 1 (+0)
GitHub issues: Enabled
Number of forks: 302
Total Stargazers: 3,173 (+0)
Total Subscribers: 12 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.8 days
Mean response time: 8.2 days
90th percentile: 22.0 days
Tracked items: 60

How this project is maintained

Practically every issue opened in the past year has drawn a reply. 98% of issues opened in the past year have since been closed. Three people close 100% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 1
New in 7 days: 1
Closed in 7 days: 8
Avg open age: 0 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 1
Closed in 7 days: 6
Comments in 7 days: 6
Events in 7 days: 14

Top labels

  • bug (2)
  • codex (1)
  • enhancement (1)

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Detailed Description

Claude Code Harness is a development workflow tool that enforces a structured plan-work-review-release cycle for AI-assisted coding with Claude, Codex, Cursor, and Grok.

The tool addresses the problem of drift in agent-driven development, where plans exist only in chat history, testing becomes optional under pressure, and review happens after code is merged. Rather than making the underlying model smarter, it establishes a repeatable procedure with clear boundaries around the model's output. The approach works through five core commands: plan converts intent into a spec and task list for human approval, work implements approved tasks with tests where needed, review examines results independently from implementation, sync detects drift between plan and actual code, and release packages verified evidence into changelogs and tags. Each stage produces material the next stage requires, creating a disciplined loop where human approval gates execution.

Developers should adopt this tool if they want to enforce accountability and traceability in AI-assisted projects, particularly when working with multiple AI coding assistants. It suits teams that need audit trails, want to prevent scope creep during implementation, or require independent review before merging. The tool is built around the constraint that written code is not the same as working code, and uses CI gates to verify that described features are actually wired and reachable.

The maintainers respond to issues and pull requests within a few days. Development activity centers on bug fixes, codex integration work, and enhancements.