harness

Orchestrate multi-agent software development sprints with feature ledgers and validation gates.

Updated Apr 3, 2026
One-click install
npx skills add https://github.com/xuzhijie-ownself/harness --skill harness-xuzhijie-ownself
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: harness
Source: https://github.com/xuzhijie-ownself/harness/tree/main/plugins/harness/skills/harness
Command: npx skills add https://github.com/xuzhijie-ownself/harness --skill harness-xuzhijie-ownself

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill turns short prompts into repeatable, long-running multi-agent development workflows that keep progress measurable, reduce context anxiety, and enforce rigorous separation of generation and evaluation so features actually pass rather than accumulate sprints.

Core Features & Use Cases

  • End-to-end sprint orchestration: coordinates initializer, planner, generator, evaluator, coordinator, and releaser roles for supervised or continuous runs.
  • Feature-led progress ledger: machine-readable features.json ledger, per-round artifacts, and auto-commit protocols to prevent lost work.
  • Robust validation and safety: authenticity gate, quantified evaluator scoring, artifact validation, and blocking gates before feature flips.
  • Context management: context reset handoffs, Variant A/B/C execution modes, and error recovery with retries and explicit stop/pause rules.
  • Use case: scaffold .harness/, propose sprint contracts, implement one failing required feature at a time, run generate→evaluate loops until required features pass.

Quick Start

Run /harness:start to scaffold the .harness directory and then run /harness:session to execute a single supervised sprint round.

Frequently Asked Questions about harness

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate multi-agent coding sprints for long-running development workflows?

Multi-agent coding sprints are orchestrated by dispatching initializer, planner, generator, evaluator, coordinator, and releaser agents to execute supervised or continuous development rounds. This approach turns short prompts into repeatable sprinted workflows that complete features one at a time.

How does multi-agent feature tracking prevent lost work during continuous development?

Feature tracking prevents lost work by maintaining a machine-readable features.json ledger and applying auto-commit protocols for per-round artifacts. This ensures measurable progress and stateful round management across long-running application harnesses without context loss.

Can I enforce strict code validation before completing a feature in an automated sprint?

Automated sprints enforce code validation through an authenticity gate, quantified evaluator scoring, and artifact validation. These blocking gates ensure features actually pass rigorous evaluation before being flipped to complete, preventing incomplete work from accumulating.

What is the best way to manage context limits when running continuous multi-agent development sessions?

Context limits are managed through context reset handoffs between agents during multi-agent development sessions. This process clears stale state between rounds, while error recovery mechanisms handle retries and explicit stop or pause rules during continuous runs.

How do I scaffold a multi-agent sprint environment and start a supervised development round?

Scaffold a multi-agent sprint environment by running the start command to create the .harness directory, which establishes the project structure. Then execute the session command to run a single supervised sprint round for incremental feature implementation.

Why does my multi-agent sprint accumulate incomplete features instead of passing validation gates?

Sprints accumulate incomplete features when the authenticity gate and quantified evaluator scoring are bypassed or misconfigured. Enforcing rigorous separation of generation and evaluation ensures features pass validation before advancing, keeping progress measurable.