Maintainer of this skill?
Automated security scan — not a certification.
by revfactory
A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.
# Add to your Claude Code skills
git clone https://github.com/revfactory/harnessGuides for using ai agents skills like harness.
Last scanned: 4/22/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-04-22T06:02:12.991Z",
"semgrepRan": false,
"npmAuditRan": true,
"pipAuditRan": true
}See how harness compares with popular alternatives.
harness is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by revfactory. A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use. It has 9,342 GitHub stars.
Yes. harness passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/revfactory/harness" and add it to your Claude Code skills directory (see the Installation section above).
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh harness against similar tools.
No comments yet. Be the first to share your thoughts!
Based on votes and bookmarks from developers who liked this skill
From $49 · 30 days · terms & refunds
⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
English | 한국어
Harness is a team-architecture factory for Claude Code. One sentence — "build a harness for this project" · "하네스 구성해줘" — and the plugin turns your domain description into an agent team and the skills they use.
v2 is a ground-up rebuild for the current Claude Code multi-agent runtime:
TeamCreate API, which no longer exists. v2 targets what actually ships today:
pipeline() / parallel() / schemas / budgets) for fan-outs, verification loops, and large-scale runsSendMessage + shared task lists, with context retained across turnsCLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 dependency is gone entirely.model: "opus". v2 selects a tier per agent — fable / opus / sonnet — based on the task's complexity, duration, autonomy, and latency needs, and forbids unjustified blanket pins./harness:evolve actually ships. The evolution mechanism v1 only documented is now a real skill: it captures the delta between your initial and current harness, generalizes feedback, and feeds it back into agents/skills/orchestrators.TeamCreate, TeamDelete, experimental flags) and offers a mechanical migration path./harness:evolve turns usage feedback into measurable next-generation improvementsPhase 0: Audit existing harness (new / extend / maintain — v1 artifacts detected here)
Phase 1: Domain analysis (incl. control-flow shape of the work)
Phase 2: Execution mode & team architecture design
Phase 3: Agent definitions (.claude/agents/)
Phase 4: Skill generation (.claude/skills/)
Phase 5: Orchestration & CLAUDE.md pointer
Phase 6: Verification & testing
Phase 7: Maintenance — evolution via /harness:evolve
/plugin marketplace add revfactory/harness
/plugin install harness@harness-marketplace
cp -r skills/harness ~/.claude/skills/harness
cp -r skills/evolve ~/.claude/skills/harness-evolve
No environment variables or experimental flags required.
하네스 구성해줘
build a harness for this project
design an agent team for <domain>
After using a generated harness:
하네스 회고해줘 / evolve the harness with this feedback
| Mode | Primitive | When |
|---|---|---|
| Workflow orchestration | Workflow scripts |
Control flow is deterministic: enumerable fan-outs, verification loops, large scale, structured outputs |
| Persistent agents | Agent(name:) + SendMessage + tasks |
Long-lived specialists that keep context; iterative feedback and negotiation |
| Sub-agent delegation | one-shot Agent calls |
Fire-and-forget parallel work; results only |
The factory picks the mode from the shape of the control flow, not from team size — and mixes modes per phase when that fits better.
your-project/
├── .claude/
│ ├── agents/ # agent definitions (who)
│ │ ├── analyst.md
│ │ ├── builder.md
│ │ └── qa.md
│ └── skills/ # skills (how) + one orchestrator (who-when-in-what-order)
│ ├── analyze/SKILL.md
│ └── build/SKILL.md
└── CLAUDE.md # minimal pointer: trigger rule + change history
See docs/migration-v1-to-v2.md. Summary: remove TeamCreate/TeamDelete/broadcast/flag references, convert fan-outs to Workflow scripts, rewrite remaining collaboration with named agents + SendMessage, drop blanket model: "opus" pins. The factory automates this when it detects v1 artifacts (Phase 0).
A controlled A/B on 15 software-engineering tasks measured the effect of structured pre-configuration on LLM code-agent output quality: mean quality 49.5 → 79.3 (+60%), 15/15 win rate, −32% output variance (n=15, author-run, see revfactory/claude-code-harness). Treat these as author-measured numbers; run your own pilot for adoption decisions.
Apache 2.0