Harness Skill — AI skill for Claude Code
An Agent Skill that builds a complete harness for coding agents into any project: always-on rules (AGENTS.md), evaluator and librarian subagents, an LLM wiki, agent-friendly scripts, no-questions runs.
How to install Harness Skill
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open hwalde/harness-skill and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Harness Skill does
An Agent Skill that builds a complete harness for coding agents into any project: always-on rules (AGENTS.md), evaluator and librarian subagents, an LLM wiki, agent-friendly scripts, no-questions runs, freilauf integration - guided setup, updatable via git pull. DE/EN/ZH.
Alternatives in AI
- WeKnora — Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, an 26.9k ★
- Open Knowledge — Beautiful, AI-native markdown IDE and LLM wiki 3.7k ★
- Wiki Graph — Build the LLM Wiki knowledge graph 2.3k ★
README
harness – a skill that builds the harness for your coding agents
**English** · [中文](README.zh-CN.md) · [Deutsch](README.de.md)
**Let coding agents work on company IT projects autonomously – and safely.** This skill builds a complete harness into any project: rules that always apply, a sceptical second reviewer that signs off every piece of work, a librarian that keeps the project memory, scripts that do the legwork, and a start script for runs that never ask questions. Your agent installs it, builds it, walks you through the setup, and keeps it up to date.
🤖 One line to start
git clone https://github.com/hwalde/harness-skill ~/.claude/skills/harnessThen, in your project, tell your coding agent: *"Load the `harness` skill and build the harness for this project with me."* (Other agents: clone into their skill folder – the skill tells the agent where.)
Why a harness
A harness is everything except the language model: the environment the agent runs in. Rule files, subagents, skills, MCP servers, scripts, a knowledge store, start and monitoring mechanics. Without one, an agent works from a blank context, grades its own work, forgets what the last session learned, and stops at the first question nobody answers. With this one:
- Every result is checked by a second pair of eyes. The
evaluatorsubagent reads specification, diff and evidence in a fresh context, with no write access, and answersPASSorNEEDS_WORK. The builder never accepts its own work. - Knowledge survives the session. The
librarianis the only door to an LLM wiki in the repository: decisions with their reasons, pitfalls, operational knowledge – filtered, so the context stays lean. - Runs never stall. Permission modes for no-questions runs, named start and attach scripts per agent and OS (
claude-background-start,claude-attach) for long-running tasks in tmux/psmux sessions, usage tracking, self-monitoring, and the rule "question round first, then
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Search Issues
gh search issues --repo {owner/repo} "llms.txt OR AI-friendly OR LLM documentation OR AI context" --limit 20 g