By sliday
Harness Engineering for AI coding agents — CAR framework, security guardrails, quality gates, context monitoring, auto-formatters, trace logging, checkpoints, MCP validation
Build or rebuild AGENTS.md with progressive disclosure
Audit your project's harness against the CAR framework
Generate resume artifacts — LEARNED.md, CHECKPOINT.json — for long-running sessions
Add context monitoring and backpressure hooks to manage agent attention budget
Generate or update PostToolUse auto-formatter hook
Audit project harness against the CAR framework. Use when: /harn:analyze, 'audit harness', 'check CAR', 'harness health', 'how good is my harness'
Scaffold resume artifacts (LEARNED.md, CHECKPOINT.json) for long-running sessions. Triggers: /harn:checkpoint, 'resume artifacts', 'checkpoint', 'session recovery', 'LEARNED.md'
Generate context monitoring and backpressure hooks. Triggers: /harn:context, 'context monitoring', 'backpressure', 'context budget', 'attention budget', 'context rot'
Scaffold a complete harness for the current project — AGENTS.md, hooks, guardrails. Use when starting a new project or hardening an existing one. Triggers: /harn:init, 'set up harness', 'scaffold guardrails', 'create AGENTS.md'
Generate MCP server health check for SessionStart. Triggers: /harn:mcp-check, 'MCP health', 'check MCP servers', 'validate MCP'
Uses power tools
Uses Bash, Write, or Edit tools
No model invocation
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Sign in to claimnpx claudepluginhub sliday/harnBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Executes directly as bash, bypassing the AI model
Executes directly as bash, bypassing the AI model
Deterministic guardrails, quality gates, and progressive disclosure for AI coding agents.
The underlying AI model matters less than the system built around it. A well-harnessed agent produces consistent, safe, high-quality output regardless of the model version. Read the foundations.
claude plugins marketplace add sliday/claude-plugins
claude plugins install harn
Then in any project:
/harn:init # scaffold your harness
| Command | Description |
|---|---|
/harn:init | Scaffold a complete harness — AGENTS.md, hooks, guardrails |
/harn:guard | Generate or update security guardrail hooks (PreToolUse) |
/harn:gate | Generate or update quality gate Stop hook |
/harn:analyze | Audit your project against the CAR framework |
/harn:agents-md | Build or rebuild AGENTS.md with progressive disclosure |
/harn:format | Generate PostToolUse auto-formatter hook |
/harn:trace | Generate PostToolUse trace logging hook for observability |
/harn:checkpoint | Generate resume artifacts (LEARNED.md, CHECKPOINT.json) |
/harn:mcp-check | Generate MCP server health check hook |
/harn:context | Monitor context window usage and apply backpressure |
/harn:init CreatesAGENTS.md — Agent behavior spec with progressive disclosure.claude/settings.json — Hook configuration.claude/hooks/security-guard.sh — PreToolUse guardrail.claude/hooks/quality-gate.sh — Stop hook for type/lint/test checks.claude/hooks/auto-format.sh — PostToolUse formatter (optional)LEARNED.md — Persistent gotchas and decisions logEvery agentic system has three layers:
Run /harn:analyze to score your project across all three. Learn more at harn.app.
jq — JSON processing (used by hooks)python3 >= 3.6 — Script executiontsc, cargo, go vet, ruff)If a hook is blocking legitimate work or the agent is stuck in a loop, see references/escape-hatches.md.
Curated articles on harness engineering — security, context, evals, specs, and tools: harn.app/kb.
MIT
Harness Engineering framework - skills, agents, and commands for safe, reviewable, incremental agent-driven development. Includes RPEQ workflow (Research, Plan, Execute, QA), ast-grep setup, and codebase analysis tools.
Session harness plugin for Claude Code workflow automation
Makes a repo agent-ready: AGENTS.md, boundary tests, CI pipeline, GC scripts — based on OpenAI's harness engineering methodology
Core safety skills for AI-assisted development: Four Laws, Three Strikes, production-first, scope validation, and environment separation
Specification-first AI harness: 11 structural gates, 11 Ouroboros commands, 11 agent personas, and 3-tier architecture enforcement. v2.1: Pair Mode (Navigator-Driver + independent test design + /review command).
Harness for Claude Code — skills, /harness:* slash commands, persona subagents, lifecycle hooks, and MCP tools without per-repo `harness setup`. Sibling plugins exist for Cursor, Gemini CLI, and Codex.