Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/mastersof-ai/harness/claude-mdgit clone --depth 1 https://github.com/mastersof-ai/harnessWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00798 | $0.00798 |
| Opus 5 | $0.00399 | $0.00399 |
| Sonnet 5 | $0.00160 | $0.00160 |
| Haiku 4.5 | $0.00080 | $0.00080 |
Grade A, and why
harness CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Masters Of AI Harness
Related Docs
- DESIGN.md — overview with links to docs/
- docs/ — architecture, agents, tools, configuration, sandbox, design decisions
- CHANGELOG.md — version history
Quick Orientation
Standalone agent runtime built on top of the Claude Agent SDK. Two interfaces, one runtime:
- Terminal TUI —
mastersof-ai [--agent x]— React/Ink, single user, local - Web UI —
mastersof-ai --serve— Fastify backend + React SPA frontend, multi-user, token auth
Both share agent loading, tools, sub-agents, sessions, and memory.
- TypeScript, runs via tsx (no build step for backend)
- Entry:
bin/mastersof-ai.js→src/index.tsx - Agent loading:
src/agent-context.ts+src/manifest.ts(frontmatter) →src/agent.ts - Tools:
src/tools/— in-process MCP servers, one per domain - Serve mode:
src/serve.ts— Fastify HTTP/WS server (REST API + WebSocket streaming) - Web frontend:
web/— React + Vite + Tailwind SPA (deploys to Cloudflare Pages) - A2A:
src/a2a/— agent card generation, A2A protocol server/client - TUI:
src/components/— React/Ink (DO NOT TOUCH unless broken) - Config:
~/.mastersof-ai/config.yaml - Auth (serve mode):
~/.mastersof-ai/access.yaml - Security:
src/env-safety.ts,src/url-safety.ts,src/content-safety.ts - Credentials:
src/credentials.ts+src/egress-proxy.ts - Process isolation:
src/ipc-protocol.ts,src/session-worker.ts,src/worker-manager.ts - WS protocol:
src/ws-protocol.ts,src/sdk-stream.ts,src/query-mutex.ts - Health/observability:
src/health.ts,src/rate-limit.ts,src/cost.ts - Privacy:
src/privacy.ts— LGPD compliance - CLI subcommands:
src/cli/— modular command handlers
Running Locally
npx tsx bin/mastersof-ai.js # TUI with default agent
npx tsx bin/mastersof-ai.js --agent researcher # TUI with specific agent
npx tsx bin/mastersof-ai.js --serve # Web UI server on port 3200
npx tsx bin/mastersof-ai.js --serve --port 5000 # Web UI on custom port
npx tsx bin/mastersof-ai.js --agent researcher --sandbox # Bubblewrap sandbox
npx tsx bin/mastersof-ai.js --card # Output Agent Card JSON
npx tsx bin/mastersof-ai.js --list-agents
# CLI subcommands
npx tsx bin/mastersof-ai.js run billing "Run monthly billing" # Headless run
npx tsx bin/mastersof-ai.js credentials check --agent billing # Check agent credentials
npx tsx bin/mastersof-ai.js credentials migrate billing # Generate migration YAML
npx tsx bin/mastersof-ai.js access create --name partner --agents a,b # Create access token
npx tsx bin/mastersof-ai.js access rotate --name partner # Rotate token
npx tsx bin/mastersof-ai.js status billing # Recent run results
npx tsx bin/mastersof-ai.js preflight --agent billing # Validate full config
npx tsx bin/mastersof-ai.js create my-agent # Scaffold new agent
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 57 lines · 798 tokens per session scan A 40995781a7c1
harness CLAUDE.md is an instructions file published in the GitHub repository mastersof-ai/harness (40 stars, last pushed 5mo ago), licensed MIT. It adds 798 tokens to every session, about $0.0040 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
agentic-sandbox CLAUDE.md
Claude Code instructions for jmagly/agentic-sandbox, covering claude.md, repository purpose, tech stack, development commands and management server.
agentic-sandbox AGENTS.md
AGENTS.md instructions for jmagly/agentic-sandbox, covering agents.md, project overview, tech stack, development commands and project structure.
Sponsio CLAUDE.md
Instructions for SponsioLabs/Sponsio, covering agent guide for sponsio, what sponsio is, positioning, what to read first and repository map.
gini-agent AGENTS.md
AGENTS.md instructions for Open-Curiosity/gini-agent, covering gini agent instructions, shape, adrs, boundaries and branches.
innovation-lab-examples typescript.instructions.md
Instructions for fetchai/innovation-lab-examples, a project described as: 80+ production-ready AI agent examples in Python — build autonomous agents, multi-agent systems and agentic AI with uAgents, ASI:One, MCP, A2A, LangChain, CrewAI, Gemini, Claude and OpenAI.
innovation-lab-examples python.instructions.md
Instructions for fetchai/innovation-lab-examples, a project described as: 80+ production-ready AI agent examples in Python — build autonomous agents, multi-agent systems and agentic AI with uAgents, ASI:One, MCP, A2A, LangChain, CrewAI, Gemini, Claude and OpenAI.