Borrowing it
Nothing to install: this file belongs to QuantumClaw/QClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/QuantumClaw/QClaw/main/AGENTS.mdgit clone --depth 1 https://github.com/QuantumClaw/QClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/quantumclaw/qclaw/agents-md)<a href="https://agentmods.dev/instructions/quantumclaw/qclaw/agents-md"><img src="https://agentmods.dev/badge/instructions/quantumclaw/qclaw/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/quantumclaw/qclaw/agents-md"><img src="https://agentmods.dev/badge/instructions/quantumclaw/qclaw/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00595 | $0.00595 |
| Opus 5.5 | $0.00238 | $0.00238 |
| Sonnet 5.5 | $0.00119 | $0.00119 |
| Haiku 4.5 | $0.00060 | $0.00060 |
Grade A, and why
QClaw AGENTS.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 1mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — For AI Coding Agents
This file tells AI coding agents (Claude Code, Copilot, Cursor, etc.) how to work on the QuantumClaw codebase.
Project Overview
QuantumClaw (QClaw) is a Node.js AI agent runtime. ESM modules throughout. No TypeScript.
Architecture
src/
├── index.js # Bootstrap (8-layer startup sequence)
├── credentials.js # AGEX credential manager (wraps secrets + AGEX SDK)
├── agex-sdk/ # Vendored AGEX SDK (AgexClient, crypto)
├── core/ # Config, logger, heartbeat, delivery queue, completion cache
├── security/ # SecretStore (AES-256-GCM), TrustKernel, AuditLog, approvals
├── memory/ # Cognee knowledge graph + SQLite conversation memory
├── models/ # 5-tier smart router, provider adapters
├── agents/ # Agent registry, personality loading from workspace
├── skills/ # Drop-in markdown skill loader
├── channels/ # Telegram, Discord, Slack, WhatsApp, WebSocket
├── dashboard/ # Express + WS dashboard server
└── cli/ # Onboarding wizard, commands, branding
Conventions
- ESM imports (
import/export, notrequire) - British English in docs and comments
- No TypeScript
- No em dashes in comments or docs
- Minimal dependencies (justify any new npm package)
log.info/warn/error/debug/successfor logging (not console.log)
Running
npm install
npx qclaw onboard # first time
npx qclaw start # run the agent
npx qclaw diagnose # health check
Testing
npm test # runs test suite
npx qclaw diagnose # runtime health check
Key Patterns
- Graceful degradation: If Cognee is down, memory falls back to SQLite. If AGEX Hub is down, credentials fall back to local encrypted secrets. Nothing crashes.
- 8-layer startup: Security → Memory → Models → Skills → Agents → Channels → Dashboard → Heartbeat. Each layer can fail without killing the rest.
- Credential resolution:
credentials.get('key')checks AGEX first, then local secrets. Drop-in compatible with SecretStore. - Trust Kernel: VALUES.md defines hard limits. The agent cannot override these. Code that enforces VALUES.md rules should never have a bypass.
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.
- 1mo ago First seen · 66 lines · 595 tokens per session scan A 2be70c7f50b6
QClaw AGENTS.md is an instructions file published in the GitHub repository QuantumClaw/QClaw (53 stars, last pushed 4mo ago), licensed MIT. It adds 595 tokens to every session, about $0.0024 per session on Opus 5.5. 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.
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