MetaClaw is an AI-agent system that learns from conversations and evolves its behavior over time. It provides memory and learning modes for users who want an agent that adapts across interactions, with support for multiple claw-based agent projects.
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 skills add aiming-lab/MetaClaw --skill secrets-managementgit clone --depth 1 https://github.com/aiming-lab/MetaClawWrote 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/skills/aiming-lab/metaclaw/secrets-management)<a href="https://agentmods.dev/skills/aiming-lab/metaclaw/secrets-management"><img src="https://agentmods.dev/badge/skills/aiming-lab/metaclaw/secrets-management/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/skills/aiming-lab/metaclaw/secrets-management"><img src="https://agentmods.dev/badge/skills/aiming-lab/metaclaw/secrets-management.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.00236 |
| Opus 5 | $0.00028 | $0.00118 |
| Sonnet 5 | $0.00011 | $0.00047 |
| Haiku 4.5 | $0.00006 | $0.00024 |
Grade A, and why
secrets-management 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 9d 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.
What it actually says
Secrets Management
Rules:
- Never hardcode secrets in source files, configs committed to git, or logs.
- Use environment variables for local development (
python-dotenv). - Use a secrets manager (AWS Secrets Manager, HashiCorp Vault, 1Password CLI) in production.
- Add
.envand*.pemto.gitignorebefore the first commit. - Rotate secrets immediately if they are exposed (leaked in a commit, log, or error message).
Scanning: Use ggshield, truffleHog, or git-secrets in CI to block secret commits.
Anti-patterns:
os.environ.get('KEY', 'hardcoded_default')in production code.- Logging full request/response bodies that may contain tokens.
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.
- 9d ago First seen · 21 lines · 55 tokens per session scan A 31022f24b891
secrets-management is a skill published in the GitHub repository aiming-lab/MetaClaw (3,495 stars, last pushed 3mo ago), licensed MIT. It adds 55 tokens to every session and 236 once invoked, about $0.0003 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.
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