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 debug-systematicallygit 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/debug-systematically)<a href="https://agentmods.dev/skills/aiming-lab/metaclaw/debug-systematically"><img src="https://agentmods.dev/badge/skills/aiming-lab/metaclaw/debug-systematically/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/debug-systematically"><img src="https://agentmods.dev/badge/skills/aiming-lab/metaclaw/debug-systematically.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 | $0.00040 | $0.00216 |
| Opus 5.5 | $0.00016 | $0.00086 |
| Sonnet 5.5 | $0.00008 | $0.00043 |
| Haiku 4.5 | $0.00004 | $0.00022 |
Grade A, and why
debug-systematically 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.
What it actually says
Debug Systematically
Process:
- Reproduce the bug with the smallest possible input.
- Isolate — narrow down which component/function causes it.
- Hypothesize — form a specific, falsifiable hypothesis.
- Test — verify or disprove the hypothesis with a minimal experiment.
- Fix — address the root cause, not just the symptom.
- Verify — re-run the failing test and related tests.
Useful tools: print/logging, pdb/ipdb, unit tests, git bisect.
Anti-patterns:
- Changing multiple things at once and not knowing what fixed it.
- Ignoring related test failures.
- Commenting out code instead of understanding it.
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 · 23 lines · 40 tokens per session scan A be01e4c65043
debug-systematically is a skill published in the GitHub repository aiming-lab/MetaClaw (3,464 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 216 once invoked, about $0.0002 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.
Other skills, from other repositories
fix-failing-tests
Diagnose a failing test in the googleapis/mcp-toolbox repo and land a fix by reasoning from the actual error: read the failure, reproduce it, shrink it until the cause is forced into the open, then fix the cause. Use this whenever a test or CI job is red, a build breaks after a change, many packages fail at once, or a…
reproduce-bug
Reproduce a reported bug in googleapis/mcp-toolbox and decide whether it is real, delivering an evidence-backed verdict: confirmed, already fixed, misconfiguration, client-side, works as intended, not reproducible, or blocked. Use whenever a maintainer asks you to reproduce, verify, confirm, or investigate a bug…
triage-issues
Triage GitHub issues in the googleapis/mcp-toolbox repo: propose the correct labels (type / priority / product / status), check for duplicates, verify a bug has enough info to act on, and draft a triage comment. Use whenever a maintainer asks you to triage, label, categorize, prioritize, or "look at" an issue (or a…
langbot-plugin-dev
Develop, debug, and test LangBot plugins. Use when creating new LangBot plugins, fixing plugin bugs, setting up a LangBot test environment, or testing plugins via WebSocket. Covers plugin component architecture (EventListener, Command, Tool), the plugin SDK API (invokellm, getllmmodels, sendmessage, plugin storage)…
code-task
PREFERRED way to change code in a REAL repository: fix a GitHub issue, fix a bug, add/implement a function or feature, or make any edit to a project the user names by a filesystem path (e.g. /tmp/foo, /code/bar) or a git URL. Clones the repo, runs an OpenSquilla agent on the host to make the change on a task branch…
stack-trace-generic-probe
Internal helper for meta-stack-trace-investigator. Use when a stack trace language is unknown and the workflow needs language-neutral failure-contract checks, reproducer guidance, and patch targets.