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 agentmods add skills/aiming-lab/metaclaw/data-validation-firstnpx skills add aiming-lab/MetaClaw --skill data-validation-firstgit 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/data-validation-first)<a href="https://agentmods.dev/skills/aiming-lab/metaclaw/data-validation-first"><img src="https://agentmods.dev/badge/skills/aiming-lab/metaclaw/data-validation-first.svg" alt="Measured on agentmods" 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 | $0.00030 | $0.00192 |
| Opus 5 | $0.00015 | $0.00096 |
| Sonnet 5 | $0.00006 | $0.00038 |
| Haiku 4.5 | $0.00003 | $0.00019 |
Grade A, and why
data-validation-first 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 5d 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
Data Validation First
Before writing any analysis code, understand the data:
# Always run these first
df.shape # rows x columns
df.dtypes # column types
df.isnull().sum() # missing values per column
df.describe() # statistics for numeric columns
df.head() # sample rows
Key questions:
- Are there nulls in columns you'll join or filter on?
- Are numeric columns stored as strings? (parse_dates, astype)
- Are there unexpected duplicates (check primary key uniqueness)?
- Does the row count match your expectation from the source?
Anti-pattern: Running .groupby().sum() without first checking for nulls in the groupby key.
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.
- 5d ago First seen · 27 lines · 30 tokens per session scan A 61aec1fb47d0
data-validation-first is a skill published in the GitHub repository aiming-lab/MetaClaw (3,494 stars, last pushed 2mo ago), licensed MIT. It adds 30 tokens to every session and 192 once invoked, about $0.0002 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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