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 niels-emmer/myace --skill data-validationgit clone --depth 1 https://github.com/niels-emmer/myaceWrote 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/niels-emmer/myace/data-validation)<a href="https://agentmods.dev/skills/niels-emmer/myace/data-validation"><img src="https://agentmods.dev/badge/skills/niels-emmer/myace/data-validation.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 5.1 | $0.00020 | $0.00292 |
| Opus 5 | $0.00010 | $0.00146 |
| Sonnet 5 | $0.00004 | $0.00058 |
| Haiku 4.5 | $0.00002 | $0.00029 |
Grade A, and why
Data Validation 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 4d 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
Purpose
Catch data problems before they waste training time or produce misleading models.
When to use it
Before every modeling pass, especially when working with new or updated data.
Checklist
- Shape and type check: confirm columns, dtypes, and row counts match expectations.
- Null distribution: count and visualize missing values per column; decide on imputation strategy.
- Value ranges: check min/max/unique values for each feature; flag out-of-domain values.
- Train/test distribution: compare feature distributions across splits (KS test or visualization); flag drift.
- Target leakage scan: check for time-based leakage, ID columns, future-looking features in training data.
- Class imbalance: quantify target distribution; plan for stratification or weighting if needed.
- Temporal ordering: for time-series data, confirm no future data leaks into training windows.
Expected output
A data validation report with PASS/FAIL per check, attached to the experiment tracker entry.
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.
- 4d ago First seen · 30 lines · 20 tokens per session scan A 423f66ec2b86
Data Validation is a skill published in the GitHub repository niels-emmer/myace (1 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 292 once invoked, about $0.0001 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 2026-09-03.
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