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 zpower426/datapowers --skill requesting-statistical-reviewgit clone --depth 1 https://github.com/zpower426/datapowersWrote 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/zpower426/datapowers/requesting-statistical-review)<a href="https://agentmods.dev/skills/zpower426/datapowers/requesting-statistical-review"><img src="https://agentmods.dev/badge/skills/zpower426/datapowers/requesting-statistical-review/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/zpower426/datapowers/requesting-statistical-review"><img src="https://agentmods.dev/badge/skills/zpower426/datapowers/requesting-statistical-review.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 | $0.00035 | $0.00523 |
| Opus 5 | $0.00017 | $0.00262 |
| Sonnet 5 | $0.00007 | $0.00105 |
| Haiku 4.5 | $0.00003 | $0.00052 |
Grade A, and why
requesting-statistical-review 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.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requesting Statistical Review
Overview
A statistical review ensures the analytical output is correct, leakage-free, and statistically significant. This is more than a code review — it's an audit of the logic of discovery.
When to Use
Use this skill:
- After generating a
data-exploration(EDA) report. - After fitting transformers in
feature-engineering. - After every
model-evaluationrun. - Before reporting any conclusion to the user.
Review Focus
| Domain | Critical Check |
|---|---|
| Leakage | Any feature derived from post-prediction data? |
| CV Strategy | Does it respect time/group boundaries? |
| Metric Fit | Are they using AUC for imbalanced data, not just Accuracy? |
| Significance | Is the result better than the baseline with p < 0.05? |
| Reproducibility | Are random seeds (42) and artifact paths correct? |
The Checklist
Before declaring the task DONE, the reviewer must check:
- No target leakage in feature definitions.
- Transformers fit ONLY on training data.
- P-values or Confidence Intervals included for every comparison.
- Random seed set to 42 for all operations.
- Calibration curve checked for classification tasks.
Anti-Patterns
- "Probably fine": Passing a review without looking at the distribution code.
- Metric Hacking: Reporting only the best fold instead of OOS average.
- Ignoring Baseline: Reporting 90% accuracy without mentioning the 89% dummy baseline.
The Iron Law
NO CONCLUSIONS WITHOUT STATISTICAL SIGNIFICANCE TESTING.
Manifest Integration
| Action | Manifest update |
|---|---|
| Review dispatched | Read-only — do NOT write to manifest here |
| BLOCKED outcome | The invoking skill (executing-plans or subagent-driven-analysis) appends to manifest["warnings"] |
This skill does not write to the manifest directly. Its verdicts (APPROVED / ISSUES FOUND / BLOCKED) are consumed by
executing-plansorsubagent-driven-analysis, which write the result tomanifest["warnings"]ormanifest["tasks"].
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 57 lines · 35 tokens per session scan A d041b12dbbf0
requesting-statistical-review is a skill published in the GitHub repository zpower426/datapowers (1 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 523 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 2026-08-31.
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