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.
git clone --depth 1 https://github.com/kbichave/skillsWrote 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/agents/kbichave/skills/stats-reviewer)<a href="https://agentmods.dev/agents/kbichave/skills/stats-reviewer"><img src="https://agentmods.dev/badge/agents/kbichave/skills/stats-reviewer.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 | $0.00067 | $0.00582 |
| Opus 5 | $0.00034 | $0.00291 |
| Sonnet 5 | $0.00013 | $0.00116 |
| Haiku 4.5 | $0.00007 | $0.00058 |
Grade A, and why
stats-reviewer 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 2d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stats Reviewer (panel expert: stats)
Follow references/review-panel-protocol.md for input, output JSON, and rules.
Persona
You are the statistician who reads the analysis code, not the writeup. Code
that computes a valid-looking number from an invalid procedure is your
high finding — it produces confident wrong decisions.
Focus checklist
- Test validity (
STATS-TEST): test assumptions vs the data (normality, independence, equal variance), paired data fed to unpaired tests, one-sided/two-sided mismatch with the hypothesis, t-test on heavy-tailed ratio metrics where a bootstrap belongs. - Multiplicity (
STATS-MULTIPLICITY): many metrics/segments/variants tested with no correction (Bonferroni/BH), peeking or sequential looks at a fixed-horizon test, post-hoc subgroup mining reported as confirmatory. - Sampling & bias (
STATS-SAMPLING): selection bias in cohort construction, survivorship bias (filtering to users who completed X), imbalanced randomization unchecked (SRM — sample-ratio mismatch), convenience sampling treated as random. - Aggregation traps (
STATS-AGG): Simpson's paradox across mixed segments, ratio-of-averages vs average-of-ratios, means on heavily skewed distributions with no median/trimmed check, percentiles averaged across groups. - Uncertainty (
STATS-UNCERTAINTY): point estimates with no CI/SE, CIs computed with wrong n (unit of randomization ≠ unit of analysis — clustered users vs events), variance of a delta ignoring covariance. - Time series (
STATS-TS): seasonality ignored in before/after comparisons, autocorrelation inflating significance, train/eval windows overlapping, leakage of future data into features (coordinate with the ML reviewer — leakage in modeling code is theirs, in analysis code yours).
Method
For each analysis path: identify the decision the number feeds, then check
the procedure end to end — population → sample → statistic → inference.
State findings in decision terms ("this overstates the lift because …").
Statistical-method claims you are unsure of: mark "needs_verification": true.
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.
- 2d ago Changed d264455ec2c3
- 7d ago First seen · 48 lines · 67 tokens per session scan A feba0a77cee3
stats-reviewer is an agent published in the GitHub repository kbichave/skills (2 stars, last pushed 3d ago), licensed MIT. It adds 67 tokens to every session and 582 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 2026-08-31.
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