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 deciqAI/knowledge-skills --skill regression-to-the-meangit clone --depth 1 https://github.com/deciqAI/knowledge-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/skills/deciqai/knowledge-skills/regression-to-the-mean)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/regression-to-the-mean"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/regression-to-the-mean/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/deciqai/knowledge-skills/regression-to-the-mean"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/regression-to-the-mean.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 | $0.00117 | $0.01906 |
| Opus 5 | $0.00059 | $0.00953 |
| Sonnet 5 | $0.00023 | $0.00381 |
| Haiku 4.5 | $0.00012 | $0.00191 |
Grade A, and why
regression-to-the-mean 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Regression to the Mean
Overview
Regression to the mean is the statistical regularity that any noisy measurement producing an extreme value tends to be followed on retest by a less-extreme value — because the extreme portion was partly driven by non-repeating random noise. There is no "force pulling back to average"; it is a mathematical consequence of signal + noise structure.
Named by Francis Galton (1886) studying parent-child height: tall parents have tall children, but slightly shorter; short parents have short children, but slightly taller. Kahneman's Israeli Air Force example (2011, Ch. 17) is the most-cited operational case — flight instructors concluded punishment works and praise doesn't, but were observing regression, not causation.
Composes with survivorship-bias (extreme survivors regress), probabilistic-thinking (regression is probabilistic), narrative-fallacy (regression drives post-hoc narratives), fundamental-attribution-error (attributing regression to character/intervention is FAE).
When to Use
- Evaluating the effect of an intervention on extreme performers (struggling teams, top sales reps, low-rated branches)
- Designing or interpreting A/B tests or pilot programs
- Reviewing year-over-year performance changes
- Hiring or promoting top performers
- Evaluating investment fund performance
- Analyzing acquisition outcomes
- Building or critiquing causal claims about training, coaching, or feedback
- Judging whether an AI startup's viral quarter, a fund's AI hot streak, or a model's benchmark spike is a durable trend or an outlier reverting toward average (AI hype extrapolation)
- Someone says "regression to the mean," "things will average out," "they always come back"
Not when: the measurement is noise-free (rare in business); the underlying signal is genuinely changing (e.g., the business model fundamentally improved); the intervention is so substantial that no plausible regression can explain the effect.
What ships with it
3 files 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 · 125 lines · 117 tokens per session scan A 39336e72de20
regression-to-the-mean is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 11d ago), licensed MIT. It adds 117 tokens to every session and 1,906 once invoked, about $0.0006 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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