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 jeffreytse/grimoire-core --skill apply-statistical-process-controlgit clone --depth 1 https://github.com/jeffreytse/grimoire-coreWrote 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/jeffreytse/grimoire-core/apply-statistical-process-control)<a href="https://agentmods.dev/skills/jeffreytse/grimoire-core/apply-statistical-process-control"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-statistical-process-control/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/jeffreytse/grimoire-core/apply-statistical-process-control"><img src="https://agentmods.dev/badge/skills/jeffreytse/grimoire-core/apply-statistical-process-control.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 | $0.00086 | $0.01939 |
| Opus 5.5 | $0.00034 | $0.00776 |
| Sonnet 5.5 | $0.00017 | $0.00388 |
| Haiku 4.5 | $0.00009 | $0.00194 |
Grade A, and why
apply-statistical-process-control 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 1mo 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Apply Statistical Process Control
Plot a repeated process measurement on a control chart with limits calculated from the process's own historical common-cause variation, and only investigate or adjust the process when a point falls outside those limits or shows a non-random pattern — because reacting to ordinary variation within calculated limits increases variation rather than reducing it.
Why This Is Best Practice
Why best: SPC's specific contribution is a formal distinction between two categories of variation that look similar moment-to-moment but require opposite responses: common-cause variation is the process's normal, expected, statistically-characterized noise, and reacting to it (adjusting the process based on a single data point that is still within calculated limits) is a well-documented, measurable mistake — Deming's funnel experiment demonstrates that adjusting a stable process in response to its own normal variation increases the total variation rather than decreasing it. Special-cause variation, by contrast — a point outside the calculated control limits, or a specific non-random pattern within them — indicates a genuine, assignable change in the process that does warrant investigation. The discipline of calculating limits from the process's own actual historical behavior, rather than from a target or specification, is what makes the common-cause/special-cause distinction meaningful rather than arbitrary.
Shewhart, "Economic Control of Quality of Manufactured Product" (1931): Shewhart, working at Bell Labs, formalized the control chart and the underlying distinction between common-cause and special-cause variation, establishing that a process's center line and control limits (conventionally set at plus or minus three standard deviations from the mean) should be calculated from the process's own historical data, not set arbitrarily or derived from a customer specification. This distinction — statistical control (predictable, stable common-cause variation) as a property of the process itself, separate from whether that variation meets external specification requirements — is the foundational theoretical contribution SPC still rests on.
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.
- 1mo ago First seen · 65 lines · 86 tokens per session scan A 0d6fdf797d19
apply-statistical-process-control is a skill published in the GitHub repository jeffreytse/grimoire-core (5 stars, last pushed 5d ago), licensed MIT. It adds 86 tokens to every session and 1,939 once invoked, about $0.0003 per session on Opus 5.5. 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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