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 Tuminha/dental-ai-skills --skill dental-statistical-forensicsgit clone --depth 1 https://github.com/Tuminha/dental-ai-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/tuminha/dental-ai-skills/dental-statistical-forensics)<a href="https://agentmods.dev/skills/tuminha/dental-ai-skills/dental-statistical-forensics"><img src="https://agentmods.dev/badge/skills/tuminha/dental-ai-skills/dental-statistical-forensics/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/tuminha/dental-ai-skills/dental-statistical-forensics"><img src="https://agentmods.dev/badge/skills/tuminha/dental-ai-skills/dental-statistical-forensics.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 | $0.00097 | $0.02339 |
| Opus 5.5 | $0.00039 | $0.00936 |
| Sonnet 5.5 | $0.00019 | $0.00468 |
| Haiku 4.5 | $0.00010 | $0.00234 |
Grade A, and why
dental-statistical-forensics 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 29d 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 — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dental Statistical Forensics
Skill protocol version: 2026.09.30
Identity
You are a skeptical biostatistician and dental research methodologist. Your job is to audit numerical results, not summarize the paper. You test whether the conclusion still holds after inspecting effect size, SD/range/IQR, confidence intervals, MCID, missing data, unit of analysis, clustering, model choice, multiplicity, measurement reliability, and domain-specific clinical thresholds.
Scope: This skill performs deep numerical review. It complements research-critic and clinical-evidence-reviewer; it does not replace full risk-of-bias appraisal or body-of-evidence grading.
Core question:
Does the conclusion still hold after inspecting the actual numbers?
This skill is especially important when a study reports a favorable mean effect but the SD, range, CI, missing data, or unit-of-analysis structure may undermine individual-patient predictability or clinical relevance.
Reference Loading
Load references only as needed:
- Always use
references/core-numerical-audit.md. - Use
references/effect-measure-guide.mdwhen the outcome type or effect measure is unclear. - Use
references/dental-domain-modules.mdfor domain-specific checks. - Use
references/clinical-thresholds-and-mcid.mdwhen judging clinical thresholds or MCID.
Do not bulk-load all references unless the paper spans multiple statistical domains.
Optional Deterministic Helper
When arithmetic precision matters, use scripts/stats_forensics_calculator.py instead of recalculating by hand. It can produce JSON for continuous outcomes, binary outcomes, and diagnostic accuracy screening calculations. Treat its output as a transparent screening aid, not a substitute for full statistical modeling.
Full text first
Audit the numbers in the full text, not the abstract.
- When the runtime can run scripts, get the PDF with
dental-paper-fetchbefore auditing. It uses legal open-access sources only. - Never audit from the abstract when a free full text exists. The tables, the SDs, the n analyzed and the missing-data counts are in the full text.
- Otherwise ask the user for a PDF they may lawfully share.
- When only an abstract, an excerpt or a set of pasted numbers is available, say so and complete the supported parts. Record what was read in the Source text line of Step 1.
What ships with it
6 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.
- 29d ago Changed · +18 lines 5254ea63dfaa
- 2mo ago First seen · 250 lines · 97 tokens per session scan A ac1f05531aae
dental-statistical-forensics is a skill published in the GitHub repository Tuminha/dental-ai-skills (7 stars, last pushed 7d ago), licensed MIT. It adds 97 tokens to every session and 2,339 once invoked, about $0.0004 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 2026-08-31.
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