dental-statistical-forensics

dental-statistical-forensics is a skill for Claude Code, Codex from Tuminha/dental-ai-skills. It costs 97 tokens per session (2,054 once invoked), scanned A, original, MIT.

A dental research review that checks whether a study's numerical results support its conclusions. It examines measures such as effect sizes, confidence intervals, missing data, and patient-level variation.

In plain words
What is it for?
Use it to audit dental studies, abstracts, systematic reviews, or evidence tables. It checks statistical methods, data quality, clinical thresholds, and the meaning of reported numbers.
Why use it?
It helps reveal when an average result looks favorable but may be uncertain, clinically unimportant, or unreliable for individual patients.

Skill for Claude CodeCodex

Written for Claude Code and Codex: when-to-use in frontmatter, but also agents/openai.yaml present.

Good fit Use it to audit dental studies, abstracts, systematic reviews, or evidence tables. It checks statistical methods, data quality, clinical thresholds, and the meaning of reported numbers.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tuminha/dental-ai-skills/dental-statistical-forensics
Install

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.

Any agent
npx skills add Tuminha/dental-ai-skills --skill dental-statistical-forensics
Clone the repo
git clone --depth 1 https://github.com/Tuminha/dental-ai-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for dental-statistical-forensics

README.md
[![agentmods](https://agentmods.dev/badge/skills/tuminha/dental-ai-skills/dental-statistical-forensics/github.svg)](https://agentmods.dev/skills/tuminha/dental-ai-skills/dental-statistical-forensics)
Your own site
<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.

agentmods 80×15 button for dental-statistical-forensics

Your own site · 80×15
<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>
Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,054 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00097 $0.02054
Opus 5 $0.00048 $0.01027
Sonnet 5 $0.00019 $0.00411
Haiku 4.5 $0.00010 $0.00205

Measured 12d ago against content hash ac1f05531aae, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/stats_forensics_calculator.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

dental-statistical-forensics/SKILL.md · 250 lines

How it starts

The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dental Statistical Forensics

Skill protocol version: 2026.05.16

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.md when the outcome type or effect measure is unclear.
  • Use references/dental-domain-modules.md for domain-specific checks.
  • Use references/clinical-thresholds-and-mcid.md when 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.


Mandatory Workflow

Step 1: Data Extraction Status

Before judging, state what numerical data are available and what is missing.

Extract:

  • Outcomes and time points.
  • Group sizes and analysis sample sizes.
  • Unit of randomization, unit of measurement, and unit of analysis.
  • Effect estimates.
  • SD/IQR/range/distribution information.
  • CI/SE/p-values.
  • Missing data and reasons.
  • Measurement reliability/error when reported.

Read the full file on GitHub · 250 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 250 lines · 97 tokens per session scan A ac1f05531aae

Subscribe to this mod's changes

dental-statistical-forensics is a skill published in the GitHub repository Tuminha/dental-ai-skills (6 stars, last pushed 3mo ago), licensed MIT. It adds 97 tokens to every session and 2,054 once invoked, about $0.0005 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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