dental-evidence-report-artifact

dental-evidence-report-artifact is a skill for Claude Code, Codex from Tuminha/dental-ai-skills. It costs 74 tokens per session (815 once invoked), scanned A, original, MIT.

A report-formatting skill for completed dental evidence reviews, paper critiques, retrieval logs, or statistical audits. It turns existing analysis into a polished HTML or PDF-ready report without performing the scientific review itself.

In plain words
What is it for?
Use it to prepare dental journal-club reports, teaching materials, shareable reports, and other presentation artifacts from finished analysis.
Why use it?
It makes completed evidence work easier to read and share while preserving citations, uncertainty, limitations, and extracted numbers. It does not add new evidence or strengthen the conclusions.

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 prepare dental journal-club reports, teaching materials, shareable reports, and other presentation artifacts from finished analysis.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tuminha/dental-ai-skills/dental-evidence-report-artifact
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-evidence-report-artifact
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-evidence-report-artifact

README.md
[![agentmods](https://agentmods.dev/badge/skills/tuminha/dental-ai-skills/dental-evidence-report-artifact/github.svg)](https://agentmods.dev/skills/tuminha/dental-ai-skills/dental-evidence-report-artifact)
Your own site
<a href="https://agentmods.dev/skills/tuminha/dental-ai-skills/dental-evidence-report-artifact"><img src="https://agentmods.dev/badge/skills/tuminha/dental-ai-skills/dental-evidence-report-artifact/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-evidence-report-artifact

Your own site · 80×15
<a href="https://agentmods.dev/skills/tuminha/dental-ai-skills/dental-evidence-report-artifact"><img src="https://agentmods.dev/badge/skills/tuminha/dental-ai-skills/dental-evidence-report-artifact.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 815 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 $0.00074 $0.00815
Opus 5 $0.00037 $0.00407
Sonnet 5 $0.00015 $0.00163
Haiku 4.5 $0.00007 $0.00081

Measured 12d ago against content hash bd83066356dd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

dental-evidence-report-artifact 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/render_evidence_report.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-evidence-report-artifact/SKILL.md · 82 lines

How it starts

The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dental Evidence Report Artifact

Skill protocol version: 2026.05.16

Identity

You are a dental evidence report designer. Your job is to convert already-completed evidence analysis into a clear, polished, standalone report. You do not perform the scientific critique yourself. You preserve the source analysis, uncertainty labels, citations, and limitations.

Scope

Use this skill after one or more of these skills have produced analysis:

  • research-critic
  • clinical-evidence-reviewer
  • dental-evidence-retriever
  • dental-statistical-forensics

If the user has not yet produced the analysis, route to the correct analysis skill first.

Non-Negotiables

  • Do not create new evidence claims.
  • Do not invent citations, PMIDs, DOIs, guideline statements, effect sizes, CIs, or p-values.
  • Preserve uncertainty labels and limitations.
  • Separate analysis from presentation: the artifact makes the report easier to read; it does not strengthen the evidence.
  • If a chart is included, it must be directly traceable to extracted numbers in the source analysis.

Optional Rendering Helper

Use scripts/render_evidence_report.py when a JSON report payload is available. The script renders a standalone HTML file using the bundled template in assets/evidence-report-template.html.

Minimum JSON shape:

{
  "title": "Report title",
  "subtitle": "Optional subtitle",
  "verdict": "One-sentence bottom line",
  "severity": "moderate",
  "metrics": [{"label": "Outcome", "value": "-1.2 ± 0.9 mm", "note": "RP group"}],
  "flags": [{"severity": "moderate", "title": "High dispersion", "body": "SD limits individual predictability."}],
  "sections": [{"heading": "Clinical Interpretation", "body": "Markdown-lite text."}],
  "citations": [{"label": "Iasella 2003", "detail": "User-provided PDF"}]
}

Report Structure

Every artifact should contain:

  1. Title block — paper/question, report type, date.
  2. Verdict card — short bottom line with severity.
  3. Evidence status — whether live retrieval was performed or sources were user-provided.
  4. Key metrics — clinically important numbers only.
  5. Major flags — critical/moderate/minor findings.
  6. Interpretation — what the numbers mean clinically.
  7. Limitations — missing data, no-network caveats, verification gaps.
  8. Sources — citations or user-provided source list.

Read the full file on GitHub · 82 lines

Files

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.

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 · 82 lines · 74 tokens per session scan A bd83066356dd

Subscribe to this mod's changes

dental-evidence-report-artifact is a skill published in the GitHub repository Tuminha/dental-ai-skills (6 stars, last pushed 3mo ago), licensed MIT. It adds 74 tokens to every session and 815 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

pydicom

Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.

K-Dense-AI/scientific-agent-skills · 56 tokens

pptx-posters

Create and audit editable scientific posters in macro-free PowerPoint (.pptx) from author-approved local content and assets. Use when the requested deliverable is a PowerPoint research/conference poster and exact physical, printer, accessibility, provenance, and package-security checks are required.

K-Dense-AI/scientific-agent-skills · 59 tokens

synthesize

Thesis report synthesis — turn a thesis's markdown artifacts into its letter-size thesis-report.html, then OPTIMIZE it against real page renders. Four-step loop — pack (deterministic Python bundle of all sources + report/metrics.json), author (the LLM writes report/content.html — narrative, KPI tiles, badges…

agentii-ai/agentii-investment-intelligence · 134 tokens

LaTeX工具

A tool for creating, compiling, and checking mathematical modelling papers written in LaTeX, a document system often used for technical writing.

XiaoMaColtAI/math-modeling-skill · 46 tokens

thesis-consistency-audit

A consistency audit for quantitative master’s and doctoral theses in management, finance, or strategy. It checks whether numbers, tables, analyses, and claims agree, and can inspect hidden author information in office documents.

Nero1688/claude-academic-skills · 500 tokens

tikz-figure-review

Review and fix alignment, label collision, clipping, legend-over-data, overlap, and layout issues in TikZ and pgfplots figures inside LaTeX documents. Use when the user wants to review figures in a paper, tutorial, lecture notes, or thesis before submission; when a reviewer flags figure problems; when a rendered…

shubham0704/claude-skills · 177 tokens