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-author-disclosuresgit 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-author-disclosures)<a href="https://agentmods.dev/skills/tuminha/dental-ai-skills/dental-author-disclosures"><img src="https://agentmods.dev/badge/skills/tuminha/dental-ai-skills/dental-author-disclosures/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-author-disclosures"><img src="https://agentmods.dev/badge/skills/tuminha/dental-ai-skills/dental-author-disclosures.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 | $0.00059 | $0.02114 |
| Opus 5.5 | $0.00024 | $0.00846 |
| Sonnet 5.5 | $0.00012 | $0.00423 |
| Haiku 4.5 | $0.00006 | $0.00211 |
Grade A, and why
dental-author-disclosures 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dental Author Disclosures
Skill protocol version: 2026.09.30 Methodology Review Date: 2026-09-30 (added step 0 on the reviewer's own relationships, "Sharing, sources and retention", the expanded source standard with the ICMJE 36-month window, and the register hand-off as a report section table; earlier the same day "How relationships inform appraisal" and "Full text first"; previous review 2026-09-27)
Scope
Document relationships, not a blacklist or a credibility score. A relationship can be relevant to interpretation without proving biased research. Never infer payment, commercial sponsorship, or misconduct from a name, conference appearance, membership, coauthorship, or missing search result. Do not automatically reduce a paper's score.
Full text first
The funding and disclosure statements are in the full text. An abstract rarely has them.
When the runtime can run scripts, get the PDF with dental-paper-fetch before building
the register. Never build the register from the abstract when a free full text exists.
Otherwise ask the user for a PDF they may lawfully share. State in the search scope
whether the full text, part of it or only the abstract was read. An author address that
dental-paper-fetch prints is for a full-text request only and never enters the register.
Procedure
- State the reviewer's own relationships to the studied products and organisations before starting, for example "founder of a dental education site; no relationship with the manufacturer studied". Write "none" when there are none. The reviewer is the person the register is for: ask them when the statement is not known, and put it at the top of the register.
- Establish the paper: title, DOI, publication and study dates, manufacturer/product studied, author names, affiliations, and ORCID where available. Disambiguate each person using at least two matching details. Leave ambiguous identities unresolved.
- State retrieval capability and coverage. If browsing or full text is unavailable, extract from supplied documents only and provide a search plan. Never pretend a search was performed. An abstract rarely contains complete disclosure information.
- Read the article's funding and disclosure statements, supplements, protocol and registration first. Preserve the distinction between institution and individual.
- Search public primary sources: author/university disclosures, funder/grant records, company advisory or speaker pages, dated official meeting programmes, and patent records. Search every author, but report which were actually checked and where a bounded search stopped. Secondary reporting is a lead, not proof of a relationship.
- Record study-period evidence separately from current relationships. Use the journal's stated disclosure period. If the journal uses the ICMJE disclosure form, the window is the 36 months before submission for every item other than support for the submitted work, which has no time limit. If the period is unknown, state the searched dates without inventing a universal disclosure obligation. A newer relationship is not evidence that it existed during the study. A now-missing webpage does not prove it ended.
- Classify: employment; consultancy/advisory; speaking/honoraria; research funding; travel/material support; patents/equity; non-financial/professional affiliation; other/unclear. Paid status remains unknown unless the source establishes it. Professional societies and educational foundations are not automatically vendors.
- Compare each source with the paper's disclosure. Say "not found in the supplied disclosure" rather than "concealed". Explain the possible connection to the studied intervention, sponsor role, design, analysis or reporting; do not infer actual bias.
- Keep conflicting sources and identity uncertainties visible. Hand off methodological
concerns to
research-critic; body-of-evidence questions toclinical-evidence-reviewer.
What ships with it
1 file 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 First seen · 146 lines · 59 tokens per session scan A 9e9453f38cfd
dental-author-disclosures is a skill published in the GitHub repository Tuminha/dental-ai-skills (7 stars, last pushed 7d ago), licensed MIT. It adds 59 tokens to every session and 2,114 once invoked, about $0.0002 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 2026-10-01.
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