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 Aperivue/medsci-skills --skill fill-icmje-coigit clone --depth 1 https://github.com/Aperivue/medsci-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/aperivue/medsci-skills/fill-icmje-coi)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/fill-icmje-coi"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/fill-icmje-coi/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/aperivue/medsci-skills/fill-icmje-coi"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/fill-icmje-coi.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00122 | $0.02470 |
| Opus 5 | $0.00061 | $0.01235 |
| Sonnet 5 | $0.00024 | $0.00494 |
| Haiku 4.5 | $0.00012 | $0.00247 |
Grade A, and why
fill-icmje-coi 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 13d 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fill-ICMJE-COI Skill
You are helping a researcher prepare ICMJE Conflict of Interest Disclosure Forms
for every co-author on a manuscript about to be submitted to an ICMJE member
journal (CHEST, NEJM, JAMA, Lancet, Radiology, etc.). This skill batch-generates
one personalized .docx per author from a synthetic all-None seed shipped with
the skill, avoiding 10–20 minutes of repetitive Word clicking per author.
Why This Skill Exists
The official ICMJE coi_disclosure.docx puts every field inside Word Content
Controls (Structured Document Tags, a.k.a. SDTs). Naive python-docx
manipulation of cell.text silently ignores SDT content, so the straightforward
programmatic approach does not work. The historical workaround was to open the
template in Word and manually fill each author's form (21 authors × 13
checkboxes × 2 clicks = ~500 clicks). This skill replaces that by operating
directly on word/document.xml inside the docx zip and doing literal-string
replacement — but that requires the target strings to already exist in the
seed, so the skill ships a pre-filled synthetic seed.
Effect: a full author roster auto-fills in seconds from the synthetic seed, with zero Word clicks.
Core Principles (Do Not Violate)
- Never author SDT XML from scratch. Only replace existing strings in an already-populated seed. Creating Content Controls programmatically is fragile and Word-version-dependent.
- Never ship a real author's filled form as the seed. The template
directory contains
icmje_coi_seed_synthetic.docxwith all PII scrubbed (synthetic name, title, date; metadata reset toICMJE/Anonymous). Real-person seeds leak PII through both document.xml and docProps. - Never modify the 13 disclosure items or certification checkbox. The script only replaces Date/Name/Title. If any author has a real disclosure, they must edit in Word manually — the skill's purpose is the common all-None case.
- Always verify before circulation. Each output must have 14 × ☒ and 13 × "None" in document.xml. The script runs this check implicitly by preserving the seed structure; a post-generation grep is cheap insurance.
What ships with it
4 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.
- 13d ago First seen · 217 lines · 122 tokens per session scan A 8aa0c34d18e1
fill-icmje-coi is a skill published in the GitHub repository Aperivue/medsci-skills (292 stars, last pushed 5d ago), licensed MIT. It adds 122 tokens to every session and 2,470 once invoked, about $0.0006 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 2026-08-30.
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