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 | $0.00063 | $0.01518 |
| Opus 5.5 | $0.00025 | $0.00607 |
| Sonnet 5.5 | $0.00013 | $0.00304 |
| Haiku 4.5 | $0.00006 | $0.00152 |
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 24d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fill-ICMJE-COI Skill
The official ICMJE coi_disclosure.docx keeps every field inside Word Content Controls (SDTs),
which python-docx cell.text silently ignores. The script therefore does literal-string
replacement in word/document.xml, which only works when the target strings already exist — hence
the shipped pre-filled synthetic seed.
Core Principles (Do Not Violate)
- Never author SDT XML from scratch. Only replace existing strings in an already-populated seed, because programmatic Content Controls are fragile and Word-version-dependent. If an error seems to require altering the seed structure, stop and escalate to the user.
- Never use or commit a seed that contains a real author's data. The shipped
templates/icmje_coi_seed_synthetic.docxis PII-scrubbed (synthetic name, title, date; metadataICMJE/Anonymous); real-person seeds leak PII through bothdocument.xmlanddocProps. Keep custom seeds with real names in private per-project directories, and before promoting any seed checkunzip -p seed.docx docProps/core.xmlfor real names. - Never modify the 13 disclosure items or the certification checkbox. The script replaces only Date, Name and Title; the ☒ None entries come from the seed unchanged. Never say the script "handled the disclosures": it cloned the seed, and no author-specific disclosure reasoning happened. An author with a real disclosure edits their own form in Word.
- The seed must be pre-filled (all-None ☒ + text). The script does not work on a blank ICMJE template.
Skip this skill when the target journal uses its own declaration form (Elsevier Declaration of Interest, a BMJ ICMJE derivative, etc.) rather than the canonical ICMJE form — check the author guidelines first.
Execution
Phase 1 — Intake
Ask the user (or extract from the conversation):
- Manuscript title (exact, as on the title page)
- Submission date (e.g., "April 20, 2026")
- Author list — ordered, one name per slot:
[(1, "Author One"), (2, "Author Two"), ...], taken verbatim from the manuscript title page or the user's list - Output directory — typically
submission/{journal}/icmje_forms/
What ships with it
5 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.
- 24d ago Changed · +10 lines a119ef0a0e5c
- 26d ago Changed · -111 lines · -59 tokens per session 702cd669f0dd
- 1mo 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 (352 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 1,518 once invoked, about $0.0003 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 2026-08-30.
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