Medical Research Agent Skills is a library of agent instructions for medical and biomedical research, covering evidence analysis, study protocol design, data analysis, and academic writing. Researchers use it to guide compatible coding agents through common scientific workflows. The catalogue contains many of the library's skills and commands.
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 aipoch/medical-research-skills --skill adverse-event-narrativegit clone --depth 1 https://github.com/aipoch/medical-research-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/aipoch/medical-research-skills/adverse-event-narrative)<a href="https://agentmods.dev/skills/aipoch/medical-research-skills/adverse-event-narrative"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/adverse-event-narrative/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/aipoch/medical-research-skills/adverse-event-narrative"><img src="https://agentmods.dev/badge/skills/aipoch/medical-research-skills/adverse-event-narrative.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 | $0.00057 | $0.02724 |
| Opus 5 | $0.00028 | $0.01362 |
| Sonnet 5 | $0.00011 | $0.00545 |
| Haiku 4.5 | $0.00006 | $0.00272 |
Grade A, and why
adverse-event-narrative 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 9d 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 — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adverse Event Narrative Generator
Quick Check
Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
Audit-Ready Commands
Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --help
python scripts/main.py --validate-only --help
Workflow
- Collect case data: Receive adverse event case data including: patient demographics, medical history, concomitant medications, suspect drug(s) with dosing, adverse event description, diagnostic results, treatment, dechallenge/rechallenge dates, outcome, and causality assessment.
- Validate completeness: Check that required CIOMS I fields are present (case ID, patient age/sex, suspect drug, AE with MedDRA PT, dates). Flag missing fields.
- Checkpoint: Display case summary and list of missing fields to user. Confirm whether to proceed with partial data or wait for complete information.
- Reconstruct timeline: Analyze temporal relationships: time to onset, dechallenge response, rechallenge response, temporal plausibility with known drug profile.
- Generate narrative: Compose CIOMS I-compliant narrative in all 10 standard sections (demographics → causality assessment). Use objective, factual language; reserve opinion for causality section only.
- Checkpoint: User reviews draft narrative for clinical accuracy and verifies that no patient identifiers remain.
- Format output: Generate in requested format (CIOMS I, ICH E2B R3, FDA MedWatch 3500A). Apply MedDRA coding.
- Fallback: If case data is too incomplete for narrative generation, output a structured checklist of required fields with example entries and a partial narrative for completed sections only.
What ships with it
9 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.
- eval_report_adverse-event-narrative_result.json 11 KB
- POLISH_CHANGELOG.md 619 B
- references/CIOMS_I_Guidelines.md 3.2 KB
- references/ICSR_Template.md 2.4 KB
- references/MedDRA_Reference.md 2.3 KB
- references/Quick_Reference.md 1.4 KB
- references/sample_case_001.json 2.9 KB
- references/sample_case_minimal.json 599 B
- scripts/main.py 12 KB runs code
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.
- 9d ago First seen · 304 lines · 57 tokens per session scan A 92654c52bd89
adverse-event-narrative is a skill published in the GitHub repository aipoch/medical-research-skills (1,860 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 2,724 once invoked, about $0.0003 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 2026-09-03.
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