OpenMed is local-first healthcare AI software that extracts clinical information and removes personally identifying details from clinical text on hardware controlled by the user. Healthcare developers use its Python runtime, Apple Silicon and mobile SDKs, and browser support for on-device clinical NER and PII de-identification.
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 maziyarpanahi/openmed --skill detecting-pv-signalsgit clone --depth 1 https://github.com/maziyarpanahi/openmedWrote 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/maziyarpanahi/openmed/detecting-pv-signals)<a href="https://agentmods.dev/skills/maziyarpanahi/openmed/detecting-pv-signals"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/detecting-pv-signals/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/maziyarpanahi/openmed/detecting-pv-signals"><img src="https://agentmods.dev/badge/skills/maziyarpanahi/openmed/detecting-pv-signals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 54 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 62 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00218 | $0.02716 |
| Opus 5 | $0.00109 | $0.01358 |
| Sonnet 5 | $0.00044 | $0.00543 |
| Haiku 4.5 | $0.00022 | $0.00272 |
Grade A, and why
detecting-pv-signals scanned grade A with 1 finding 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.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
r = requests.get(BASE, params=params, timeout=30) How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detecting pharmacovigilance signals (disproportionality)
Spontaneous-report databases like the FDA's FAERS are mined for signals of disproportionate reporting (SDR): drug-reaction pairs that occur together more than expected given the background of all reports. The core device is a 2x2 contingency table and a disproportionality metric computed from it — PRR, ROR, EBGM, or IC (BCPNN).
You can build the 2x2 table directly from the public, free OpenFDA
/drug/event endpoint (no PHI, no MedDRA license to query; the reaction terms
returned are already MedDRA PTs). This skill is statistical screening: a high
PRR is a hypothesis, not a confirmed adverse drug reaction.
When to use
- You have a drug of interest and want to see which reactions are over-reported.
- You need a PRR / ROR with confidence interval, or an Empirical Bayes EBGM/EB05 / IC025 to control for the small-count noise PRR/ROR suffer from.
- You are building a routine signal-screening run over OpenFDA or your own aggregated case counts.
The 2x2 table
For one drug D and one reaction R, classify every report:
| Reaction R | Not R | |
|---|---|---|
| Drug D | a | b |
| Not D | c | d |
- PRR = [a/(a+b)] / [c/(c+d)]
- ROR = (a·d)/(b·c)
- IC (BCPNN, log2 information component) ≈ log2( a·(a+b+c+d) / ((a+b)·(a+c)) )
- EBGM = Empirical Bayes Geometric Mean — a gamma-Poisson shrinkage of the observed/expected ratio (the MGPS method) that pulls small-count estimates toward 1; report EB05 (the 5th percentile) as the conservative signal.
Common signal thresholds (screening only): PRR ≥ 2 with χ² ≥ 4 and a ≥ 3; ROR lower 95% CI > 1; IC025 > 0; EB05 ≥ 2.
Quick start (real OpenFDA count queries)
Base endpoint: https://api.fda.gov/drug/event.json. No key needed to try it
(240 req/min, 1,000/day per IP; with a free api_key= key: 240/min,
120,000/day). The count=<field>.exact parameter returns a terms histogram, and
search= with +AND+ filters the population — that is all you need for a 2x2.
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.
- 12d ago First seen · 190 lines · 218 tokens per session scan A e8728da5acfe
detecting-pv-signals is a skill published in the GitHub repository maziyarpanahi/openmed (5,290 stars, last pushed yesterday), licensed Apache-2.0. It adds 218 tokens to every session and 2,716 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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