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 usathyan/epistract --skill clinicaltrialsgit clone --depth 1 https://github.com/usathyan/epistractWrote 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/usathyan/epistract/clinicaltrials)<a href="https://agentmods.dev/skills/usathyan/epistract/clinicaltrials"><img src="https://agentmods.dev/badge/skills/usathyan/epistract/clinicaltrials/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/usathyan/epistract/clinicaltrials"><img src="https://agentmods.dev/badge/skills/usathyan/epistract/clinicaltrials.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 | $0.00083 | $0.04249 |
| Opus 5 | $0.00042 | $0.02124 |
| Sonnet 5 | $0.00017 | $0.00850 |
| Haiku 4.5 | $0.00008 | $0.00425 |
Grade A, and why
clinicaltrials-extraction 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 10d 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 — 530 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clinical Trials Extraction Skill
NCT ID Capture
For any Trial entity, the name field MUST be the NCT identifier
(format: NCT followed by 8 digits, e.g. NCT04303780) when one is
present in the document. Study acronyms ("CodeBreaK 200"), sponsor-assigned
codes ("AMG 510 study"), and descriptive titles ("Phase 3 study of
sotorasib in NSCLC") go into attributes.trial_acronym or
attributes.descriptive_title — not into name.
If no NCT ID is present, fall back to the study acronym. Only use a
descriptive title as name as a last resort, and set confidence <= 0.6.
This rule exists because the post-build enrichment step resolves Trial nodes against ClinicalTrials.gov via their NCT ID. Nodes without an NCT ID cannot be enriched.
Output Format
Return a DocumentExtraction JSON envelope. The document_id field is
REQUIRED. All entities and relations are lists of objects.
{
"document_id": "nct04303780_protocol",
"entities": [
{
"name": "NCT04303780",
"entity_type": "Trial",
"confidence": 0.95,
"context": "Phase 3 CodeBreaK 200 trial evaluating sotorasib vs docetaxel",
"attributes": {
"trial_acronym": "CodeBreaK 200",
"study_type": "Interventional"
}
},
{
"name": "sotorasib",
"entity_type": "Compound",
"confidence": 0.95,
"context": "AMG 510; KRAS G12C inhibitor evaluated as experimental arm",
"attributes": {
"inn": "sotorasib",
"sponsor_code": "AMG 510"
}
}
],
"relations": [
{
"source_entity": "NCT04303780",
"target_entity": "sotorasib",
"relation_type": "tests",
"confidence": 0.95,
"evidence": "Trial NCT04303780 evaluates sotorasib in the experimental arm"
}
]
}
Field Requirements
Entity fields
name(required): Canonical identifier. For Trial, use NCT ID. For Compound, use INN. For Condition, use MeSH term.entity_type(required): Must match one of the 12 types in domain.yaml exactly (case-sensitive).confidence(required): Float 0.0–1.0. See Confidence Calibration section.context(required): Short excerpt or paraphrase from the source document justifying extraction.attributes(optional): Dict of domain-specific metadata (e.g.trial_acronym,sponsor_code,phase,dose).
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.
- 10d ago First seen · 530 lines · 83 tokens per session scan A 102ea2c12e9d
clinicaltrials-extraction is a skill published in the GitHub repository usathyan/epistract (8 stars, last pushed 25d ago), licensed MIT. It adds 83 tokens to every session and 4,249 once invoked, about $0.0004 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 2026-08-31.
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