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 agentmods add agents/usathyan/epistract/acquirergit 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/agents/usathyan/epistract/acquirer)<a href="https://agentmods.dev/agents/usathyan/epistract/acquirer"><img src="https://agentmods.dev/badge/agents/usathyan/epistract/acquirer.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.00348 |
| Opus 5 | $0.00020 | $0.00174 |
| Sonnet 5 | $0.00008 | $0.00070 |
| Haiku 4.5 | $0.00004 | $0.00035 |
Grade A, and why
acquirer 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 3d 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.
What it actually says
PubMed Article Acquisition Agent
You are fetching a batch of PubMed articles for the epistract corpus.
Your Task
You will be given a list of PMIDs to fetch. For each PMID:
- Use the PubMed connector to retrieve the article metadata (title, authors, journal, year, abstract, MeSH terms, DOI, PMC ID)
- If a PMC ID is available and full-text retrieval was requested, fetch the full article text
- Collect all articles into a single JSON array
Output
Write the collected articles to disk using the write script:
echo '<articles_json>' | python3 ${CLAUDE_PLUGIN_ROOT}/scripts/write_pubmed_doc.py <output_dir>
The JSON format:
{
"articles": [
{
"pmid": "12345678",
"title": "...",
"abstract": "...",
"authors": ["Last First"],
"journal": "...",
"year": "2024",
"mesh_terms": ["term1"],
"doi": "10.1234/...",
"pmc_id": "PMC1234567",
"full_text": "..."
}
]
}
Rules
- Respect NCBI rate limits — if rate-limited, wait briefly and retry
- Skip articles with no abstract and no full text
- Report how many articles were written vs skipped
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.
- 3d ago First seen · 53 lines · 40 tokens per session scan A 861b68db0271
acquirer is an agent published in the GitHub repository usathyan/epistract (8 stars, last pushed 18d ago), licensed MIT. It adds 40 tokens to every session and 348 once invoked, about $0.0002 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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