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 commands/usathyan/epistract/querygit clone --depth 1 https://github.com/usathyan/epistractWhat 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 | $0.00013 | $0.00422 |
| Opus 5 | $0.00006 | $0.00211 |
| Sonnet 5 | $0.00003 | $0.00084 |
| Haiku 4.5 | $0.00001 | $0.00042 |
Grade A, and why
epistract-query 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 2d 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
Search the knowledge graph for entities by name.
Arguments:
query-- search term--type-- filter by entity type (COMPOUND, GENE, PROTEIN, etc.)output_dir-- directory with graph_data.json (default: ./epistract-output)
python3 ${CLAUDE_PLUGIN_ROOT}/core/run_sift.py search <output_dir> <query> [--type TYPE]
Present results as a formatted table with entity name, type, and confidence.
Usage Guard
If invoked with no arguments or with --help: Display the following usage block verbatim and stop — do not run any pipeline steps.
Usage: /epistract:query <search-term> [options]
Required:
<search-term> Entity name or keyword to search for in the knowledge graph
Options:
--type <entity-type> Filter results by entity type
(Trial, Compound, Condition, Sponsor, Endpoint, Arm, Biomarker,
Gene, Protein, Drug, Disease, Party, Obligation, etc.)
<output-dir> Path to extraction output directory (default: ./epistract-output)
Examples:
/epistract:query "remdesivir"
/epistract:query "COVID-19" --type Condition
/epistract:query "NCT04280705" --type Trial
Arguments:
query-- search term--type-- filter by entity type (COMPOUND, GENE, PROTEIN, etc.)output_dir-- directory with graph_data.json (default: ./epistract-output)
python3 ${CLAUDE_PLUGIN_ROOT}/core/run_sift.py search <output_dir> <query> [--type TYPE]
Present results as a formatted table with entity name, type, and confidence.
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.
- 2d ago First seen · 52 lines · 13 tokens per session scan A 1a7f8b3b483d
epistract-query is a command published in the GitHub repository usathyan/epistract (8 stars, last pushed 17d ago), licensed MIT. It adds 13 tokens to every session and 422 once invoked, about $0.0001 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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