Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add usathyan/epistract/plugin install 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/commands/usathyan/epistract/epistemic)<a href="https://agentmods.dev/commands/usathyan/epistract/epistemic"><img src="https://agentmods.dev/badge/commands/usathyan/epistract/epistemic.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 5.1 | $0.00034 | $0.01166 |
| Opus 5 | $0.00017 | $0.00583 |
| Sonnet 5 | $0.00007 | $0.00233 |
| Haiku 4.5 | $0.00003 | $0.00117 |
Grade A, and why
epistract-epistemic 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 6d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Epistract Epistemic Analysis
Analyze a built knowledge graph for epistemic patterns — hypotheses, contradictions, conditional claims, negative results, and patent vs. paper epistemic signatures. Produces a claims_layer.json overlay complementing the existing community structure.
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:epistemic <output-dir>
Required:
<output-dir> Path to directory containing graph_data.json from a prior /epistract:ingest or /epistract:build run
Examples:
/epistract:epistemic ./epistract-output
/epistract:epistemic ./clinical-output
Arguments
Arguments
path(required): Output directory containinggraph_data.json(from a prior/epistract-ingestor/epistract-build)--no-narrate(optional): Skip the LLM analyst narrator and produce only the deterministic rule output. Use for offline runs, when no API key is available, or when you want byte-identical repeat runs.
What This Does
This command runs after the graph is built (after /epistract-ingest or /epistract-build). It reads the assembled graph_data.json — where cross-document evidence is merged — and identifies five categories of epistemic facts:
- Hypothesized mechanisms — clusters of hedged relations forming a conjecture
- Conflicting evidence — same relation with opposing evidence from different sources
- Patent vs. paper signatures — prophetic/claimed vs. empirically demonstrated
- Temporal state transitions — evolving truth (pre-clinical → approved)
- Negative results — high-confidence statements about absence
After the rule-based analysis, an automatic analyst narrator reads the domain persona (from domains/<name>/workbench/template.yaml:persona) plus the fresh claims_layer.json and produces epistemic_narrative.md — a structured briefing an analyst would write: prophetic claim landscape grouped by topic, contested claims with source IDs, coverage gaps, recommended follow-ups. The narrator is ADDITIVE — any failure (no API key, API error, domain has no persona) leaves claims_layer.json on disk as the authoritative rule output and prints a clear note to stderr.
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.
- 6d ago First seen · 84 lines · 34 tokens per session scan A 548bb4dd3689
epistract-epistemic is a command published in the GitHub repository usathyan/epistract (8 stars, last pushed 21d ago), licensed MIT. It adds 34 tokens to every session and 1,166 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.