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 fmschulz/omics-skills --skill csag-extractiongit clone --depth 1 https://github.com/fmschulz/omics-skillsWrote 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/fmschulz/omics-skills/csag-extraction)<a href="https://agentmods.dev/skills/fmschulz/omics-skills/csag-extraction"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/csag-extraction/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/fmschulz/omics-skills/csag-extraction"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/csag-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.02942 |
| Opus 5.5 | $0.00015 | $0.01177 |
| Sonnet 5.5 | $0.00008 | $0.00588 |
| Haiku 4.5 | $0.00004 | $0.00294 |
Grade A, and why
csag-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 1mo 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CSAG extraction skill
Goal
Convert a manuscript into a CSAG PaperExtraction instance that is:
- Schema-valid (LinkML:
assets/csag.yaml) - Evidence-grounded (TextSpans for key objects)
- Canonical (support/refute only via
EvidenceLink, chains viaInferenceStep) - Conditional (every Assertion has ≥1 Context)
Files in this skill
assets/csag.yaml— authoritative schemaassets/csag_qa_templates.yaml— QA template catalogreferences/CSAG_PLAYBOOK.md— detailed extraction guide + edge cases
Quick Reference
| Task | Action |
|---|---|
| Extract paper | Build one PaperExtraction per manuscript, not per search hit. |
| Ground claims | Attach important assertions, evidence, and links to TextSpans. |
| Validate schema | Run scripts/validate_paper_extraction.py before finalizing. |
| Review quality | Run scripts/csag_quality_report.py --strict and resolve issues. |
Non‑negotiable invariants
- Every Assertion MUST have ≥1 Context (schema-enforced).
- Support/refute polarity ONLY in
EvidenceLink. - Contradictions/qualification ONLY in
AssertionRelation. - Reasoning chains ONLY as
InferenceStep. - Ground important objects to TextSpans.
- Every Assertion MUST have
normalization_status:raw/partially_normalized/fully_normalized
Extraction scope
The extraction scope is the full manuscript: title, abstract, introduction, methods, results, discussion, conclusion, and supplementary material when available. If retrieval was driven by topic terms (organisms, genes, methods), do not restrict extracted assertions or evidence to sentences that mention those terms — the retrieval scope is not the extraction scope.
Instructions
Reliability pattern for model-assisted extraction
When a model is used to assist extraction, prefer a two-step workflow:
- Draft the scientific content first: claims, evidence snippets, evidence polarity, inferences, critiques, gaps, artifacts, datasets, and exact source quotes.
- Let tooling assemble the final
PaperExtraction: deterministic IDs, reference fields, enum normalization, offset lookup fromexact_text, and validator repair for mechanical schema-shape issues.
What ships with it
6 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.
- 1mo ago First seen · 264 lines · 38 tokens per session scan A ff4e2140cfb6
csag-extraction is a skill published in the GitHub repository fmschulz/omics-skills (11 stars, last pushed 23d ago), licensed MIT. It adds 38 tokens to every session and 2,942 once invoked, about $0.0002 per session on Opus 5.5. 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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