Csag extraction

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 via InferenceStep)
• Conditional (every Assertion has ≥1 Context)

Files in this skill

• assets/csag.yaml — authoritative schema
• assets/csag_qa_templates.yaml — QA template catalog
• references/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

1. Every Assertion MUST have ≥1 Context (schema-enforced).
2. Support/refute polarity ONLY in EvidenceLink.
3. Contradictions/qualification ONLY in AssertionRelation.
4. Reasoning chains ONLY as InferenceStep.
5. Ground important objects to TextSpans.
6. 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:

1. Draft the scientific content first: claims, evidence snippets, evidence polarity, inferences, critiques, gaps, artifacts, datasets, and exact source quotes.
2. Let tooling assemble the final PaperExtraction: deterministic IDs, reference fields, enum normalization, offset lookup from exact_text, and validator repair for mechanical schema-shape issues.

Do not rely on a model to perform all final bookkeeping in one pass. The most common failures are missing IDs, dangling references, scalar values where lists are required, field aliases such as evidence_id instead of evidence_item, wrong enum labels, and missing artifact/dataset metadata.

Use the validator as a feedback loop:

uv run --script skills/csag-extraction/scripts/validate_paper_extraction.py \
  work/STEM/paper_extraction.json \
  --source-markdown work/STEM/STEM.md \
  --article-json work/STEM/STEM.article.json \
  --report-out work/STEM/paper_extraction.validation.json

For mechanical cleanup before curation, write a repaired candidate and inspect the repair_actions in the validation report:

uv run --script skills/csag-extraction/scripts/validate_paper_extraction.py \
  work/STEM/paper_extraction.raw.json \
  --source-markdown work/STEM/STEM.md \
  --article-json work/STEM/STEM.article.json \
  --repair-out work/STEM/paper_extraction.repaired.json \
  --report-out work/STEM/paper_extraction.validation.json

Repairs are intentionally narrow. They can normalize deterministic schema shape, but they do not certify scientific correctness.

Phase 1 — Core graph (always)

1. Build PaperExtraction metadata (id/title/doi/pmid if available).
   • Resolve doi and pmid from the staged source, OCR/article outputs, TEI/XML, or local metadata whenever recoverable.
   • If one or both cannot be resolved, record explicit doi_status / pmid_status entries in extraction_activities.parameters with resolved or unresolved.
2. Extract Artifacts when the paper exposes figure/table/supplement captions.
3. Extract Datasets when the paper exposes data-availability text, repository links, accessions, or project identifiers.
4. Extract Entities with ontology annotations when possible.
5. Extract Assertions (hypotheses, result-claims, conclusions):
   • must include contexts (≥1)
   • must include normalization_status
   • include criticality (core, major, supporting, background) when the importance of the claim can be judged
   • include falsification_criteria for any core or major assertion (what observation would weaken or refute the claim)
6. Extract EvidenceItems (results/analyses/citations).
7. Create EvidenceLinks (EvidenceItem -> Assertion) with polarity, strength, rationale.
8. Add TextSpans grounding key Assertions/EvidenceItems/EvidenceLinks.

Phase 2 — Study & experiment structure (if feasible)

• Add Study and Experiment objects.
• Enrich Contexts (organism/cell_type/tissue/disease) using Entity references.

Phase 3 — Conditions + reasoning + critique/gaps + QA (when available)

• Add Condition objects (dose/time/genotype/treatment regime).
• Add InferenceSteps for explicit/implicit reasoning chains.
• Add StudyCritique and KnowledgeGap objects.
• Generate QA items from templates.

Input Requirements

• A full manuscript, preprint, article markdown, XML/JSON extraction, or equivalent source text.
• Any available identifiers: DOI, PMID, PMCID, title, journal, authors, year, and source URL.
• Source text or article JSON when offset-aware TextSpan validation is required.
• A target path for paper_extraction.json and validation reports.

Output

• paper_extraction.json conforming to assets/csag.yaml.
• Validation report from scripts/validate_paper_extraction.py.
• Quality report from scripts/csag_quality_report.py.
• Optional QA items generated from assets/csag_qa_templates.yaml.

Minimum coverage expectations (full research articles)

Unless the source is a short note/editorial with genuinely limited content, target:

• =1 assertion for hypothesis/research-question/objective when present

• =2 result/conclusion assertions from different parts of the paper when present

• =2 evidence items and >=2 evidence links when present

• =1 inference step when reasoning combines multiple premises/evidence

• =1 critique/gap when explicitly discussed by authors

• =1 artifact when figure/table captions are present in the source

• =1 dataset when data-availability text, accessions, or repository links are present in the source

If a category is not present in the paper, state that in notes for the paper/assertion rather than silently omitting it.

Quality Gates

Confirm all checks:

• Assertions are not just keyword-hit snippets; they reflect core study claims.
• Evidence links cover key claims, not only the first matched sentence.
• At least one TextSpan anchors each non-trivial extracted object.
• Output includes explicit statement of absent components (e.g., no clear hypothesis section).
• doi / pmid are resolved when recoverable, or explicit doi_status / pmid_status parameters are present in extraction_activities.
• artifacts are present when figure/table captions are present in the source.
• datasets are present when data-availability text, accessions, or repository links are present in the source.
• The repo-local validation pass succeeds (scripts/validate_paper_extraction.py).
• The quality report is reviewed (scripts/csag_quality_report.py). Treat it as the default CSAG knowledge-artifact evaluation after paper_extraction.json is generated: inspect completeness, missing_or_weak, and field_quality as well as issues.
• Any non-empty issues list is resolved before stopping; warnings in missing_or_weak are fixed or justified in notes.

For higher-stakes use, run validation with --profile promoted_claim or --profile benchmark_key. These profiles additionally require:

• every non-background assertion has decisive evidence and assertion/evidence text-span grounding
• core and major benchmark assertions have at least moderate decisive evidence unless explicitly marked as limitation or speculation

Anti-patterns (forbidden)

• Emitting a paper with only one assertion/evidence pair when richer claims are present.
• Restricting extraction to sentences that mention the topic terms used during retrieval.
• Skipping mechanistic or statistical claims because they sit outside the topic terms.

Identifier and provenance requirements

1. Use pmid:<id> for PaperExtraction.id when PMID is known; fall back to doi:<doi> or csag:doc/<slug> otherwise.
2. Anchor TextSpan.document_id to the same identifier used for PaperExtraction.id.
3. If only PMCID is available, use pmc:<id> and mark PMID resolution as pending in notes.
4. Produce one PaperExtraction per source manuscript; do not mix spans across manuscripts.
5. Record extraction_activities with tool_name, tool_version, model_name (when applicable), and explicit doi_status / pmid_status parameters when those identifiers cannot be resolved from the source.

Validation hook (blocking)

Before finalizing the extraction, run:

uv run --script skills/csag-extraction/scripts/validate_paper_extraction.py \
  ABS_PATH/paper_extraction.json \
  --source-markdown ABS_PATH/STEM.md \
  --article-json ABS_PATH/STEM.article.json \
  --report-out ABS_PATH/paper_extraction.validation.json

Use --profile promoted_claim for curated claims and --profile benchmark_key for scoring keys. Do not stop until the validator returns OK.

Use JSON for PaperExtraction instances and validation reports. YAML is reserved for the authoritative LinkML schema and QA templates. Add --strict when every assertion, evidence item, and evidence link must carry a TextSpan; missing grounding then exits nonzero.

Then run the quality report and review how complete the artifact is, what is missing or weak, and the field-level information quality:

uv run python skills/csag-extraction/scripts/csag_quality_report.py \
  ABS_PATH/paper_extraction.json \
  --source-markdown ABS_PATH/STEM.md \
  --article-json ABS_PATH/STEM.article.json \
  --report-out ABS_PATH/paper_extraction.quality.json \
  --strict

Manuscript interrogation questions

Before finalizing a PaperExtraction, answer these (internally or as qa_items):

• What are the manuscript's hypotheses, research questions, or objectives?
• What are the primary result claims and conclusions or discoveries?
• What evidence supports each key claim, and what evidence (if any) refutes it?
• Which assertions are core, major, supporting, or background?
• What observation or analysis would falsify or seriously weaken each core or major assertion?
• What inference or mechanistic chains connect evidence to conclusions?
• What limitations or flaws are stated or strongly implied?
• What open knowledge gaps or future-work items are stated?

Map answers to CSAG objects:

• hypotheses / results / conclusions → assertions
• claim importance → assertion criticality
• falsifiability checks → assertion falsification_criteria
• support / refute → evidence_links
• reasoning chains → inferences
• limitations → critiques
• open questions → knowledge_gaps
• question-driven outputs → qa_items (from assets/csag_qa_templates.yaml)

Normalization rubric

• raw: free-text only; triple fields may be empty.
• partially_normalized: some of {subject,predicate,object} filled OR ambiguous mappings.
• fully_normalized: subject+predicate+object present; refer to Entity IDs; predicate is CURIE/URI (RO/SIO preferred).

QA templates

Use assets/csag_qa_templates.yaml to instantiate QAItem + Answer objects. Answers must cite supporting_assertions and/or supporting_evidence_links.

For edge cases and scoring guidance, see references/CSAG_PLAYBOOK.md.

Examples

uv run --script skills/csag-extraction/scripts/validate_paper_extraction.py \
  work/paper/paper_extraction.json \
  --source-markdown work/paper/paper.md \
  --article-json work/paper/paper.article.json \
  --report-out work/paper/paper_extraction.validation.json

Troubleshooting

• Validator reports missing contexts: Add at least one Context to every Assertion; this is not optional.
• Dangling references: Rebuild IDs deterministically and verify every referenced assertion, evidence item, entity, artifact, and dataset exists.
• Weak extraction: Re-read the full manuscript and add missing hypotheses, result claims, evidence links, critiques, gaps, artifacts, and datasets when present.
• Offset mismatch: Use exact source text for TextSpans and rerun validation against the same markdown/article JSON used for extraction.