Claim extraction

Skill rhowardstone/Claude-Code-Scientist/.claude/skills/claim-extraction

Transform Claude Code into a semi-autonomous, self-improving scientific researcher. Literature review, data acquisition, experimentation, synthesis, peer review

Install

npx -y skills add rhowardstone/Claude-Code-Scientist --skill claim-extraction

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

8 stars8 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Guides rigorous evidence extraction from papers. Use when reviewing literature to ensure proper provenance tracking.

SKILL.md

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Claim Extraction Guidelines

Extract evidence with full provenance from research papers.

Target: 2-5 Claims Per Paper

If averaging less than 2 claims per paper, re-read. You're missing evidence.

What to Extract

From Results Section (Richest)

Quantitative findings ("X increased by Y%")
Comparative results ("A outperformed B")
Statistical significance ("p < 0.05")

From Methods Section

Algorithmic claims ("uses penalty-based scoring")
Parameter choices ("default k=5 optimal")
Implementation details affecting reproducibility

From Discussion Section

Limitations acknowledged
Comparisons to prior work
Future directions

From Introduction

State-of-the-art claims
Known gaps motivating the study

Claim Structure

{
  "claim_text": "Tool-X achieves O(n) time complexity for data processing",
  "supports_rq": ["RQ1", "RQ2"],
  "rq_context": "Addresses RQ1 by characterizing efficiency; supports RQ2 baseline",
  "importance": "Establishes performance expectations for analysis tools",
  "evidence": {
    "source_doi": "10.1093/nar/gks596",
    "source_type": "journal",
    "quote": "The algorithm achieves linear time complexity O(n) where n is the input data size",
    "page": 7,
    "section": "Results",
    "context_surrounding_text": "We benchmarked Tool-X on datasets ranging from 1KB to 10GB. The algorithm achieves linear time complexity...",
    "confidence": 0.95,
    "confidence_justification": "Explicit quantitative statement with empirical validation in peer-reviewed publication"
  }
}

Required Fields

Every claim MUST have:

source_doi - Paper DOI
quote - EXACT text (not paraphrased)
page or section - Location in source
confidence - 0.0-1.0 score
confidence_justification - Why this confidence

Confidence Guidelines

Score	Meaning	Example
0.9-1.0	Explicit quantitative with validation	"achieved 95% accuracy (n=1000, p<0.001)"
0.7-0.9	Clear statement with evidence	"significantly outperformed baseline"
0.5-0.7	Reasonable inference	"suggests improved performance"
0.3-0.5	Weak evidence, needs corroboration	"may indicate..."
<0.3	Speculation	Don't extract as claim

Handling Conflicts

When papers disagree:

{
  "conflict": "Paper A claims X, Paper B claims Y",
  "investigation": {
    "paper_a_method": "Used dataset Z with parameters...",
    "paper_b_method": "Different dataset W with...",
    "root_cause": "Different experimental setups"
  },
  "resolution": "Both valid in their contexts",
  "confidence": 0.8
}

Anti-Patterns

Paraphrased quotes: Must be exact text
Missing DOIs: Every claim needs source
Vague claims: "Tool is good" (no specifics)
Unsupported confidence: Score without justification
Single-source claims: Try to corroborate

Output Format

Save to evidence_report.json:

{
  "papers_reviewed": 12,
  "rq_coverage": {
    "RQ1": {"status": "answered", "confidence": 0.9, "claims": [...]},
    "RQ2": {"status": "partial", "gaps": ["..."]}
  },
  "all_claims": [...],
  "conflicts_identified": [...],
  "new_rqs_proposed": [...]
}

Extract rigorously. Cite exactly. Justify confidence.