.claude/skills/claim-extraction/SKILL.md
Extract structured claims, predictions, hints, and opinions from AI research content. Use when processing tweets, blog posts, substacks, or other content from AI researchers to identify substantive assertions about AI capabilities, limitations, and progress.
npx skillsauth add rickoslyder/HypeDelta claim-extractionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Extract all substantive claims from AI research content. A claim is any assertion that:
For each claim, extract:
The claim in clear, standalone form. Paraphrase if needed for clarity.
fact: Assertion about current state ("GPT-4 can do X")prediction: Forward-looking ("By 2026, we'll have...")hint: Implies unreleased work ("We've been seeing interesting results with...")opinion: Positioned take ("I think scaling is/isn't sufficient")critique: Challenges others ("Marcus is wrong because...")question: Genuine uncertainty expressed ("I'm not sure if...")Primary topic category:
scaling: Scaling laws, compute, training efficiencyreasoning: LLM reasoning, chain-of-thought, planningagents: AI agents, tool use, autonomysafety: AI safety, alignment, controlinterpretability: Mechanistic interpretabilitymultimodal: Vision, audio, video modelsrlhf: RLHF, preference learning, Constitutional AIbenchmarks: Evals, benchmarks, capability measurementinfrastructure: Training infra, chips, hardwarepolicy: AI policy, regulation, governancegeneral: General AI commentarybullish: Optimistic about AI progress/capabilitiesbearish: Skeptical/pessimistic about AI progressneutral: Balanced or factual without clear stanceFloat from 0.0 (maximally bearish) to 1.0 (maximally bullish)
How confident does the author seem? (0.0-1.0)
near-term: < 1 yearmedium-term: 1-3 yearslong-term: 3-10 yearsunspecified: No clear timeframenull: Not a predictionstrong: Cites data, papers, or detailed reasoningmoderate: Some reasoning but not rigorousweak: Assertion without supportappeal-to-authority: "Trust me, I work on this"Is this claim notable enough to quote in a digest? (0.0-1.0)
Return JSON:
{
"claims": [
{
"claimText": "The claim in clear form",
"claimType": "prediction",
"topic": "reasoning",
"stance": "bullish",
"bullishness": 0.8,
"confidence": 0.7,
"timeframe": "medium-term",
"evidenceProvided": "moderate",
"quoteworthiness": 0.6,
"relatedTo": ["o1", "chain-of-thought"],
"originalQuote": "Brief relevant quote if notable"
}
]
}
Consider the author's affiliation when assessing:
development
Filter and classify AI research content for relevance. Use when processing raw content from Twitter, Substacks, blogs, or podcasts to determine if it's worth extracting claims from. Assigns relevance scores, topics, and author categories.
data-ai
Synthesize claims across multiple sources to identify consensus, disagreements, and emerging narratives on AI research topics. Use when you have claims from both lab researchers and critics on the same topic and need to understand where they agree, disagree, and what the overall hype level is.
data-ai
Track and evaluate AI predictions over time to assess accuracy. Use when reviewing past predictions to determine if they came true, failed, or remain uncertain.
testing
Assess overall hype levels across AI topics by comparing lab researcher enthusiasm against critic skepticism. Use after topic synthesis to identify which topics are overhyped, underhyped, or accurately assessed by the field.