.claude/skills/hint-detection/SKILL.md
Detect hints about unreleased AI research or capabilities from lab researcher communications. Use when analyzing tweets, posts, or interviews from people at major AI labs to identify signals about upcoming work.
npx skillsauth add rickoslyder/HypeDelta hint-detectionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Lab researchers often hint at work before publication. This skill identifies these signals.
Language that implies results without specifics:
Answers that acknowledge something exists:
Certainty about unreleased capabilities:
Disproportionate excitement about a topic:
Sometimes denial calls attention:
Suggestions about release timing:
Language implying current vs future:
Hiring patterns can indicate direction:
Weight hints by author credibility:
For each potential hint:
Extract the exact language that suggests a hint.
What capability or result is being hinted at?
How confident are you this is a real hint vs. noise?
Consider:
When might this be revealed?
imminent: Days to weeksnear-term: 1-3 monthsmedium-term: 3-12 monthsunclear: No timing signalWhat area of AI?
Return JSON:
{
"hints": [
{
"hintText": "The exact quote suggesting a hint",
"author": "Author name",
"affiliation": "Company/org",
"impliedCapability": "What they're hinting at",
"confidence": 0.7,
"reasoning": "Why you think this is a hint",
"timeframe": "near-term",
"domain": "reasoning",
"sourceUrl": "URL if available"
}
],
"noHintsFound": false
}
If no credible hints are detected, return:
{
"hints": [],
"noHintsFound": true,
"notes": "Brief explanation of why content doesn't contain hints"
}
Not every comment is a hint. Exclude:
Prioritize hints that:
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.