hype-assessment
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.
npx skills add majiayu000/claude-skill-registry --skill hype-assessment-rickoslyder-hypedelta --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Hype Assessment Skill Assess which AI topics are overhyped, underhyped, or accurately assessed based on synthesized claims. ## Assessment Framework ### Overhyped Topics (Lab enthusiasm exceeds warranted confidence) Signs of overhype: - Lab researchers make strong claims that critics have substantively challenged - Evidence quality is low but confidence is high - Past predictions in this area have repeatedly failed - Marketing language exceeds technical substance - Hype delta > +0.3 ### Underhyped Topics (Critic skepticism may be excessive) Signs of underhype: - Real progress has been made but critics haven't updated - Evidence is strong but narrative hasn't caught up - Lab hints suggest unreleased capabilities - Quiet progress without announcements - Hype delta < -0.3 ### Accurately Assessed Topics Signs of accurate assessment: - Lab and critic views are relatively aligned - Claims match observable evidence - Predictions have been reasonably accurate - Hype delta between -0.2 and +0.2 ## Scoring System For each topic, assign a score from -1.0 to +1.0: | Score | Meaning | |-------|---------| | +1.0 | Severely overhyped - massive gap between claims and reality | | +0.5 | Moderately
- Assessment Framework
- Overhyped Topics (Lab enthusiasm exceeds warranted confidence)
- Underhyped Topics (Critic skepticism may be excessive)
- Accurately Assessed Topics
- Scoring System
- Evidence to Consider
- For Overhyped Assessment
- For Underhyped Assessment
- For Accurate Assessment
- Output Format
- Overall Field Sentiment
- Summary Guidelines
What does the hype-assessment skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill hype-assessment-rickoslyder-hypedelta --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.