.claude/skills/digest-generation/SKILL.md
Generate a weekly AI intelligence digest from synthesized topic analyses and hype assessments. Use after synthesis and hype assessment to produce a readable, opinionated summary for sophisticated technical readers.
npx skillsauth add rickoslyder/HypeDelta digest-generationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Generate a weekly digest of AI research intelligence for sophisticated technical readers.
Your readers are:
You MUST cover ALL major topics proportionally to their claim volume.
Before writing, check the claim distribution. If a topic has 15% of claims, it should get roughly 15% of the digest. Do NOT let hype signals dominate - a topic with 5% of claims but high hype should NOT get more coverage than a topic with 15% of claims.
Topics to always cover (if they have claims):
If multimodal has 15% of claims and RLHF has 5%, multimodal should get 3x the coverage.
5-7 bullet points covering the BREADTH of topics analyzed.
Format:
Brief assessment of what's overhyped and underhyped.
For each:
Example:
Overhyped: Agents (+0.4 delta) — Lab enthusiasm for autonomous agents continues to outpace demonstrated reliability. Recent production deployments show 30-40% failure rates on complex tasks.
Underhyped: Interpretability (-0.3 delta) — Golden Gate Claude and follow-on work show feature steering is becoming practical. Most coverage focuses on capabilities, missing this control story.
REQUIRED SECTION - Brief summary of EACH major topic with claims.
For each topic with >3% of claims, include:
Example:
Multimodal (137 claims, 15%): Video generation architectures converging on diffusion with temporal attention. Key debate: compute efficiency vs quality tradeoffs.
Reasoning (101 claims, 11%): Chain-of-thought still dominant but tree-of-thought gaining traction. Critics note benchmark gaming concerns.
What lab researchers are hinting at or claiming.
Cover signals from MULTIPLE topics, not just the most hyped.
Focus on:
Quote notable statements with attribution.
What skeptics are saying and why.
Focus on:
The most important ongoing disagreements.
For each debate:
Notable predictions made this week.
Format as table or list: | Prediction | Author | Confidence | Timeframe | |------------|--------|------------|-----------| | "..." | Name | High/Med/Low | Near/Med/Long |
Topics or threads that may become important in coming weeks.
Brief bullets on:
This week's AI discourse was dominated by [topic], with lab researchers claiming [X] while critics countered with [Y]. The most interesting signal came from [source], who hinted that [implication]. Meanwhile, [underhyped topic] continues to see quiet progress that deserves more attention.
Return the digest as markdown, ready for publication.
Include frontmatter:
---
title: AI Intelligence Digest - Week of [DATE]
generated: [TIMESTAMP]
claims_analyzed: [N]
topics_covered: [LIST]
---
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.