digest-generation

majiayu000's avatarfrom majiayu000

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.

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When & Why to Use This Skill

This Claude skill automates the generation of sophisticated AI intelligence digests by synthesizing complex research data and hype assessments. It provides technical professionals with evidence-based, opinionated insights, ensuring balanced coverage of critical topics like multimodal capabilities, reasoning, and infrastructure while filtering out industry noise.

Use Cases

  • Weekly Technical Intelligence: Transform raw research claims and data into a structured weekly briefing for AI engineering teams and researchers to stay ahead of the curve.
  • Trend & Hype Assessment: Analyze industry narratives to identify overhyped technologies versus under-the-radar breakthroughs using specific evidence-based delta scores.
  • Strategic Decision Support: Monitor lab signals, expert critiques, and key debates to provide technical leadership with a clear, high-signal view of the evolving AI landscape.
namedigest-generation
descriptionGenerate 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.

Digest Generation Skill

Generate a weekly digest of AI research intelligence for sophisticated technical readers.

Audience

Your readers are:

  • Technical professionals who follow AI closely
  • Don't need basics explained
  • Want signal, not noise
  • Appreciate direct, opinionated takes
  • Value evidence-based analysis
  • Skeptical of hype but interested in real progress

CRITICAL: Balanced Topic Coverage

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):

  • multimodal, reasoning, agents, infrastructure, benchmarks, scaling (the "core capability" topics)
  • policy, safety, rlhf, interpretability (the "meta" topics)
  • robotics, general

If multimodal has 15% of claims and RLHF has 5%, multimodal should get 3x the coverage.

Digest Structure

TL;DR

5-7 bullet points covering the BREADTH of topics analyzed.

Format:

  • Include at least one bullet from each major topic area (capabilities, safety, infrastructure)
  • Lead with the topic that has the most claims, not the most hype
  • Ensure diverse topic representation - don't let 2-3 topics dominate

Hype Check

Brief assessment of what's overhyped and underhyped.

For each:

  • Name the topic
  • Explain WHY in 1-2 sentences
  • Cite specific evidence

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.

Topic Breakdown

REQUIRED SECTION - Brief summary of EACH major topic with claims.

For each topic with >3% of claims, include:

  • Topic name and claim count
  • Key finding or trend in 1-2 sentences
  • Notable quote if available

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.

Research Signals

What lab researchers are hinting at or claiming.

Cover signals from MULTIPLE topics, not just the most hyped.

Focus on:

  • Hints about unreleased work
  • Specific capability claims
  • Unexpected admissions of limitations
  • Predictions from credible sources

Quote notable statements with attribution.

Critic Corner

What skeptics are saying and why.

Focus on:

  • Substantive critiques (not just dismissals)
  • Specific counter-arguments to lab claims
  • Alternative explanations for results
  • Concerns worth considering

Key Debates

The most important ongoing disagreements.

For each debate:

  • State the question
  • Summarize both positions
  • Note any new evidence this week

Predictions Tracker

Notable predictions made this week.

Format as table or list:

Prediction Author Confidence Timeframe
"..." Name High/Med/Low Near/Med/Long

Worth Watching

Topics or threads that may become important in coming weeks.

Brief bullets on:

  • Emerging narratives
  • Quiet developments
  • Things that might break through

Tone Guidelines

Do:

  • Be direct and opinionated
  • Take positions based on evidence
  • Call out hype when warranted
  • Acknowledge genuine progress
  • Use specific examples and quotes
  • Write for experts

Don't:

  • Hedge excessively
  • Repeat conventional wisdom without analysis
  • Use marketing language
  • Explain basics
  • Be boring
  • Exceed 1500 words

Example Opening

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.

Output Format

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]
---