What is topic-synthesis?

This Claude skill provides a sophisticated framework for synthesizing complex AI research discourse. By analyzing claims from lab researchers, critics, and independent experts, it identifies consensus points, maps fundamental disagreements, and calculates a 'hype delta' to provide a balanced perspective on emerging AI narratives and technological progress. It is designed to transform fragmented technical debates into structured, actionable intelligence.

When should I use topic-synthesis?

topic-synthesis is useful in the following scenarios: • Evaluating the validity of new AI model performance claims by comparing official lab reports with independent skeptic reviews. • Tracking the evolution of specific AI debates, such as the scaling hypothesis or AGI timelines, across different stakeholder groups. • Identifying potential overhype in AI marketing by calculating the sentiment gap between developer enthusiasm and critical analysis. • Generating structured summaries of academic discourse for policy-makers or investors who require a balanced view of technical breakthroughs. • Synthesizing emerging narratives in the AI industry to predict future research directions and technological shifts.

name	topic-synthesis
description	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.

Topic Synthesis Skill

Synthesize claims from multiple sources to produce a coherent picture of discourse on an AI topic.

Input Structure

You'll receive claims grouped by source type:

Lab researcher claims: From people at Anthropic, OpenAI, DeepMind, Meta AI, etc.
Critic claims: From credentialed skeptics like Marcus, Chollet, Mitchell, Bender
Independent claims: From independent researchers and practitioners

Synthesis Components

1. Lab Consensus

What do lab researchers generally agree on? Write 2-3 sentences capturing the central themes.

Look for:

Repeated claims across multiple lab researchers
Consistent stance on capabilities/limitations
Shared predictions or timelines

2. Critic Consensus

What do critics generally agree on? Write 2-3 sentences capturing the central themes.

Look for:

Common critiques raised by multiple critics
Shared concerns about hype or methodology
Consistent alternative explanations

3. Agreements

What do BOTH sides agree on? These are often the most reliable signal.

Examples:

"Current models struggle with certain forms of reasoning"
"More compute does improve capabilities"
"Benchmarks have limitations"

4. Disagreements

Where do they fundamentally disagree? Structure as:

{
  "point": "Whether scaling alone leads to AGI",
  "labPosition": "Many believe continued scaling will yield AGI-like capabilities",
  "criticPosition": "Fundamental architectural changes needed beyond scaling"
}

5. Emerging Narratives

What new framings or narratives are emerging in the discourse?

Examples:

"Post-training is the new scaling"
"Reasoning models are hitting walls"
"Safety concerns are becoming mainstream"

6. Notable Predictions

Extract specific predictions with attribution:

{
  "text": "Prediction text",
  "author": "Author name",
  "confidence": 0.7,
  "timeframe": "medium-term"
}

7. Evidence Quality

Rate overall quality of evidence cited (0.0-1.0):

1.0: Multiple papers cited, detailed reasoning, reproducible claims
0.7: Some evidence, logical arguments
0.4: Mostly opinions with occasional support
0.1: Pure speculation, no evidence

Hype Delta Calculation

Calculate the "hype delta" - the gap between lab enthusiasm and critic skepticism:

hypeDelta = avgLabBullishness - avgCriticBullishness

Interpretation:

Positive delta (> 0.2): Labs more bullish → potentially overhyped
Negative delta (< -0.2): Critics more bullish → potentially underhyped
Near zero (-0.2 to 0.2): Relatively aligned assessment

Output Format

Return JSON:

{
  "topic": "reasoning",
  "labConsensus": "Lab researchers believe...",
  "criticConsensus": "Critics argue...",
  "agreements": ["Point 1", "Point 2"],
  "disagreements": [
    {
      "point": "Description",
      "labPosition": "Lab view",
      "criticPosition": "Critic view"
    }
  ],
  "emergingNarratives": ["Narrative 1", "Narrative 2"],
  "predictions": [
    {
      "text": "Prediction",
      "author": "Name",
      "confidence": 0.7,
      "timeframe": "medium-term"
    }
  ],
  "evidenceQuality": 0.6,
  "hypeDelta": {
    "delta": 0.25,
    "labSentiment": 0.75,
    "criticSentiment": 0.50,
    "interpretation": "Moderately overhyped"
  },
  "synthesisNarrative": "Two paragraphs summarizing the current state..."
}

Synthesis Narrative Guidelines

Write a balanced 2-paragraph narrative:

Paragraph 1: Current state of the topic

What's actually happening
Key developments
Where there's genuine progress

Paragraph 2: Contested areas and outlook

Where disagreement exists
What's uncertain
What to watch for

Maintain balanced tone - acknowledge both genuine progress AND legitimate concerns.

topic-synthesis

When & Why to Use This Skill

Use Cases