Content Filter Skill

Assess content for relevance to AI research intelligence gathering. Filter noise and classify what remains.

Assessment Criteria

1. Relevance Score (0.0-1.0)

How relevant is this to understanding AI research progress, capabilities, limitations, or field direction?

Score Range	Meaning	Examples
0.0-0.3	Not relevant	Personal updates, off-topic, promotional
0.3-0.6	Tangentially relevant	General tech news, adjacent topics
0.6-0.8	Relevant	Discusses AI research, capabilities, field
0.8-1.0	Highly relevant	Substantive claims, predictions, research insights

2. Topic Classification

Assign ONE primary topic:

• scaling

  : Scaling laws, compute, training efficiency

• reasoning

  : LLM reasoning, chain-of-thought, planning capabilities

• agents

  : AI agents, tool use, autonomy

• safety

  : AI safety, alignment, control

• interpretability

  : Mechanistic interpretability, understanding models

• multimodal

  : Vision, audio, video models

• rlhf

  : RLHF, preference learning, Constitutional AI

• robotics

  : Embodied AI, robotics

• benchmarks

  : Evals, benchmarks, capability measurement

• infrastructure

  : Training infra, chips, hardware

• policy

  : AI policy, regulation, governance

• general

  : General AI commentary

• other

  : Doesn't fit above categories

3. Content Type

What kind of content is this?

• prediction

  : Makes claims about future AI capabilities/timelines

• research-hint

  : Hints at ongoing/unpublished research

• opinion

  : Expresses opinion on AI progress/direction

• factual

  : Reports factual information about released work

• critique

  : Critiques AI capabilities or claims

• meta

  : Meta-commentary on the field

• noise

  : Not substantive

4. Substantiveness

Does this contain actual claims, arguments, or insights?

Substantive examples:

• "We found that CoT prompting shows diminishing returns beyond 8 steps"
• "The next generation will likely solve ARC-AGI"
• "Interpretability research is underrated"

Non-substantive examples:

• "Cool paper!" (reaction only)
• "Link: [url]" (link share without commentary)
• "Having coffee ☕" (personal update)

5. Author Category

Classify the author:

• lab-researcher

  : Works at major AI lab (Anthropic, OpenAI, DeepMind, Meta AI, xAI, Mistral, Cohere)

• critic

  : Known AI skeptic/critic with credentials (Marcus, Chollet, Mitchell, Bender, Brooks)

• academic

  : University researcher

• independent

  : Independent researcher/commentator

• journalist

  : AI journalist

• unknown

  : Cannot determine

Output Format

Return JSON:

{
  "assessments": [
    {
      "itemIndex": 0,
      "relevance": 0.85,
      "topic": "reasoning",
      "contentType": "research-hint",
      "isSubstantive": true,
      "authorCategory": "lab-researcher",
      "brief": "One sentence summary"
    }
  ]
}

Filtering Heuristics

High Signal Indicators

• Lab researchers discussing their own work area
• Specific technical claims with numbers/benchmarks
• Predictions with timeframes
• Explicit disagreements between notable figures
• Hints using hedged language ("we've been seeing...", "I can't say much but...")

Low Signal Indicators

• Pure link shares without commentary
• Conference attendance announcements
• Hiring posts
• Generic congratulations
• Retweets without quote
• Personal life updates
• Product launches (unless with technical claims)

Gray Areas

• Paper summaries (relevant if includes opinion/analysis)
• Q&A responses (depends on question depth)
• Thread continuations (may need full thread context)