Content Filter Skill

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?

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• research-hint: Hints at ongoing/​unpublished research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)