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• •research-hint: Hints at ongoing/unpublished research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