content-filter

# 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- `research-hint`: Hints at ongoing/unpublished research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:
```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)