Generate falsifiable, testable research hypotheses from the user's notes and research content.
Workflow
- Read available content — scan the notes, documents, or files the user provides
- Identify themes — find recurring concepts, claims, relationships, and open questions
- Generate hypotheses — produce 4–6 specific, falsifiable hypotheses
- Assign confidence — rate each hypothesis HIGH / MEDIUM / LOW based on evidence in the content
- Format output — present in structured markdown
Output Format
# 🧪 Hypothesis Analysis
## Summary
[2–3 sentences describing the main themes and what the hypotheses cover]
Generated Hypotheses
🔴 [Hypothesis Title] — HIGH confidence
Hypothesis: [Specific, falsifiable statement with measurable prediction]
Rationale: [Which sources/notes support this, with specific references]
Testable: Yes | Confidence: HIGH
🟡 [Hypothesis Title] — MEDIUM confidence
...
## Confidence levels
- 🔴 **HIGH** — directly supported by multiple sources in the content
- 🟡 **MEDIUM** — partially supported or requires inference across sources
- 🟢 **LOW** — speculative but worth investigating; limited direct support
## Quality criteria for good hypotheses
- **Falsifiable**: Can be proven wrong — avoid "X may affect Y"
- **Specific**: Mentions measurable variables, not vague concepts
- **Grounded**: Traceable to actual content provided, not general knowledge
- **Novel**: Connects ideas across sources rather than restating the obvious
## Example
**Input:** Notes on predictive coding and synaptic plasticity
**Good hypothesis:**
> "Precision-weighted prediction errors in the Rao and Ballard model are encoded through spike-timing-dependent plasticity (STDP) in the visual cortex, such that altering STDP timing windows disrupts receptive field formation."
**Poor hypothesis:**
> "Synaptic plasticity is important for learning." ← not falsifiable, too vague