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From archora-research
Generates falsifiable, testable research hypotheses from notes and documents. Use when brainstorming hypotheses, generating research questions, or identifying testable predictions. Not for general Q&A.
npx claudepluginhub richard-kim-79/archora-skillsHow this skill is triggered — by the user, by Claude, or both
Slash command
/archora-research:hypothesisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Generate falsifiable, testable research hypotheses from the user's notes and research content.
Formulates testable hypotheses from observations using the scientific method. Helps design experiments, propose mechanisms, and generate predictions.
Turns observations into testable hypotheses with predictions, mechanisms, and experiments. Follows scientific method; use for ideation or LLM testing on datasets.
Guides research project initiation from idea generation to planning using 5W1H brainstorming, literature review, gap analysis, and research question definition.
Share bugs, ideas, or general feedback.
Generate falsifiable, testable research hypotheses from the user's notes and research content.
# 🧪 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
...
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