By name: run-hypothesis
description: Run a 6-phase scientific hypothesis trajectory on any open problem — from rough intuition to a formally structured, mathematically testable framework. Use when the user wants to formalize a hypothesis, prove a theory works, improve an existing model (e.g. Black-Scholes, CAPM, relativity, evolution), or explore any open research question across any domain.
argument-hint: " [domain]"
Hypothesis Trajectory Engine
This skill invokes the run_hypothesis tool from the AgentLab MCP server (mcp__agentlab__run_hypothesis), which runs a 6-phase multi-agent reasoning pipeline that turns rough intuitions into formal, testable hypotheses.
Pipeline
| Phase | Agent | Mode | Runs |
|---|---|---|---|
| 0 | formalizer — maps every informal term to a formal mathematical object |
deep (Opus) | solo |
| 1 | existence_prover — proves the critical condition/moment exists (EVT, IVT) |
smart | parallel |
| 2 | constraint_closer — writes the full conservation/constraint balance sheet |
smart | parallel |
| 3 | gap_finder — quantifies the gap between current theory and observations |
smart | parallel |
| 4 | mechanism_designer — designs a mechanism that closes the gap |
deep (Opus) | sequential |
| 5 | deeper_connector — connects mechanism to deeper principles, lists predictions |
smart | sequential |
| ∞ | synthesizer — structured final report written to workspace_dir/final_report.md |
smart | sequential |
Phases 1–3 run in parallel threads. Each phase reads prior phase outputs. If a phase writes a compute.py, the runner executes it with python3 (numpy, scipy, sympy available) and injects results into the next phase.
How to invoke
Directly:
/run-hypothesis prove that special relativity works
/run-hypothesis make Black-Scholes better
/run-hypothesis why do L-amino acids dominate in biology
/run-hypothesis improve the SEIR epidemic model
Claude auto-invokes this skill when the user asks to:
- Prove or verify a theory
- Formalize a hypothesis or rough idea
- Improve an existing model or framework
- Explore an open research question
- Find the mechanism behind an observed phenomenon
Instructions
- Parse
$ARGUMENTSto extract the problem statement and any domain hint (e.g. "quantitative finance", "theoretical physics", "biology", "economics") - If no domain is specified, infer it from the problem content
- Call
mcp__agentlab__run_hypothesiswith:problem: the full problem statement from$ARGUMENTSdomain: inferred or specified domainworkspace_dir:hypothesis_workspace(relative, in current repo)
- The tool returns a JSON object with phase outputs and a synthesis section
- Present the synthesis report clearly to the user, noting which workspace files were written
Output
The tool writes phase findings to hypothesis_workspace/phaseN_<name>/findings.md and a final report to hypothesis_workspace/final_report.md. Reference these files if the user wants to dig into a specific phase.
Domain examples
| Problem type | Domain hint |
|---|---|
| Options pricing, volatility surface | quantitative finance |
| Particle physics, cosmology | theoretical physics |
| Protein folding, amino acids | biochemistry |
| Epidemic modeling | epidemiology |
| Market microstructure | financial economics |
| Neural synchrony, consciousness | computational neuroscience |
| Economic tipping points | complexity economics |