run-hypothesis

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	<problem statement> [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

1. Parse $ARGUMENTS to extract the problem statement and any domain hint (e.g. "quantitative finance", "theoretical physics", "biology", "economics")
2. If no domain is specified, infer it from the problem content
3. Call mcp__agentlab__run_hypothesis with:
   • problem: the full problem statement from $ARGUMENTS
   • domain: inferred or specified domain
   • workspace_dir: hypothesis_workspace (relative, in current repo)
4. The tool returns a JSON object with phase outputs and a synthesis section
5. 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