How do I install the Run Hypothesis skill?

Run npx skillmds@latest add justrach/run-hypothesis in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Run Hypothesis safe to use?

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Which AI agents work with Run Hypothesis?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Run Hypothesis free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Run Hypothesis?

justrach (@justrach) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Run Hypothesis by justrach — Agent Skill

Name: Run Hypothesis
Author: justrach

Run Hypothesis

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.

justrach 1a7e0f0 3.4 KB Updated Sep 17, 2026

File contents

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 $ARGUMENTS to 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_hypothesis with:
- problem: the full problem statement from $ARGUMENTS
- domain: inferred or specified domain
- workspace_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`

---name: run-hypothesisdescription: 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.---# Hypothesis Trajectory EngineThis 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## Instructions1. Parse `$ARGUMENTS` to extract the problem statement and any domain hint (e.g. "quantitative finance", "theoretical physics", "biology", "economics")2. If no domain is specified, infer it from the problem content3. Call `mcp__agentlab__run_hypothesis` with: - `problem`: the full problem statement from `$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 section5. Present the synthesis report clearly to the user, noting which workspace files were written## OutputThe 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` |

Frequently asked questions