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running-experiments

Execute hypothesis-specified experiments via Bash and record outputs into hypothesis files.

Updated Jan 23, 2026
Search Tags:#data-analysis#hypothesis#code-execution#experiments#research-workflow#evidence-gathering
One-click install
npx skills add https://github.com/pipemind-com/pipemind-marketplace --skill running-experiments-pipemind-com
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: running-experiments
Source: https://github.com/pipemind-com/pipemind-marketplace/tree/main/plugins/scientific-method/skills/running-experiments
Command: npx skills add https://github.com/pipemind-com/pipemind-marketplace --skill running-experiments-pipemind-com

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill executes pending experiments defined in a hypothesis file and records honest, reproducible results so research progress is verifiable and not narrated.

Core Features & Use Cases

  • Automated Code Execution: Create experiment artifacts under experiments/<hypothesis-slug>/, run code via Bash, and capture stdout, stderr, and exit codes.
  • Multi-Modal Experiments: Support for math proofs, logical deductions, evidence-gathering, and data analysis with structured result templates and evidence strength labels.
  • Safety and Provenance: Checks dependencies, enforces sandboxing and file-write limits, never overwrites existing artifacts, and stops further tests after decisive outcomes.
  • Use Case: Run all pending experiments in a project's hypothesis file to produce reproducible Results entries, artifacts, and a novelty assessment when applicable.

Quick Start

Run the experiments listed in ./dark-matter/hypothesis-01.md and record Results into the same file.

Frequently Asked Questions about running-experiments

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run hypothesis experiments and capture code execution results?▼

To run hypothesis experiments, this Skill executes specified Bash code, creates artifacts under experiments paths, captures stdout/stderr/exit codes, and records outcomes directly into the hypothesis file.

What is the best way to automate reproducible data analysis and evidence-gathering?▼

Automating reproducible data analysis means executing experiments defined in a hypothesis file, capturing structured outputs and exit codes, and recording results with evidence strength labels.

How do I record experiment artifacts and update a hypothesis file with results?▼

Recording experiment artifacts happens by running code via Bash, saving files under the experiments directory, and automatically updating the hypothesis Results sections with outcome and evidence strength.

Does running experiments in a sandbox enforce file-write limits and dependency checks?▼

Yes, running experiments enforces sandboxing by checking dependencies, enforcing file-write limits, never overwriting existing artifacts, and stopping further tests after decisive outcomes.

Can I use this for mathematical proofs and logical deductions alongside data analysis?▼

Yes, multi-modal experiments support mathematical proofs, logical deductions, evidence-gathering, and data analysis using structured result templates to document reproducible research outputs.

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