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Experiment Runner

Community
cpt13-g
experiment-runner

Implement and execute approved research Experiment, Tuning, or Design Cards with reproducible logs, metrics, environment, and artifacts. Use for bounded coding, debugging, and runs; not for changing claims, priorities, budgets, or the Research Spine.

Overview

Publishercpt13-g
Repositorybuild-research-evidence
Skill nameexperiment-runner
Stars
50
Forks
2
Bundled files
1
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cpt13-g on GitHub. Read the source before you install it.

Installation

Install the Experiment Runner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cpt13-g/build-research-evidence/tree/main/.agents/skills/experiment-runner
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cpt13-g/build-research-evidence.git /tmp/build-research-evidence
mkdir -p .claude/skills
cp -r /tmp/build-research-evidence/.agents/skills/experiment-runner .claude/skills/experiment-runner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Experiment Runner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Experiment Runner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Experiment Runner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Experiment Runner

Read the current task with python tools/context_view.py show --role runner --experiment E0001 (replace the ID). It supplies the complete Card/contract and relevant authoritative state with source hashes. Read docs/PROTOCOLS.md, docs/ROLE_HANDOFFS.md and docs/CONTEXT_POLICY.md on a fresh context or changed policy hash; reuse unchanged loaded rules. For Cheap Evaluation or refinement, also read docs/METHOD_REALIZATION_PROTOCOL.md. The view is not authorization: use Registry activation and Runner checks. Refuse pre-Gate-1, missing, draft, stale, budget-ineligible, dependency-blocked, phase-ineligible or priority-ineligible work and return exact blocking fields.

Implement shared behavior in src/ and experiment differences in configs/. Preserve original data and checkpoints. Use tools/runner.py to emit the run manifest with command, Card/config identity, declared data and seed protocol, code/environment identity, timestamps, logs, raw metrics, exit status, artifact hashes, and explicit missing declarations. Do not describe a run as reproducible when its manifest is partial.

Complete approved seeds and necessary checks as one bounded execution task, retaining all attempt logs/manifests. Resolve clear F1 engineering faults within the Card and approved debug budget without a scientific-audit invocation per shell error. Uncertain causes, scientifically meaningful failures and changes affecting mechanism or comparison return to Orchestrator/Auditor. A method change requires an approved Design Iteration. Never alter the Research Spine, Claim/EQ, priority, fairness protocol, budget or stop condition. Refresh state after changes; do not add runs or lower seed requirements for context savings.

For cheap_evaluation, implement exactly the Card's proxy reductions and shared protocol across Candidates; label outputs preliminary and never promote them to paper evidence. For refinement_validation, modify only the approved Design Iteration dimensions and preserve the controlled comparison.

Stop once the Card completion contract is decided. Extra runs require an Orchestrator-approved named evidence gap; general completeness is insufficient. Return execution facts without a scientific verdict or the Orchestrator's desired interpretation. A failed run remains in experiment memory and goes to result-auditor when interpretable.

An Evidence Completion Queue entry is a recommendation, not execution authorization. Run it only after the Orchestrator creates and approves a normal Card and Registry activation succeeds. P3/SKIP entries are never default execution work.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Experiment Runner AI skill do?

Implement and execute approved research Experiment, Tuning, or Design Cards with reproducible logs, metrics, environment, and artifacts. Use for bounded coding, debugging, and runs; not for changing claims, priorities, budgets, or the Research Spine.

Why use Experiment Runner on TypingMind?

Because you install it once and use it with any model. Experiment Runner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Experiment Runner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cpt13-g/build-research-evidence/tree/main/.agents/skills/experiment-runner. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Experiment Runner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Experiment Runner?

As many as you like. As long as a model supports skills, you can use Experiment Runner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Experiment Runner AI skill free?

Yes. It is published on GitHub by cpt13-g under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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