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inno-experiment-dev

Generate ML implementation plans, scaffold code, and submit experiment runs.

1.0k|117|Updated Feb 26, 2026
Search Tags:#automation#planning#iteration#code-generation#experiment#ml
One-click install
npx skills add https://github.com/OpenLAIR/dr-claw --skill inno-experiment-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: inno-experiment-dev
Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/inno-experiment-dev
Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill inno-experiment-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creates an end-to-end ML project workflow by generating an implementation plan, scaffolding project code, integrating a judge feedback loop, and submitting the final experiment run. It guides teams from idea refinement to measurable submission, ensuring reproducible experiments.

Core Features & Use Cases

  • Plan generation: produces a detailed dataset, model, training, and testing plan coordinated with reference codebases.
  • Code scaffolding & integration: creates a self-contained Experiment/core_code workspace with datasets, models, and training loops.
  • Judge feedback loop: iterates between ML and Judge agents to refine the implementation based on atomic concepts.
  • Submission handling: manages the final submission run, including checkpoint saving and result reporting.

Quick Start

Start by running the inno-experiment-dev skill after completing the code-survey and planning phases to produce an end-to-end implementation and final submission.

Frequently Asked Questions about inno-experiment-dev

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

FAQPage Schema
How do I automate end-to-end ML experiment development from planning to submission?▼

Automating end-to-end ML experiments involves generating implementation plans, scaffolding project code, and using a judge feedback loop to refine the implementation before submitting the final experiment run.

What is a judge feedback loop in machine learning code generation?▼

A judge feedback loop iterates between ML and Judge agents to refine generated code based on atomic concepts, ensuring reproducible, judge-guided experiments across Idea and Plan branches.

How do I scaffold project code for a new ML experiment?▼

Scaffolding project code creates a self-contained Experiment/core_code workspace equipped with datasets, models, and training loops coordinated with reference codebases.

Can I generate a dataset and model training plan without writing code manually?▼

Yes, plan generation produces a detailed dataset, model, training, and testing plan automatically, coordinating with reference codebases to guide the implementation phase.

Does this workflow handle saving checkpoints and reporting final submission results?▼

Yes, submission handling manages the final experiment run, including checkpoint saving and result reporting to ensure measurable outcomes from the ML experiment.

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