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explore-run

Plan and summarize isolated exploratory deep learning experiment runs.

515|11|Updated Mar 30, 2026
Search Tags:#planning#research#variants#exploratory#experiment-management#outputs
One-click install
npx skills add https://github.com/lllllllama/ai-research-workflow-skills --skill explore-run
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: explore-run
Source: https://github.com/lllllllama/ai-research-workflow-skills/tree/main/skills/explore-run
Command: npx skills add https://github.com/lllllllama/ai-research-workflow-skills --skill explore-run

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill helps researchers plan and summarize isolated exploratory runs for deep learning projects, ensuring explicit authorization and safe separation from trusted baselines.

Core Features & Use Cases

  • Plan and rank exploratory variants for small-subset validations, quick-cycle sweeps, or idle-GPU searches, with results stored in explore_outputs/ (CHANGESET.md, TOP_RUNS.md, status.json).
  • Integrate with execution policy guidance in references/execution-policy.md and use plan_variants.py and write_outputs.py to generate and publish results.
  • Use-case: when you need candidate-only exploration results that can be reviewed before any trusted deployment.

Quick Start

Plan an explicit exploratory run for the current research task and generate the explore_outputs bundle with CHANGESET.md, TOP_RUNS.md, and status.json.

Frequently Asked Questions about explore-run

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

FAQPage Schema
How do I plan isolated exploratory runs for deep learning experiments without affecting trusted baselines?▼

Plan isolated exploratory runs by defining candidate-only variants for small-subset validations or quick sweeps, then execute them with results written exclusively to explore_outputs/ to keep experiment state safely separated from trusted baselines.

What is the best way to summarize short-cycle sweep results for transfer learning trials?▼

Summarize short-cycle sweep results by generating an outputs bundle containing CHANGESET.md, TOP_RUNS.md, and status.json, which captures variant rankings and run status for review before any trusted deployment.

Can I use this for batch experiments and idle-GPU searches in my research repository?▼

Yes, batch experiments and idle-GPU searches are supported by planning candidate-only explorations that leverage execution-policy notes to ensure explicit researcher authorization and safe isolation within the research repository.

How does variant planning work for small-subset validations?▼

Variant planning coordinates candidate selection and scoring for small-subset validations by consulting execution-policy guidance and generating ranked outputs, ensuring exploratory results remain isolated for explicit researcher review.

Do I need specific scripts to generate explore outputs for my deep learning research?▼

You need plan_variants.py to coordinate variant planning and write_outputs.py to generate the explore_outputs bundle, while consulting execution-policy notes to maintain explicit authorization and safe experiment isolation.

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