Individual skill

explore-code

Rigor Improve implementation leaf skill for auditable candidate implementation in deep learning research repositories. Use when the researcher explicitly authorizes exploratory work on an isolated branch or worktree to transplant modules, adapt a backbone, add LoRA or adapter layers, replace a head, or stitch together meaningful low-risk migration ideas with rollback-aware records in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline reproduction, conservative debugging, environment setup, verified contribution claims, or default repository analysis.

skills/explore-code/SKILL.md

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Observed behavior: Local scripts
Last checked: Nov 10, 2026

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Repository stars (not skill ratings): 486
Repository last updated: Sep 23, 2026
License: MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

agent-skills Repository topic
ai-agents Repository topic
deep-learning Repository topic
reproducibility Repository topic
research-tools Repository topic
testing Derived category
research Derived category
education Derived category
testing-browser Derived category

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SKILL.mdcore · 2,592 bytes
agents/openai.yamlasset · 326 bytes
references/explore-policy.mddoc · 652 bytes
scripts/plan_code_changes.pyruns · 14,526 bytes
scripts/write_outputs.pyruns · 1,186 bytes

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# explore-code

Use this as the Rigor Improve implementation leaf skill. The installed slug
remains `explore-code` for compatibility.

Use the shared operating principles in
`../ai-research-reproduction/references/agent-operating-principles.md`; this skill should guide
bounded candidate code work without over-prescribing implementation details.

## When to apply

- When the researcher explicitly authorizes exploratory code changes on an isolated branch or worktree.
- When the task is source-anchored module transplant, backbone adaptation, LoRA or adapter insertion, or low-risk module combination.
- When summary-level recording is sufficient and the result is a candidate, not a trusted conclusion.