explore-code
π―SkillA skill that helps you try small, low-risk code changes in a deep learning research project on a separate branch. It records why each change matters and how to undo it.
Installation
npx skills add lllllllama/rigorpilot-skills --skill explore-codeRun it in a terminal; it asks which AI coding tools to install it for (Claude Code, Codex, Cursor and more).
Uses skills.sh Β· Node.js 22.20+
After installing
Ask for related work and the agent uses it on its own, or call it yourself.
/explore-codeInstall by hand
Copy the whole skill folder from GitHub to the place below. Copying only SKILL.md can leave out files the skill uses.
Open the skill folder β- This project
.claude/skills/explore-code/ - All projects
~/.claude/skills/explore-code/
These paths are for Claude Code. For other tools, the command above puts it in the right place.
What this skill does
Overview
explore-code is the Rigor Improve implementation skill. When you clearly give permission, it makes trial code changes on an isolated branch or worktree (a separate working copy of the code). Your trusted baseline (the version you know works) stays untouched, and every result is treated as a candidate to test, not as a proven improvement.
Key Features
- Copy a module from existing source code into your model with as few edits as possible.
- Adapt a backbone (the main part of a model), add LoRA or adapter layers (small add-on layers), or replace the model's head (the last part that gives the output).
- Get a record of why each change is meaningful, how to roll it back, and why it is still only a candidate.
- Hand off the actual runs to
minimal-run-and-auditorrun-train. - Find
CHANGESET.md,SCIENTIFIC_CHANGELOG.md,COMPARABILITY_REPORT.md,TOP_RUNS.md, andstatus.jsoninexplore_outputs/.
Who is this for?
Use it when you want to test an idea, such as adding LoRA layers to an existing model, without touching the version you trust. It is not for reproducing a baseline, debugging, large refactors, or building an idea from scratch.
Summarized by AI from this skill's SKILL.md. Suggest a better one
SKILL.md
View on GitHub βRigor 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.
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.
When not to apply
- When the request is for trusted baseline work, conservative debugging, or normal training execution.
- When the user did not explicitly authorize exploratory modifications.
- When the task is a broad refactor or a from-scratch idea implementation.
Clear boundaries
- This skill owns exploratory code modifications only.
- It must keep work isolated from the trusted baseline.
- Use
ai-research-exploreinstead when the task spans both current_research coordination and exploratory runs. - It may hand off execution to
minimal-run-and-auditorrun-train. - It should favor source-anchored copying and minimal adaptation over freeform rewrites.
- It should record why a candidate change is meaningful, how to roll it back, and why it remains a candidate rather than a verified contribution.
Output expectations
explore_outputs/CHANGESET.mdexplore_outputs/SCIENTIFIC_CHANGELOG.mdexplore_outputs/COMPARABILITY_REPORT.mdexplore_outputs/TOP_RUNS.mdexplore_outputs/status.json
Notes
Use references/explore-policy.md, ../ai-research-reproduction/references/research-rigor-principles.md, scripts/plan_code_changes.py, and scripts/write_outputs.py.
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