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…
- installs 8w
- 160,052
- 30-day movement
- starts with the next reading
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A Claude Code skill for auditable exploratory implementation in deep learning research repositories. It guides an agent through isolated-branch code changes such as module transplants, backbone adaptations, LoRA/adapter additions, or head replacements, recording outputs in explore_outputs/ for rollback and review.
You want an agent to make exploratory model changes without contaminating a trusted baseline, with documented change records.
Use it to
- Transplant modules between research codebases on an isolated branch
- Add LoRA or adapter layers to an existing backbone
- Replace a model head and document the changeset
- Produce a comparability report for exploratory edits
- Prepare rollback-aware records before migrating to the baseline
For Deep learning researchers authorizing exploratory code changes in their repositories
- Host repository
- lllllllama/RigorPilot-Skills
- Installs, lifetime
- 311k
- Installs, 8 weeks
- 160k
- Host stars
- 489
- Host language
- Python