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Skill

explore-code

@lllllllama 357k installs Trending #194 on Skills.Sh Updated 2026-09-23

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.

agent-skillsai-agentsdeep-learningreproducibilityresearch-tools

Install

With your agent

Install OMGSkills skill "lllllllama/rigorpilot-skills:explore-code" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.

Manual command

git clone https://github.com/lllllllama/RigorPilot-Skills /tmp/RigorPilot-Skills && ln -s /tmp/RigorPilot-Skills/skills/explore-code ~/.claude/skills/explore-code

From README

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.