How do I install the Explore Code skill?

Run npx skillmds@latest add lllllllama/explore-code in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Explore Code safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Explore Code?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Explore Code free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Explore Code?

lllllllama (@lllllllama) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Explore Code by lllllllama — Agent Skill

Name: Explore Code
Rating: 4.2 (5 reviews)
Author: lllllllama

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.

lllllllama Updated Sep 9, 2026

File contents

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-explore instead when the task spans both current_research coordination and exploratory runs.
It may hand off execution to minimal-run-and-audit or run-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.md
explore_outputs/SCIENTIFIC_CHANGELOG.md
explore_outputs/COMPARABILITY_REPORT.md
explore_outputs/TOP_RUNS.md
explore_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.

---name: explore-codedescription: 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.--- # 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-explore` instead when the task spans both current_research coordination and exploratory runs. - It may hand off execution to `minimal-run-and-audit` or `run-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.md` - `explore_outputs/SCIENTIFIC_CHANGELOG.md` - `explore_outputs/COMPARABILITY_REPORT.md` - `explore_outputs/TOP_RUNS.md` - `explore_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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