explore-code

深度学习研究仓库中的探索性实现叶子技能：在研究者明确授权且与可信基线隔离的前提下，进行源码锚定的模块移植、骨干适配、LoRA/adapter 插入或 head 替换等低风险候选改动，产出 CHANGESET、科学变更日志、可比性报告、候选运行记录与状态文件，并说明改动意义、回滚方式及为何仍属候选结论。

lllllllama

热度 0.00

最近更新 2026-10-18 15:53:52

分类：开发依赖：代码执行 / 读写文件应用场景：代码生成 / 研究 / 工作流 / 报告撰写

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add lllllllama/rigorpilot-skills/explore-code

Metadata

name: explore-code
description: 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.