npx agentspec-cli install 157ad2d0-f2e2-4e1b-9d9c-09b4d64c13b6 --framework ClaudeCode→ .claude/skills/safe-debug/SKILL.md
# safe-debug Use this as the Rigor Debug / Rigor Audit skill. The installed slug remains `safe-debug` for compatibility. Use the shared operating principles in `../ai-research-reproduction/references/agent-operating-principles.md`; this skill should guide conservative diagnosis without blocking the model from finding the local root cause. ## When to apply - The user provides a traceback, terminal error, or concrete training or inference failure symptom. - The user wants diagnosis, root-cause narrowing, and minimal patch suggestions before code is changed. - The user wants a safe debug flow with explicit human approval before mutation. ## When not to apply - When the user wants a broad repository walkthrough without an active failure. - When the task is speculative experimentation or code adaptation. - When the user is asking for a large refactor or readability rewrite. ## Clear boundaries - Diagnose first. - Do not modify repository code by default. - If a patch is needed, propose the smallest fix and require explicit approval first. - Escalate savepoint or branch creation before medium-risk or high-risk changes. - A debug fix is not automatically a research contribution; if it changes experiment meaning or comparability, say so explicitly. ## Output expectations - `debug_outputs/DIAGNOSIS.md` - `debug_outputs/PATCH_PLAN.md` - `debug_outputs/status.json` ## Notes Use `references/debug-policy.md`, `../ai-research-reproduction/references/research-rigor-principles.md`, and the shared `../ai-research-reproduction/references/research-pitfall-checklist.md`.
Built on the Agent Skills standard
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 reprod
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minimal-run-and-audit
Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.
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analyze-project
Rigor Analyze / Rigor Audit read-only skill for deep learning research repositories. Use when the user wants to read and understand a repository, inspect model structure and training or inference entrypoints, review configs and insertion points, or flag suspicious implementation patterns without modifying code or running heavy jobs. Do not use for active command execution, broad refactoring, speculative code adaptation, or automatic bug fixing.
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