npx agentspec-cli install 5d3f27eb-c23a-4dd0-8e16-fb8236055e7f --framework ClaudeCode→ .claude/skills/analyze-project/SKILL.md
# analyze-project Use this as the Rigor Analyze / Rigor Audit read-only skill. The installed slug remains `analyze-project` for compatibility. Use the shared operating principles in `../ai-research-reproduction/references/agent-operating-principles.md`; this skill should guide read-only analysis without constraining the model's project-specific reasoning. ## When to apply - The user wants to understand a deep learning repository before changing it. - The user needs a map of model structure, training entrypoints, inference entrypoints, and config relationships. - The user wants conservative suggestions about likely insertion points or suspicious implementation patterns. - The user explicitly wants read-only analysis and not heavy execution. ## When not to apply - When the main task is to execute a failing command or debug a traceback. - When the user wants environment setup or asset download only. - When the user wants speculative adaptation or broad exploratory patching. - When the task is a general literature summary without repository analysis. ## Clear boundaries - This skill is read-mostly. - It may run lightweight static inspection helpers. - It does not patch repository code. - It does not own final reproduction outputs. - It should mark suspicious patterns as heuristics, not confirmed bugs. ## Output expectations - `analysis_outputs/SUMMARY.md` - `analysis_outputs/RISKS.md` - `analysis_outputs/status.json` ## Notes Use `references/analysis-policy.md` and the shared `../ai-research-reproduction/references/research-pitfall-checklist.md`.
Built on the Agent Skills standard
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.
by AgentHub Crawler
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
by AgentHub Crawler
Sign in to leave a comment.
safe-debug
Rigor Debug / Rigor Audit skill for deep learning research work. Use when the user pastes a traceback, terminal error, CUDA OOM, checkpoint load failure, shape mismatch, NaN loss symptom, or training failure and wants conservative diagnosis before any patching, with debug fixes clearly separated from research contributions. Do not use for broad refactoring, speculative adaptation, automatic exploratory patching, or general repository familiarization.
by AgentHub Crawler