Guides AI through the end-to-end task submission workflow for the claude-config repo: (1) initializing a task workspace with new_task.sh, (2) committing and pushing to v2 and main branches, (3) deploying to the remote GPU server via ssh. Trigger when user says 'submit', 'push', 'deploy', 'version bump', 'push to main', or asks to commit and push changes to this repo.
Enforces just-in-time term definition in technical writing — every name / file path / acronym / black-box term gets explained the moment it first appears (one line, parenthetical, inline), and re-explained whenever it reappears after a long gap. **NEVER** front-load a glossary at the top of a document; **NEVER** assume the reader has already read 100 turns of context. Trigger this skill aggressively whenever (1) the user complains 'I don't understand / 听不懂 / 说人话 / 给我说人话 / 你在说啥 / 这是啥 / 黑话 / 别用术语 / 操你妈的看不懂', (2) the user pushes back on a previous reply with 'what is X' / 'what does X mean' / '这个 X 是什么', (3) you're writing any technical report / diagnosis / plan / postmortem / architecture doc that mentions internal file paths, scripts, hooks, FSM states, variable names, or any term that wasn't in the user's original prompt verbatim, (4) you're about to write a 'Glossary' or 'Definitions' section at the top of a document — STOP, this skill says don't. Over-trigger rather than under-trigger; the cost of one extra
Audits whether the current code is already running at maximum speed by checking GPU/CPU/IO bottleneck saturation. Red-alarms if NONE of GPU/CPU/IO is bottlenecked — that proves speedup is mathematically possible and the code is wasting compute. Loads 5 sub-skills progressively. Trigger this skill aggressively whenever the user asks about speed, performance, throughput, latency, "why is this slow", "is my code optimal", "can I make this faster", "find the bottleneck", profiling, optimization, GPU utilization, dataloader speed, training speed, inference speed, 性能, 加速, 瓶颈, 慢, 优化, 训练慢, 推理慢, even when the user doesn't explicitly use the word "audit". Also trigger when reviewing training scripts, inference scripts, or any data-processing pipeline for efficiency. Skip only if the user clearly asks about correctness, not speed.
Document-level rigour auditor. Enforces that every term, symbol, abbreviation, experimental label, and formula appears with its definition adjacent to its first occurrence — and re-states it on distant reuse. Also enforces no-step-skipping in math derivations. Invoke whenever writing or reviewing a technical report, paper explanation, or derivation where a reader could encounter an undefined token. Pairs with math-explain (single-formula checklist) and zero-jump-check (inter-step seam auditor) as the outer document-level layer of the rigorous-writing stack.
Generate a structured, navigable code documentation document for a Python codebase — the 'code tree'. Use this skill whenever the user asks to document a codebase, explain how the code works end-to-end, generate a code tree, write code documentation, understand call chains, or map out how functions relate to each other. Trigger on: '生成代码树', '代码树', '给我写代码文档', '解释代码结构', '画一个调用树', 'generate a code tree', 'document the codebase', 'write code docs', 'explain how the code works', 'map out the call chains', 'code documentation'. The output is a single Markdown file with: (1) directory tree, (2) ASCII call trees per functional area, (3) per-function docs with always-visible input/output descriptions and collapsed technical details. After generating, the skill runs ask-claude to verify format quality and fixes issues.
Generate a project-specific draft_simplify.md for a code repository — a high-level plan for an upcoming maintainability refactor that preserves runtime behavior bit-for-bit. Use this skill whenever the user wants to PLAN a code-cleanup / refactor / maintainability pass on a whole repository, before any code is changed. Trigger aggressively on phrases like '生成simplify draft', '写一下化简方案', '重构方案', '生成可维护性化简的draft', '帮我规划一下重构', 'plan a refactor', 'draft a simplify plan', 'generate a refactor plan for this repo'. The skill does NOT execute the refactor — it only produces the planning document. The output is a single instantiated draft_simplify.md at the repo root, with all placeholders filled from a mandatory AskUserQuestion intake. Do NOT trigger for: single-PR review (use /simplify), one-shot small edits, or when the user explicitly asks to start refactoring immediately. Do NOT confuse with /gen-draft (which is the generic draft generator) — this skill is specialized: the output draft already contains the full si
Reference card for a single-node SLURM GPU server. General template — fill in all {{PLACEHOLDER}} values in FILL_IN.md before use. Use when the user invokes /this-cluster-general, or automatically before: running local commands on this box, submitting/cancelling SLURM jobs, writing sbatch scripts, choosing a Python interpreter, deploying code locally, or reading job logs. Also consult when other skills need local machine facts. Triggers on: /this-cluster-general, sbatch, squeue, scancel, sinfo, SLURM, 本机, 这台机子, 这台服务器, 提交任务, 跑实验, gpu partition, 本地部署, 本地提交.
Spawn a /loop 30m task that monitors the active humanize RLCR loop and any background shells launched in this session. Use ONLY when the user explicitly invokes it — e.g. '/humanize-watchdog', 'start humanize watchdog', 'monitor my humanize loop'. The watchdog checks every 30 minutes whether the RLCR loop is still progressing and whether any background shells in /tmp/claude-bg.log are stuck. It self-terminates when the humanize loop ends. Never trigger automatically; never run without an active humanize RLCR loop.