Loading...
Loading...
Found 741 Skills
Forensic audit of the user's recent Claude Code sessions to surface step-change workflow improvements — not marginal ones. Use when the user asks to "audit my Claude Code sessions", "analyze how I use Claude Code", "find patterns in my usage", "improve my Claude Code workflow", "review my sessions", "find leverage in my setup", or wants to understand where their Claude Code setup is leaking time. Samples dozens of real transcripts, extracts quantitative signal via scripts, uses parallel subagents for deep reads, then synthesizes into a short prioritized report with drafted implementations (new skills, CLAUDE.md rules, hooks, settings diffs) that the user can install directly. Trigger even when the user doesn't say the word "audit" — if they're asking about improving or reviewing their Claude Code habits at scale, use this skill.
Audit a Duvo Assignment — or a multi-Assignment workflow connected by a Case Queue — across many Jobs to find systemic inefficiencies and quality issues, then recommend concrete SOP and architecture changes. Use when the user asks to "analyze this workflow", "audit my Assignment", "why is this Assignment slow / inconsistent / low quality across runs", "why does my queue keep backing up", or wants a health check over an Assignment's recent Jobs — as opposed to debugging one failed Job (that's job-debugger). Reads recent Jobs, eval scores, the producer/consumer queue topology, and the SOPs those Jobs actually ran against via the Duvo public API; hands off to sop-writer for any SOP rewrite.
Official Roblox Studio MCP server tools reference. What each tool does, how to use it, reliability patterns, and workflows for script editing, building, playtesting, and debugging. Use when the AI has an MCP connection to Roblox Studio.
Read-only Storage Analysis Assistant for macOS / Windows (auto-detects system). Scans the entire disk usage to identify space hogs, categorizes each item into three levels: 🟢 Auto-cleanable / 🟡 Manual judgment required / 🔴 Clean with caution, and provides actionable disposal plans. Generates an interactive HTML report with beautiful formatting, collapsible sections, and one-click copy commands. Also supports starting a local service to delete files directly via the web (move to trash / delete immediately). The entire scanning process is read-only. Must be used in the following scenarios: When users mention "storage analysis", "disk full", "C drive/hard disk full", "insufficient space", "clean up space", "disk cleanup", "space occupied", "what's taking up space", "help me check storage", "check computer storage/space", "storage space", "computer space insufficient", "memory full/insufficient" (in Chinese colloquial, "memory" often refers to storage), "storage analysis", "disk cleanup", "clear cache", "disk cleanup"; or when users complain about insufficient computer space, want to know what's taking up hard disk space, or need cleanup suggestions. Note: If users explicitly refer to RAM (e.g., "which process is using memory", "high memory usage", want to see Activity Monitor), that's RAM, not storage, and does not belong to this skill.
Rules and worked examples for writing prose that does not read like AI-generated slop. Consult before writing or editing any prose.
SOP for Vehicle Entry/Exit Operation Process of AIKE Smart Parking Open Platform (End-to-end Orchestration for Entry/Exit). This skill is applicable when users want to complete the full entry/exit of a vehicle, simulate the real physical entry/exit process, run through the entire link of 'Supplement/Capture Entry → Correction' or 'Exit Capture → Correction → Fee Inquiry → Payment', or ask questions like 'How does a vehicle enter/exits the parking lot?', 'Entry/exit process', 'Entry/exit SOP', 'How to move a vehicle into/out of the parking lot', 'Simulate vehicle entry/exit', or encounter stuck/debugging issues during entry/exit (e.g., vehicle fails to enter after capture or correction, capture returns error code 908, unsure how to proceed to the next step). This skill is a cross-domain orchestration layer that connects multiple commands from parking (supplement/correction/inventory), device (capture), and trade (fee inquiry/payment), and clarifies cross-command hard constraints (paired exit, capture device, channel release mode, token validity) and failure handling. Boundary: For querying a single record, checking on-site vehicles, locking vehicles, or calling a single command, please directly use the corresponding domain skills openydt-record / openydt-device / openydt-billing instead of this process skill.
Use when babysitting a PR/MR until CI is green and every valid reviewer feedback is addressed — supports GitHub PR (gh) and GitLab MR (glab), triages comments into Valid / Discuss / Out-of-scope, addresses valid items with small commits and inline thread replies, escalates invisible findings (SonarQube/Snyk dashboards) and 3-round bot deadlocks, reports ready-to-merge (never auto-merges). Triggers — '監看 PR', 'babysit PR/MR', 'PR 顧到 merge', 'address review feedback', 'wait until CI green', '把 PR 顧到綠'. NOT for writing PR descriptions, NOT for diff code review (use pr-review), NOT for actually merging the PR (user does that).
Owns Python code style for this stack: ruff for lint + format, numpydoc for docstrings. Two responsibilities — (1) place the project's `ruff.toml` from the bundled template once the stack and workspace are in place, and (2) run ruff against any Python files Claude has just generated or edited. Stops at "the touched files pass `ruff check`." TRIGGER when (any of these): (1) a Python file was just created or edited via Write / Edit / MultiEdit — invoke this skill before declaring the task done so ruff is run on the touched files; (2) a fresh ML workspace was just scaffolded by `organize-ml-workspace` and the project has no `ruff.toml` at its root yet — drop the bundled template; (3) the user asks about lint, format, docstring style, or reaches for `black` / `isort` / `flake8` / `pydocstyle` (redirect to ruff — the stack's canonical linter, owned by `data-science-python-stack` Tier 1). SKIP when: the project is non-Python; the only edits in this turn are to Markdown / TOML / JSON / YAML; the file lives in a third-party vendored directory the user doesn't own. HOW TO USE: run ruff manually on the files you just touched — do not configure a PostToolUse hook for this. **Read the "Stop conditions" block and emit the Pre-flight checklist as visible text in your response — both are mandatory before running ruff.**
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Interactive prompt studio for HappyHorse 1.0 video generation. Guides users through scenario discovery with vivid examples, then assembles production-ready prompts in JP/CN/EN. Use when someone wants to create AI video content with HappyHorse but doesn't know where to start, or when they have a specific scenario and need a polished prompt. Covers manga drama, character PV, manga motion, virtual idol MV, and free-form scenarios.
Diagnose and audit SEO issues affecting crawlability, indexation, rankings, and organic performance. Use when the user asks for an SEO audit, technical SEO review, ranking diagnosis, on-page SEO review, meta tag audit, or SEO health check. This skill identifies issues and prioritizes actions but does not execute changes. For large-scale page creation, use programmatic-seo. For structured data, use schema-markup.
Use when turning a dbt Core project into an Airflow DAG/TaskGroup using Astronomer Cosmos. Does not cover dbt Fusion. Before implementing, verify dbt engine, warehouse, Airflow version, execution environment, DAG vs TaskGroup, and manifest availability.