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Found 920 Skills
This skill is strict implementation instruction, not advisory reference text. The skill treats the HTML as discovery-only input, forces interactive Playwright route/state capture, then moves through scored gates for source acceptance, implementation planning, authored UI reproduction, implementation integrity, visual verification, and adversarial proof before signoff.
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.
Investigate why a Duvo Job failed or produced the wrong outcome. Use when the user shares a failed Job, asks "why did this Job fail", or wants to fix a recurring failure on an Assignment. Reads the Job's transcript and the Build that was active for it via the Duvo public API, names the root cause from a fixed failure-mode taxonomy, and proposes one concrete fix — handing off to sop-writer for any SOP rewrite.
Discover article URLs from https://www.eceee.org/all-news/ and extract/persist full article text into SQLite with retry-safe incremental sync. Use when building or maintaining an eceee news fulltext corpus for downstream search, indexing, or summarization.
Polish raw screenshots into LP-ready heroes.
Review contracts and legal agreements (PDF, Word, images) for risks, unfair clauses, missing provisions, and key obligations using SoMark for accurate document parsing. Provides structured risk analysis with severity ratings. Requires SoMark API Key (SOMARK_API_KEY).
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.**
Decide where files live in an ML experimentation project: reusable code in `src/<pkg>/`, one `# %%` script per experiment in `experiments/`, design notes + index in `journal/`, reports in `reports/`, agent-only probes in `scratch/`, narrative digest in `overview/summary.md`. Owns the layout, the file-creation rules (one file per experiment, ask before editing), and the jupytext `# %%` script convention. Never imposes `data/` — the user owns that. TRIGGER — any of: - Starting a new ML project / scaffolding a workspace. - About to create the first experiment file in a project. - About to create `src/<pkg>/data.py` / `features.py` / `pipeline.py` / `evaluate.py` for the first time. - About to write a `.ipynb` for experimentation — redirect to a `# %%` script under `experiments/`. - User asks where something should live, how to organize the project, or how to set up the workspace. - About to add a new experiment iteration — decide new file vs edit existing (ask the user). SKIP when: the file is clearly part of an already-populated module (e.g., adding a function to existing `features.py`); pure refactor inside a single existing file; pipeline declaration mechanics (`build-ml-pipeline`); evaluation mechanics (`evaluate-ml-pipeline`); skore symbol lookup (`python-api`). HOW TO USE: **first run the Detection table** below — if any signal matches, glue to existing conventions (do not rename or move folders). If no signal matches, scaffold the default layout. **Emit the Pre-flight checklist as visible text and read the Stop conditions before any file is created or edited.** Use templates in `templates/`; copy and adapt, do not rewrite from scratch.
Audit an AI agent skill for security risks before installing or trusting it. Runs a deterministic scanner (regex patterns, Python AST analysis, source-to-sink taint tracking, and YARA signatures) and then reasons about intent — catching prompt injection, credential exfiltration, persistence, memory poisoning, malicious code, supply-chain risks, and description-vs-behavior mismatch. Make sure to use this skill whenever the user wants to scan, audit, vet, review, or check the safety of a skill, plugin, SKILL.md, or agent tool — whether it is a local folder, a zip/.skill file, or a cloned repo — and whenever someone asks "is this skill safe to install?".
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.
Model Selection and Recommendation for Alibaba Cloud Tongyi Wanli. Activated when users need to "select, recommend, compare" models, or describe an AI scenario/functional requirement (implying the need to decide which model to use). The core intention is to help users make decisions, not just provide information. Trigger words: recommend model, which one to choose, which is suitable, compare, build a XX, implement XX function, which model is good to use, XX scenario solution. When users involve both model query and model selection at the same time, prioritize using this skill (this skill will read model data internally to complete the recommendation).
Analyze and implement purposeful UI animations for Next.js + Tailwind + React projects. Use when user asks to add animations, enhance UI motion, animate pages/components, or improve visual feedback. Triggers on "add animations", "animate UI", "motion design", "hover effects", "scroll animations", "page transitions", "micro-interactions".