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Found 808 Skills
Use this skill whenever the user wants to transcribe audio to text, convert speech to text, or get a transcript from an audio or video file. Triggers include: any mention of 'transcribe', 'transcription', 'speech to text', 'STT', 'convert audio to text', 'what does this audio say', 'get transcript', 'subtitle generation', or requests to extract spoken words from a file. Also use when the user wants speaker identification from audio, timestamps for captions, or multilingual transcription.
Best practices and rules for securing FiveM resources against cheaters and exploits. Use this skill when writing or reviewing server-side and client-side code to ensure malicious events, unauthorized entity creations, and client trust issues are prevented. Focuses on strict server authority and safe event handling.
Retrieve year-over-year growth in balance sheet items including Total Assets, Total Liabilities, Shareholders Equity, Cash, and Inventories. Use when analyzing company financial position trends, capital structure changes, or liquidity management.
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.
Deploy and operate Infisical self-hosted instances with Docker, Docker Compose, and Kubernetes. Covers architecture, environment variables, ENCRYPTION_KEY management, database setup, Redis configuration, production hardening, FIPS compliance, scaling, and high availability patterns.
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.
Generate matching visuals from text via Picsart gen-ai.
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.**
Share markdown reports to the user's configured Slack agent_cli_report channel via Fastfold API, and persist the markdown as a library item.
Generate and transcribe speech using Google's Gemini-TTS and Chirp 3 models. Supports Text-to-Speech (Single/Multi-speaker), Instant Custom Voice, and Speech-to-Text (Transcription/Diarization).
Help address review/issue comments on the open GitLab MR for the current branch using glab CLI. Use when the user wants help addressing review/issue comments on an open GitLab MR