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Browse reusable Agent Skills, each with a clear purpose and practical guidance.
reviewdocs
Review a PR's end user documentation updates.
kedro-docs-draft-writer
Draft or polish Kedro documentation. Writes a first draft from source code or a branch, or polishes an existing draft the user provides. Follows the Kedro style guide, places new files in the correct docs/ path, and runs Vale before handing back. Use when the user says "draft docs for X", "write a how-to for X", "document this feature", "polish my draft", or wants to beat blank-page paralysis on any Kedro docs page.
kedro-security-review
Run a Kedro security scan on the full codebase or just a pull request. This skill always runs Semgrep first and then evaluates the findings against the Kedro security model. Use when the user says things like "run security scan on the full codebase" or "run security scan on this PR". Produce one final report only, in chat by default or posted to GitHub when explicitly asked.
review-kedro-pr
Review a Kedro PR for checklist compliance, architecture, correctness, and clarity. Optionally post findings as a GitHub PR comment. Use when the user asks to review a PR, review this PR, or do a PR review.
lancedb-connect
Resolve how to connect to a LanceDB deployment over the REST API — figure out the base URL, API key, and database header. Use this before making any REST requests to a LanceDB table, whenever the endpoint or auth setup is not already known. Also useful on its own when someone asks how to connect, authenticate, or curl their LanceDB instance.
lancedb-update-lance-dependency
Update LanceDB to a specific Lance release or tag. Use when bumping Lance dependencies in the lancedb repository, including Rust workspace Lance crates, Java lance-core, validation, branch creation, commit, push, and PR creation when requested.
skia-add-test
Add a new test to the Skia repository. Use this skill when you need to create a new unit or integration test in the tests/ directory and ensure it's part of the build system.
hf-cloud-serving-image-selection
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.
huggingface-trackio
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.