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Found 1,666 Skills
Authoring & setting up Rust projects — idiomatic Rust (ownership/borrowing/cloning patterns, Result error handling, clippy config, static vs dynamic dispatch, performance, doc tests) plus project scaffolding (Cargo.toml, multi-crate workspaces, CI pipelines, rustfmt). Use when writing Rust code or starting/restructuring a Rust project.
AI-powered content automation pipeline that researches, generates scripts, and creates videos automatically using Claude/OpenAI and Remotion
Automated AI content pipeline for research, scriptwriting, and video generation using Claude/OpenAI and Remotion
Creates comprehensive GitHub Actions CI/CD workflows for linting, testing, building, and deploying. Includes caching strategies, matrix builds, artifact handling, and failure diagnostics. Use for "GitHub Actions", "CI pipeline", "workflow automation", or "continuous integration".
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.
Unity shaders, materials, and rendering pipelines (URP/HDRP/Built-in). PROACTIVELY activate for: (1) writing shaders in Shader Graph, HLSL, or ShaderLab, (2) URP and HDRP shader authoring, (3) custom render pipeline work (SRP), (4) lighting setup (baked vs realtime, lightmaps, Global Illumination), (5) post-processing stacks, (6) reflection probes and light probes, (7) custom render features and full-screen passes, (8) shader stripping and variant management, (9) compute shaders, (10) ray tracing in HDRP. Provides: Shader Graph templates, HLSL snippets, URP/HDRP differences, lighting setup recipes, render-feature examples, and shader-variant guidance.
Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and conventions. For testing and debugging DAGs, see the testing-dags skill.
Expert deployment engineer specializing in modern CI/CD pipelines, GitOps workflows, and advanced deployment automation. Masters GitHub Actions, ArgoCD/Flux, progressive delivery, container security, and platform engineering. Handles zero-downtime deployments, security scanning, and developer experience optimization. Use PROACTIVELY for CI/CD design, GitOps implementation, or deployment automation.
Data validation patterns including schema validation, input sanitization, output encoding, and type coercion. Use when implementing validate, validation, schema, form validation, API validation, JSON Schema, Zod, Pydantic, Joi, Yup, sanitize, sanitization, XSS prevention, injection prevention, escape, encode, whitelist, constraint checking, invariant validation, data pipeline validation, ML feature validation, or custom validators.
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.