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Found 945 Skills
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.**
Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.
Claude AI cookbooks - code examples, tutorials, and best practices for using Claude API. Use when learning Claude API integration, building Claude-powered applications, or exploring Claude capabilities.
Master React Native 0.79.5 components, styling, performance optimization, and mobile UI best practices with real-world examples
Write viral, persuasive, engaging tweets and threads. Uses web research to find viral examples in your niche, then models writing based on proven formulas and X algorithm optimization. Use when creating tweets, threads, or X content strategy.
Talk to Alex Hormozi about their expertise. Alex Hormozi provides authentic advice using their mental models, core beliefs, and real-world examples.
Look up VoltAgent documentation embedded in node_modules/@voltagent/core/docs for version-matched docs. Use for API signatures, guides, and examples.
Android APK unpacking and resource extraction tool for reverse engineering. Use when you need to decode APK files, extract resources, examine AndroidManifest.xml, analyze smali code, or repackage modified APKs.
Generates documentation for Terraform modules using terraform-docs tool to auto-generate README files with input/output tables, usage examples, and requirements. This skill should be used when users need to document Terraform modules, create or update README files, or maintain consistent module documentation.
Write and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Claude, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.
Use when you need complete SF Symbols API reference including every rendering mode, symbol effect, configuration option, UIKit equivalent, and platform availability - comprehensive code examples for iOS 17 through iOS 26
Performs structural code search and refactoring by matching code structure instead of exact text. Use when editing code structure with text matching ambiguity, handling "old_string not unique" problems, or performing formatting-independent pattern matching across function signatures, method calls, and class structures