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概要と使いどころ
Exhaustive idea extraction through recursive breadth-before-depth exploration with Deep Think protocol and fractal structure. Use during /ideate to transform raw ideas into comprehensive, structured ideation output. Writes to a fractal folder hierarchy — every node has an index, CX file, and children.
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Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly
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Mamba-3 ist schneller als Transformer beim Decode und stärker als Mamba-2 — open source. Mein Qwen3.5 nutzt Hybrid-Mamba; ein Upgrade-Pfad auf Mamba-3
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Artificial intelligence-driven transformative applications in disease ... — In cancer diagnostics, AI systems have achieved breakthrough performances
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Architecture Matters We often conflate different AI paradigms: LLMs (Transformers): Predict token se
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Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs Published in arXiv , 2025 Abstract : Efficient deployment of 1-bit Large Language
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scikit-learn compatible Python toolkit for time series ML: classify, cluster, regress, segment, transform with 30+ algorithms (ROCKET, InceptionTime, KNN-DTW, HIVE-COTE, WEASEL). Handles panel, multivariate, and unequal-length series. Maintained successor to sktime. Alternatives: sktime (larger ecosystem), tslearn (fewer algorithms), catch22 (features only).
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Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.
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Transform rough UI requests into structured Stitch prompts with platform, layout, component, and design-system context.
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Delegate routine work to subagents — boilerplate generation, data transformation, file analysis, documentation drafting. Use when splitting tasks into independent subtasks for parallel subagent execution.
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Write, configure, and deploy Netlify Edge Functions (Deno runtime at the network edge) in TypeScript/JavaScript. Use when adding request/response manipulation at the edge — auth middleware, geolocation redirects, A/B testing and personalization, content localization, redirects/rewrites, SSR at the edge, or transforming responses — or when configuring path routing, response caching, or edge error handling. Triggers on tasks like "add auth middleware", "geo-based redirect", "A/B testing at the edge", "rewrite requests", or editing files in netlify/edge-functions.
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Transform, resize, crop, reformat, and optimize images on demand via Netlify Image CDN's /.netlify/images endpoint. Use when adding responsive images, generating thumbnails, converting formats (avif/webp/png), cropping to aspect ratios, tuning image quality, creating blurred placeholders, allowlisting remote image domains, serving user-uploaded images, or wiring framework image components (Next.js, Astro, Nuxt, Angular, Gatsby) to Netlify. Triggers on tasks like "optimize images", "add image thumbnails", "resize images on the fly", "serve images from an external domain", or "add blur placeholders".
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Use this skill any time a `.pptx` file is involved as input, output, or both. Covers reading decks, editing existing presentations, creating slides from scratch, visual QA, notes, layouts, and presentation-safe file transforms.
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Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when the active client should create a new image, transform an existing image, or derive visual variants from references, and route through only the image tool or approved API actually exposed by that host.
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Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
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You are a code education expert specializing in explaining complex code through clear narratives, visual diagrams, and step-by-step breakdowns. Transform difficult concepts into understandable explanations for developers at all levels.
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Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations
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This skill embodies the principles of "Clean Code" by Robert C. Martin (Uncle Bob). Use it to transform "code that works" into "code that is clean."
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Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.
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Catalog of MSBuild anti-patterns with detection rules and fix recipes. Only activate in MSBuild/.NET build context. USE FOR: reviewing, auditing, or cleaning up .csproj, .vbproj, .fsproj, .props, .targets, or .proj files. Each anti-pattern has a symptom, explanation, and concrete BAD→GOOD transformation. Covers Exec-instead-of-built-in-task, unquoted conditions, hardcoded paths, restating SDK defaults, scattered package versions, and more. DO NOT USE FOR: non-MSBuild build systems (npm, Maven, CMake, etc.), project migration to SDK-style (use msbuild-modernization).
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Patterns for managing MSBuild item groups: Include/Remove/Update semantics, item metadata, batching with %(Metadata), transforms, per-item filtering, and cross-product batching pitfalls. Only activate in MSBuild/.NET build context. USE FOR: diagnosing and fixing item group anti-patterns in .csproj files, reviewing item management for correctness, fixing CS2002 duplicate file warnings from SDK globbing, fixing targets that run more times than expected due to cross-product batching, fixing Include vs Update misuse on SDK-globbed items, fixing FileWrites registration for generated file clean support, moving generated files to IntermediateOutputPath. DO NOT USE FOR: target chain architecture (use target-authoring), property patterns (use property-patterns), incrementality (use incremental-build), general anti-patterns (use msbuild-antipatterns), non-MSBuild build systems.
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Transform debugging from frustrating guesswork into systematic problem-solving with proven strategies, powerful tools, and methodical approaches.
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