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Foundation for writing Estuary derivations — what they are, language choice (SQL/TypeScript/Python), project layout, flowctl workflow, stateless vs stateful shuffling, and universal gotchas. Read this before any other derivation-* skill. Use when user says "write a derivation", "how do derivations work", "create a derivation", "derivation setup", or before starting any transformation work.
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Create an Estuary derivation that maintains a sliding time window of events (e.g. "transfers in the last 24h", "failed logins in the last 5 minutes") using internal SQLite state and readDelay for time-based expiration. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "windowing derivation", "derivation for last-24h events", "rolling-window derivation", "sliding-window transformation", "fraud-detection derivation", "rate-limit derivation", or "time-window derivation".
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Implements BLoC (v9.x) event-driven state management for Flutter with unidirectional data flow using the flutter_bloc package. Use when implementing Bloc/Cubit classes, mapping Events to States, consuming state with BlocBuilder/BlocListener/BlocConsumer, applying event transformers (restartable/droppable/sequential) for concurrency control, setting up BlocProvider/MultiBlocProvider DI, integrating Freezed for sealed union types and exhaustive pattern matching, or testing with blocTest. Ideal for enterprise apps requiring business logic separation, precise concurrency control (search debouncing, form deduplication), or event replay debugging.
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Implements data visualization charts in Flutter using the fl_chart package (v1.2.0). Supports LineChart, BarChart, PieChart, ScatterChart, RadarChart, and CandlestickChart with implicit animations, touch interactivity, and pan/zoom transformations. Use when building any Flutter chart, graph, or data visualization — including line graphs for trends, bar charts for comparisons, pie/donut charts for proportions, scatter plots for distributions, radar charts for multi-axis comparisons, or candlestick charts for financial OHLC data. Also use when customizing chart axes, titles, tooltips, grid lines, touch callbacks, chart animations, or chart zoom/pan behavior.
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WAAPI and compositing: production-level GPU-friendly transform/opacity, offscreen pausing. Use when implementing, reviewing or testing waapi and compositing for React, Vite, Next.js or Perfect_AI projects.
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Optimize and convert Zenn Flavored Markdown (ZFM) articles to Dev.to (Forem) compatible Markdown. Use this skill whenever the user wants to post a Zenn article to Dev.to, needs to convert markdown for cross-posting, or asks to "optimize for dev.to". It handles front matter conversion, Liquid tag embeds, KaTeX math blocks, callout transformations, and general formatting adjustments required by Dev.to.
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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 expl...
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Use before creative or constructive work (features, architecture, behavior). Transforms vague ideas into validated designs through disciplined reasoning and collaboration.
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Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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Transforms user prompts into optimized prompts using established frameworks (RTF, RISEN, Chain of Thought, RODES). Analyzes intent, selects framework, and produces structured, ready-to-use prompts.
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Manage database migrations and schema versioning. Use when planning migrations, version control, rollback strategies, or data transformations for any database paradigm.
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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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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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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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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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Transform rough UI requests into structured Stitch prompts with platform, layout, component, and design-system context.
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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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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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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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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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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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Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)
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