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Found 1,832 Skills
Choose how and where to store football data. Use when the user asks about database choices, file formats, cloud storage, data pipelines, or how to organise their football data project. Also covers publishing and sharing outputs (Streamlit, Observable, GitHub Pages).
BMad Autonomous Development — orchestrates parallel story implementation pipelines. Builds a dependency graph, updates PR status from GitHub, picks stories from the backlog, and runs each through create → dev → review → PR in parallel — each story isolated in its own git worktree — using dedicated subagents with fresh context windows. Loops through the entire sprint plan in batches, with optional epic retrospective. Use when the user says "run BAD", "start autonomous development", "automate the sprint", "run the pipeline", "kick off the sprint", or "start the dev pipeline". Run /bad setup or /bad configure to install and configure the module.
Combine multiple forecasting models into ensemble predictions for improved accuracy. Use this skill when the user needs to improve forecast reliability, combine ARIMA/Prophet/ETS outputs, or build a robust forecasting pipeline — even if they say 'combine forecasts', 'model averaging', or 'which forecast should I trust'.
Uses MCP Connectors to read Gmail inbound leads, score them by ICP fit, draft personalized responses, and log qualified leads to your CRM. Turns your inbox into an automated pipeline.
Use this skill when applying visual effects to PixiJS v8 containers via the filter pipeline. Covers built-in filters (AlphaFilter, BlurFilter, ColorMatrixFilter, DisplacementFilter, NoiseFilter), custom Filter.from() with GLSL/WGSL, options (resolution, padding, antialias, blendRequired), filterArea optimization, pixi-filters community package. Triggers on: filters, BlurFilter, ColorMatrixFilter, DisplacementFilter, NoiseFilter, Filter.from, GLSL filter, pixi-filters, filterArea.
Designs production-grade RAG pipelines with chunking optimization, retrieval evaluation, and pipeline architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, designing embedding pipelines, or evaluating RAG performance with RAGAS metrics.
Grafana Tempo distributed tracing backend. Covers TraceQL query language (span selectors, attribute scopes, pipeline operators, structural operators, metrics functions), trace ingestion via OTLP/Jaeger/Zipkin, Tempo architecture (distributor/ingester/compactor/querier/metrics-generator), full configuration reference with YAML, metrics-from-traces (span metrics, service graphs, TraceQL metrics), deployment modes (monolithic/microservices/Helm/Kubernetes), multi-tenancy, performance tuning, caching, and HTTP API. Use when working with distributed traces, writing TraceQL queries, deploying Tempo, configuring trace pipelines, or setting up Grafana-Tempo integrations (traces-to-logs, traces-to-metrics, traces-to-profiles).
Builds composable, pipeable function chains on the iii engine. Use when building functional pipelines, effect systems, or typed composition layers where each step is a pure function with distributed tracing and retry.
Draft patent claims for an invention. Use when user says "撰写权利要求", "draft claims", "写权利要求书", "claim drafting", or wants to create patent claims. The core skill of the patent pipeline.
Guide for using Netlify Image CDN for image optimization and transformation. Use when serving optimized images, creating responsive image markup, setting up user-uploaded image pipelines, or configuring image transformations. Covers the /.netlify/images endpoint, query parameters, remote image allowlisting, clean URL rewrites, and composing uploads with Functions + Blobs.
Execute KQL management commands (table management, ingestion, policies, functions, materialized views) against Fabric Eventhouse and KQL Databases via CLI. Use when the user wants to: 1. Create or alter KQL tables, columns, or functions 2. Ingest data into an Eventhouse (inline, from storage, streaming) 3. Configure retention, caching, or partitioning policies 4. Create or manage materialized views and update policies 5. Manage data mappings for ingestion pipelines 6. Deploy KQL schema via scripts Triggers: "create kql table", "kql ingestion", "ingest into eventhouse", "kql function", "materialized view", "kql retention policy", "eventhouse schema", "kql authoring", "create eventhouse table", "kql mapping"
Auto-generate viral 9:16 YouTube Shorts (or TikTok/Reels clips) from a long-form YouTube URL or hosted video. Pipeline downloads the source, transcribes locally with Whisper, ranks highlights through a virality framework (hook / emotional peak / opinion bomb / revelation / conflict / quotable / story peak / practical value), dedupes overlapping candidates, and vertically auto-crops the top N as mp4s via `muapi edit clipping`.