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Found 134 Skills
Image file processing utilities (圖像檔案處理工具). Use when working with image resizing (圖片縮放), format conversion (格式轉換), watermarks (浮水印), or processing pipelines (處理管道). Covers Sharp integration, Buffer/Stream handling (緩衝/串流處理), batch processing (批次處理), and NestJS integration (NestJS 整合).
Interact with the Cargo platform via CLI. Use when the user wants to execute an action, run a workflow, trigger a batch, message an AI agent, query orchestration runtime tables (runs/batches/spans/records) with SQL, fetch segment records, or inspect a model schema.
Complete subtitle and caption system for FFmpeg 7.1 LTS and 8.0.1 (latest stable, released 2025-11-20). PROACTIVELY activate for: (1) Burning subtitles (hardcoding SRT/ASS/VTT), (2) Adding soft subtitle tracks, (3) Extracting subtitles from video, (4) Subtitle format conversion, (5) Styled captions (font, color, outline, shadow), (6) Subtitle positioning and alignment, (7) CEA-608/708 closed captions, (8) Text overlays with drawtext, (9) Whisper AI automatic transcription (FFmpeg 8.0+ with VAD, multi-language, GPU), (10) Batch subtitle processing. Provides: Format reference tables, styling parameter guide, position alignment charts, Whisper model comparison, VAD configuration, dynamic text examples, accessibility best practices. Ensures: Professional captions with proper styling and accessibility compliance.
Use when working with multi-item data, batches, paginated APIs, rate-limited APIs, fan-out across multiple branches, anything that needs to "do this for each", or any time the user mentions looping, iterating, batching, paging, parallelism, or "loop over items". Triggers on "loop", "iterate", "for each", "batch", "page through", "paginate", "rate limit", "process all", "fan-out", "parallel branches", "concurrency", or any node that should run once vs once-per-item.
Automatically discover data pipeline and ETL skills when working with ETL, data pipelines, streaming, batch processing, data validation, or pipeline orchestration. Activates for data development tasks.
Convert between physical units (length, mass, temperature, time, etc.). Use for scientific calculations, data transformation, or unit standardization.
Resize a Canva design into multiple social media formats (Facebook post, Facebook story, Instagram post, Instagram story, LinkedIn post) and export all versions as PNGs. Use this skill when users want to resize Canva designs specifically for multiple social media platforms in one operation, rather than resizing to a single format manually.
Wallet profiler — balance, PnL, labels, transactions, counterparties, related wallets, batch, trace, compare. Use when analysing a specific wallet address or comparing wallets.
Execute deep research on every item in a research outline, producing structured JSON per item and a final markdown report. Use after running /research to generate an outline. Reads outline.yaml and fields.yaml, launches parallel research agents in batches, validates output, generates a consolidated report, and supports resume on interruption. Trigger when the user says "start deep research", "research these items", "run the deep phase", "fill in the fields for each item", or "generate the research report".
GameObject component management. Use when users want to add, remove, or configure components like Rigidbody, Collider, AudioSource. Triggers: component, add component, rigidbody, collider, audio source, script, 组件, 添加组件, 刚体, 碰撞体.
Download workflow run results, export segment data, and monitor run metrics using the Cargo CLI. Use when the user wants run metrics, error rates, data export, or download results for their Cargo workspace. For billing and credit usage, use the cargo-billing skill instead.
Use when working with multi-item data, batches, paginated APIs, rate-limited APIs, anything that needs to "do this for each", or any time the user mentions looping, iterating, batching, paging, or "loop over items". Triggers on "loop", "iterate", "for each", "batch", "page through", "paginate", "rate limit", "process all", or any node that should run once vs once-per-item.