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Found 110 Skills
Convert audio files between formats (MP3, WAV, FLAC, OGG, M4A) with bitrate and sample rate control. Batch processing supported.
Extract vendor, date, items, amounts, and total from receipt images using OCR and pattern matching with structured JSON output.
Use when asked to generate legal contracts, agreements, or documents from templates with variable substitution and formatting.
Use when generating 50+ structured items with parallel Claude Code subagents and merging outputs into one file.
Use when "Modal", "serverless GPU", "cloud GPU", "deploy ML model", or asking about "serverless containers", "GPU compute", "batch processing", "scheduled jobs", "autoscaling ML"
Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations
OCR skill using PaddleOCR model via SiliconFlow API. This skill should be used when the user asks to "recognize text from an image", "extract text from a photo", "OCR this image", "read text from screenshot", or mentions "PaddleOCR", "image text recognition", "text extraction from images".
Unified task execution protocol for Codex-only work. Supports Single Task, Epic Task, and Batch Task while preserving CSV truth-source, validation gates, context recovery, and Debug-First failure exposure. WHEN TO USE: user asks to "track tasks", "create todo list", "make a plan", "track progress", "long task", "big project", "build from scratch", "autonomous session", "跟踪任务", "自主执行", "长时任务", "从零开始", "任务管理", "做个计划", "大工程", or when a task clearly requires 3+ ordered steps that produce file changes. DO NOT USE: single-step fixes, pure Q&A, code review, explaining code, search/research tasks, tasks with fewer than 3 steps, or tasks that do not produce file changes.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Docling document parser for PDF, DOCX, PPTX, HTML, images, and 15+ formats. Use when parsing documents, extracting text, converting to Markdown/HTML/JSON, chunking for RAG pipelines, or batch processing files. Triggers on DocumentConverter, convert, convert_all, export_to_markdown, HierarchicalChunker, HybridChunker, ConversionResult.
Provides strategies for efficiently transforming large text files (thousands to millions of lines) using text editors like Vim, sed, or awk. This skill should be used when tasks involve bulk text transformations, CSV manipulation at scale, pattern-based edits across massive files, or when keystroke/operation efficiency is constrained. Applicable to tasks requiring macros, regex substitutions, or batch processing of structured text data.
Universal watermark removal with ML-based inpainting and automatic detection. Works on ANY watermark type (Google SynthID, Midjourney, DALL-E, stock photos, logos). Four methods: inpaint (ML, best quality), aggressive (fast), crop (fastest), paint (basic). Auto-detects watermark location in any corner. Use when: (1) Removing ANY type of watermark, (2) Google AI/Imagen/Gemini watermarks, (3) Stock photo watermarks, (4) Logo overlays, (5) Cleaning images for production, (6) Batch processing, or (7) User mentions 'watermark', 'remove watermark', 'clean image', 'SynthID'