transcription-skill

Execution engine: typed FFmpeg/ffprobe tools every delegating Skill runs on (the owners above it decide; it runs), plus preflight checks and cut-list export

16 skills

Screen recording of a task into a how-to guide and a SKILL.md

2 skills

Subtitle validation, SRT/WebVTT generation, burn-in, and animated captions generated from subtitle cues

2 skills

Verifies AI-generated documents (scripts, caption scripts as documents, reports; caption files belong to subtitle-skill and qc-skill) and issues evidence-backed receipts

1 skill

Gain, mix, loudness normalization (ffmpeg-skill is the engine), dynamics, denoise, filler and silence removal

1 skill

Headless Blender: inspect, convert, optimize and render 3D assets (needs Blender installed)

1 skill

Complete Speak integration skill pack with 24 skills covering AI language learning, speech recognition, conversation practice, and education technology. Flagship tier vendor pack.

24 skills

Problem-solving techniques: collision-zone-thinking, inversion-exercise, meta-pattern-recognition, scale-game, simplification-cascades, when-stuck

Framework-agnostic design principles for cognition, mental models, complexity reduction, and learnability. Covers Hick's Law, Wayfinding, Progressive Disclosure, 80/20 Rule, Chunking, Mental Model, Recognition Over Recall, Consistency, Mimicry, and Iconic Representation. Drawn from the cognition-related entries of 'Universal Principles of Design' (Lidwell, Holden, Butler, 2003) and the broader HCI literature (Norman, Miller, Nielsen, Krug).

33 skills

面向《模式识别与人工智能》(Pattern Recognition and Artificial Intelligence, PR&AI) 的 12 个中文技能，聚焦 AI 细分方向的中文期刊投稿全流程，事实以官网 manu46.magtech.com.cn/Jweb_prai 与 CNKI/CCF 推荐中文科技期刊目录双源核验。

12 skills

AI and machine learning: Foundation Models, Core ML, Vision, NaturalLanguage, Speech Recognition

86 skills · 1 MCP server

Use this agent when implementing AI/ML features, integrating language models, building recommendation systems, or adding intelligent automation to applications. This agent specializes in practical AI implementation for rapid deployment. Examples:\n\n<example>\nContext: Adding AI features to an app\nuser: "We need AI-powered content recommendations"\nassistant: "I'll implement a smart recommendation engine. Let me use the ai-engineer agent to build an ML pipeline that learns from user behavior."\n<commentary>\nRecommendation systems require careful ML implementation and continuous learning capabilities.\n</commentary>\n</example>\n\n<example>\nContext: Integrating language models\nuser: "Add an AI chatbot to help users navigate our app"\nassistant: "I'll integrate a conversational AI assistant. Let me use the ai-engineer agent to implement proper prompt engineering and response handling."\n<commentary>\nLLM integration requires expertise in prompt design, token management, and response streaming.\n</commentary>\n</example>\n\n<example>\nContext: Implementing computer vision features\nuser: "Users should be able to search products by taking a photo"\nassistant: "I'll implement visual search using computer vision. Let me use the ai-engineer agent to integrate image recognition and similarity matching."\n<commentary>\nComputer vision features require efficient processing and accurate model selection.\n</commentary>\n</example>

1 agent