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Found 52 Skills
Restructure project documentation for clarity and accessibility. Use when users ask to "organize docs", "generate documentation", "improve doc structure", "restructure README", or need to reorganize scattered documentation into a coherent structure. Analyzes project type and creates appropriate documentation hierarchy.
Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.
Strategies for managing LLM context windows effectively in AI agents. Use when building agents that handle long conversations, multi-step tasks, tool orchestration, or need to maintain coherence across extended interactions.
Typst Academic Paper Assistant (supports Chinese and English papers, conference/journal submissions). Domains: Deep Learning, Time Series, Industrial Control, Computer Science. Trigger Words (any module can be called independently): - "compile", "compile", "typst compile" → Compilation Module - "format", "format check", "lint" → Format Check Module - "grammar", "grammar", "proofread", "polish" → Grammar Analysis Module - "long sentence", "long sentence", "simplify", "decompose" → Complex Sentence Analysis Module - "academic tone", "academic expression", "improve writing" → Academic Expression Module - "logic", "coherence", "logic", "cohesion", "methodology", "methodology" → Logical Cohesion & Methodology Depth Module - "translate", "translate", "Chinese to English" → Translation Module - "bib", "bibliography", "bibliography" → Bibliography Module - "deai", "de-AI", "humanize", "reduce AI traces" → De-AI Editing Module - "title", "title", "title optimization", "create title" → Title Optimization Module - "template", "template", "IEEE", "ACM" → Template Configuration Module
Set up or repair codecontext adoption in a project. Use this whenever the user wants to add @context annotations to a repo, install the codecontext toolchain, update AGENTS.md guidance, improve agent workflows around decision capture, or audit whether an existing codecontext setup is coherent. Prefer this skill over vague "document the tool" work: it is specifically for making a repo actually usable with codecontext.
The craft of designing icons that communicate instantly across cultures, contexts, and scales. Icon design bridges semiotics, cognitive psychology, and visual craft to create symbols that users understand without thinking. Great icons are invisible in the best way - they convey meaning so naturally that users never pause to decode them. This skill covers icon grid systems, optical alignment, stroke consistency, metaphor selection, scalability across sizes, SVG optimization, and icon set coherence. The best icon designers understand that icons are a visual language - each icon must speak the same dialect while carrying its own distinct meaning. Use when "icon, iconography, symbol, glyph, icon set, icon library, pictogram, svg icon, icon grid, icon pack, feather icons, lucide, phosphor, heroicons, icon system, icon style, icons, iconography, svg, symbols, glyphs, pictograms, ui-icons, icon-set, visual-design, design-system" mentioned.
SmartACE (Agentic Context Engineering) workflow engine with MCP-B (Master Client Bridge) and AMUM-QCI-ETHIC module. Dual database architecture using DuckDB (analytics) + SurrealDB (graph). Uses Blender 5.0 (bpy) and UE5 Remote Control. Use when (1) MCP-B agent-to-agent communication (INQC protocol), (2) AMUM 3→6→9 progressive alignment, (3) QCI quantum coherence states, (4) ETHIC principles enforcement (Marcel/Anthropic/EU AI Act), (5) SurrealDB graph relationships, (6) DuckDB SQL workflows, (7) ML inference with infera/vss, (8) Blender 5.0 headless processing, (9) UE5 scene control, (10) DuckLake time travel.
The orchestration layer for AI-native creative production. This skill coordinates multiple AI tools—video, image, audio, digital humans, effects—into cohesive campaigns, productions, and creative systems. As AI tools proliferate, the challenge shifts from "can we create this?" to "how do we orchestrate these capabilities into something coherent?" The AI Creative Director thinks in systems, not tools. In pipelines, not one-offs. In brand consistency across AI-generated assets. This is where creative vision meets technical orchestration. The AI Creative Director doesn't just use AI tools—they compose them into creative instruments that produce at scales and speeds previously impossible. Use when "AI creative director, orchestrate AI, AI campaign, multi-tool, AI workflow, AI pipeline, coordinate AI, AI production, AI creative system, full AI production, AI at scale, orchestration, creative-direction, ai-production, workflow, pipeline, multi-tool, scale, quality-control" mentioned.
AI-enhanced LaTeX Example Intelligent Generator, achieving organic integration of AI and hard-coding. AI handles "semantic understanding" (analyzing chapter themes, inferring resource relevance, generating coherent narratives), while hard-coding is responsible for "structure protection" (format validation, hash verification, access control). It applies to scenarios where users request "filling example content/generating examples/supplementing LaTeX examples".
Verify implementation matches change artifacts. Use when the user wants to validate that implementation is complete, correct, and coherent before archiving.
Groups related git changes into coherent commits and drafts commit messages. Use when the user asks to commit, commit current changes, or create a commit.
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.