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Found 66 Skills
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
Universal AI voice / text-to-speech skill supporting OpenAI TTS (gpt-4o-mini-tts, tts-1), ElevenLabs multilingual TTS with voice cloning, Bailian Qwen TTS (qwen-tts / qwen3-tts-vd with voice-design custom voices, long-text chunking built in), MiniMax speech-02-hd, SiliconFlow CosyVoice / SenseVoice, and PlayHT 2.0. Use this skill whenever the user asks to read text aloud, synthesize speech, generate narration, create voice-over, dub a script, or turn any text into audio (mp3 / wav / ogg / flac). Typical phrases include "read this aloud", "generate voice for ...", "create a narration of ...", "tts this", "把这段念出来", "做个配音", "合成语音", or mentions of voices / TTS model names like Alloy, Ash, Cherry, Rachel, CosyVoice, PlayHT. Always use this skill even if the user does not specify a provider — pick one from EXTEND.md defaults or available env keys.
Use when performing bulk insert, update, or delete operations in Bknd. Covers createMany, updateMany, deleteMany, batch processing with progress, chunking large datasets, error handling strategies, and transaction-like patterns.
Efficient project file browser. Use it when you need to list the entire project structure, fuzzy search files, or safely read (supports chunking of large files) local codebase content.
MCP server providing local-first document management with AI-powered semantic search, hybrid vector search, and intelligent chunking using Orama and Gemini
Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-skill.
Use when writing or modifying Python code that imports `genoray` to read genotypes/dosages from VCF, PGEN, or SparseVar (`.svar`) files. Covers the public API surface, mode constants, range queries, chunking, filtering, and the SparseVar workflow. Skip for unrelated bioinformatics work.
Match spoken edit beats to candidate B-roll assets using a normalized transcript, subtitle chunking, optional A-roll analysis, and a reusable B-roll catalog. Use this when the goal is to decide what B-roll should support each beat, not just to list assets or describe the video.
Transcribe audio with StepFun's stepaudio-2.5-asr — an SSE endpoint (NOT /v1/audio/transcriptions) with 32K context, ~85-101x RTF on long audio, and a single-call ceiling around 30 minutes (no client-side chunking). Use when transcribing Chinese / English audio with StepFun, when long-form recordings (5-30 min) need to land in one request, when migrating from step-asr / step-asr-1.1, or when hitting the misleading `model stepaudio-2.5-asr not supported` error (which actually means wrong endpoint). Triggers on 阶跃 ASR, StepFun ASR, stepaudio-2.5-asr, 转录, 语音识别, 长音频转写, 语音转文字. For TTS with the sibling stepaudio-2.5-tts model, use the stepfun-tts skill instead.
Make dense or complex information easier to understand, navigate, remember, and act on through clear layers, concrete examples, and visible progress. Use when the user explicitly invokes Focus Friendly; requests focus-oriented simplification, chunking, pacing, mapping, or reorientation; reports overwhelm, trouble reading or focusing, task-initiation difficulty, or losing their place in the current task; asks whether those difficulties prove ADHD; or asks for help navigating long, structurally complex material that genuinely needs a map. Do not invoke merely because a request mentions ADHD or asks for summarization, teaching, planning, research, or routine coding; those tasks need an explicit focus or navigation signal.
This skill should be used when the user asks to "audit a website for AI visibility", "scan a domain", "check AI readiness", "evaluate content quality", "run a Morphiq Scan", "check if a site is optimized for LLMs", or mentions scanning a website for LLM citation readiness. Performs a full AI visibility audit across 5 categories (agentic readiness, content quality, chunking & retrieval, query fanout, policy files) and scores the domain on a 100-point rubric.
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