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Found 262 Skills
Semantic search using embeddings and vector storage. Search documents semantically using similarity matching.
Эксперт categorical encoding. Используй для ML feature engineering, one-hot, target encoding и embeddings.
Add captions to a talking-head video. ONE catalog (CATALOG.md) of 32 visual identities behind two engines: column-flow (captions composited INTO the scene — matte occlusion + mix-blend; cream/ink/editorial/keynote/documentary/loud/neon/glitch/chrome/velocity) and themed constitutions (anchor/ordnance/terminal/neonsign/stardust/stomp/scoreboard/transit/vhs/arcade/dossier/laser/thunder/hologram/biolume/aurora/spectrum/papercut/popup/chalkboard/graffiti/brush/inkwater/ransom/lastpage/nightcity — e.g. a glyph-decode climax, a neon sign WRITTEN stroke by stroke, or the quiet `anchor` rail default). Route by identity, never by mode. Trigger on "captions/subtitles", "embed/cinematic captions", "VFX captions", "炸/特效/酷炫字幕", a named identity, or top-tier motion-graphics asks. Embedding every word is wrong for most talking-head content — `anchor` is the verbatim default. Pipeline: transcription → hyperframes remove-background matting → HTML render → ffmpeg overlay. Requires hyperframes and a single-subject clip.
Lottie and dotLottie adapter patterns for HyperFrames. Use when embedding lottie-web JSON animations, .lottie files, @lottiefiles/dotlottie-web players, registering instances on window.__hfLottie, or making After Effects exports deterministic in HyperFrames.
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".
Golang struct and interface design patterns — composition, embedding, type assertions, type switches, interface segregation, dependency injection via interfaces, struct field tags, and pointer vs value receivers. Use this skill when designing Go types, defining or implementing interfaces, embedding structs or interfaces, writing type assertions or type switches, adding struct field tags for JSON/YAML/DB serialization, or choosing between pointer and value receivers. Also use when the user asks about "accept interfaces, return structs", compile-time interface checks, or composing small interfaces into larger ones.
Golang CLI application development. Use when building, modifying, or reviewing a Go CLI tool — especially for command structure, flag handling, configuration layering, version embedding, exit codes, I/O patterns, signal handling, shell completion, argument validation, and CLI unit testing. Also triggers when code uses cobra, viper, or urfave/cli.
MANDATORY recipe for every Caffeine build that calls OpenAI (ChatGPT, GPT-4o, an LLM, a chatbot, embeddings). The ONLY supported path is the `openai-client` mops package with a canister-side API-key bearer. Hand-rolling `ic.http_request` to `api.openai.com/v1/...` is a FORBIDDEN anti-pattern — it leaks the bearer across replicated outcalls (security + 13× billing impact), bypasses the typed request/response bindings, and forces hand-rolled JSON on a language with poor JSON support. Load this skill whenever the user, spec, or any prior task mentions ChatGPT, GPT (any version), OpenAI, an LLM, a chatbot, or embeddings — and BEFORE writing any code that touches `api.openai.com`.
Generate a standards-aligned browser favicon.ico from a user-supplied source image, embedding PNG rasters at 32×32, 48×48, and 180×180 in one ICO container. Use when the user asks to create a favicon, 生成 favicon、网站图标、从图片做 ico、favicon.ico、create favicon from image. 从用户提供的源图生成含 32/48/180 三档尺寸的 favicon.ico（ICO 内嵌 PNG）。若用户未上传或未指定可用源图，必须中止并提示上传/路径。
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
Visualizes datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when exploring dataset structure, finding clusters, identifying outliers, or understanding data distribution.
This skill should be used to watch a long-running background job (ffmpeg/media encode, qmd or other embedding/vector-DB run, batch agent/LLM pipeline, or a real-browser/agent-browser daemon) until it finishes or wedges, then deliver a verdict (done, needs-attention, or blocked) plus the exact next command, without burning dozens of manual poll commands. Triggers on "babysit this job", "watch this until it's done", "ping me when the encode/embed/batch finishes", "is this background process stuck", "monitor this ffmpeg/qmd run", or any request to wait on a long-running process and be told when it's complete or hung.