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Found 258 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.
Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.
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.