Total 54,113 skills, AI & Machine Learning has 8999 skills
Showing 12 of 8999 skills
Convert text to speech (TTS) using the ListenHub API. Three modes are supported: Quick Synthesis (/v1/tts), Multi-role Script (/v1/speech), and Long Text Streaming Synthesis (/v1/flow-speech/episodes). If no voice is specified, automatically retrieve the voice list for user selection, with chat-girl-105-cn (Xiaoman) as the default. Use when user says: "tts", "text to speech", "语音合成", "文字转语音", "朗读", "生成语音", "生成音频", "转音频", "text to audio"
Codex-native Academic Research Skills suite for deep research, academic paper writing, manuscript review, full research-to-paper pipelines, and experiment planning or validation. Use when the user asks for deep research, literature review, systematic review, meta-analysis, research question refinement, academic paper drafting, paper revision, citation or integrity checks, reviewer simulation, peer review, editorial decision letters, research-to-paper workflows, experiment execution planning, statistical interpretation, or human study protocol support. Also use for Claude-style ARS command aliases such as /ars-plan, ars-plan, /ars-outline, /ars-abstract, /ars-lit-review, /ars-citation-check, /ars-disclosure, /ars-format-convert, /ars-revision-coach, /ars-revision, and /ars-full. This skill vendors ARS role prompts, references, templates, and shared handoff schemas under ars/.
A collection of Agent Skills for the Stitch MCP server: generate high-fidelity UI screens, create multi-page websites from a single prompt, produce DESIGN.md documentation, enhance vague UI prompts, convert designs to React/shadcn-ui components, and generate walkthrough videos via Remotion. Use when the user needs AI-assisted UI design generation, prompt refinement, or screen-to-code workflows. Triggers on: stitch, stitch-design, stitch-loop, enhance-prompt, react-components, remotion, shadcn-ui, screen generation, ui generation.
Build and operate multi-agent workflows with OpenAI Agents SDK (Python): define agents/tools/handoffs, add guardrails, run conversations, and debug orchestration behavior. Use when users ask for agent orchestration with OpenAI-native patterns, handoff routing, or production-ready agent loops.
Design enterprise-grade agent systems with Microsoft's agent framework patterns: role separation, workflow control, policy boundaries, and observability. Use when users need robust organizational agent workflows, governance, and maintainable multi-agent architecture.
Generate true pixel art sprites, tilesets, and animations with the Retro Diffusion API — the best dedicated pixel art model. Use when the user says "generate pixel art", "make a sprite with AI", "use Retro Diffusion", "rdpk", or wants high-quality pixel art faster than hand-coded sprite arrays. Requires a Retro Diffusion account and RETRODIFFUSION_API_KEY (paid credits). Great for quick prototyping of 2D Phaser games.
Transcribe pre-recorded audio files or URLs with Gladia. Use when the user needs batch/async transcription, speaker diarization, subtitles (SRT/VTT), PII redaction, translation, NER, summarization, chapterization, audio-to-LLM, or any audio intelligence on pre-recorded content. Always prefer the official SDK; fall back to raw REST only when SDK cannot satisfy the requirement.
Generate llms.txt and llms-full.txt files for a website to improve AI discoverability. Use when the user asks to create llms.txt, generate llms.txt, fix llms.txt, make site AI-readable, or mentions llms.txt generation.
Use when planning, running, comparing, or recording computational experiments, benchmarks, ablations, autonomous research loops, overnight runs, training runs, or exploratory variants.
Paper Analyst — Responsible for in-depth paper reading, extracting method details, and building comparison tables. Activated when assigned to analyze papers by research supervisors or literature investigators. Conduct structured analysis on the Top 20 core papers, generate paper analysis cards and cross-paper comparison tables.
Apply CUDA Graphs to PyTorch workloads — API selection (torch.compile, PyTorch make_graphed_callables, TE make_graphed_callables, MCore CudaGraphManager, FullCudaGraphWrapper, manual torch.cuda.graph), code compatibility, capture workflows, dynamic pattern handling, and troubleshooting. Triggers: CUDA graph, torch.cuda.graph, make_graphed_callables, reduce-overhead, graph capture, graph replay, kernel launch overhead, CudaGraphManager, FullCudaGraphWrapper, full-iteration graph, stream capture.
Extracts text (with locations) from images and PDF documents using PaddleOCR.