Total 53,107 skills, AI & Machine Learning has 8882 skills
Showing 12 of 8882 skills
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.
AI voice generation, text-to-speech, and voice synthesis via inference.sh CLI. Models: Inworld TTS-2 (100+ languages, emotion/non-verbal steering), Inworld TTS 1.5 (ultra-low latency), ElevenLabs (22+ premium voices, 32 languages), Kokoro TTS, DIA, Chatterbox, Higgs, VibeVoice for natural speech. Capabilities: multiple voices, emotions, accents, long-form narration, conversation, voice transformation, delivery mode control, character voices. Use for: voiceovers, audiobooks, podcasts, video narration, accessibility, gaming NPCs, avatar audio, UGC. Triggers: voice cloning, tts, text to speech, ai voice, voice generation, voice synthesis, voice over, narration, speech synthesis, ai narrator, elevenlabs, eleven labs, natural voice, realistic speech, voice ai, voice changer, inworld, inworld tts, character voice, npc voice
Web-based chat interface for Hermes Agent with multi-profile management, streaming chat, and interactive terminal integration
Use when executing oncology target-validation steps that need dependency, pharmacology, and cancer-context evidence interpreted together.
Pure text-to-image generation for all creative scenarios: logo design, poster design, illustration, meme, game assets, social media content, 3D rendering, education, fashion, food, pet, wedding, holiday marketing, and artistic styles. Use when generating images from text descriptions without a reference photo (e.g. design a logo, create a poster, generate game art, make a meme, 3D render).
Diagnose why an agent harness misbehaved by reading the local flight-recorder ledger (.vigiles/runs.jsonl) — which skills fired or got hijacked, which hooks blocked or wrongly allowed, which subagent tool-contract violations happened, and how a skill's trigger rate moved. Use when asked why a skill stopped firing, why a hook didn't block, why the wrong skill ran, or to debug/investigate what the harness actually did. NOT for writing new rules (use strengthen) or editing the spec (use edit-spec).
Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced For You algorithm. Use this skill whenever the user is building any system that picks "the top K items for a (user, context)" — social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.
Generates paste-ready Power Apps Canvas App YAML. Invoke when the user wants to replicate a UI mockup, improve an existing Canvas app screen, or build a new screen from a text description. Also invoke when the user asks to "improve", "redesign", or "generate YAML" for a Canvas app screen.
Migrate GPU/CUDA Triton operators to Triton-Ascend, or rewrite Python/PyTorch operators into Triton-Ascend implementations that can run on Ascend NPU. When clear optimization opportunities are identified, directly output the optimized code, minimal validation script, and troubleshooting instructions. This skill should be prioritized when users mention 昇腾 (Ascend), Ascend, NPU, triton-ascend, Triton operator migration, PyTorch operator rewriting, coreDim, UB overflow, 1D grid, physical core binding, block_ptr, stride, memory access alignment, mask performance, dtype degradation, operator optimization, or directly ask questions like "How to use this skill", "How to run it in the command line", "How to perform migration/validation in a container", even if users do not explicitly say "write a skill" or "perform migration".
Produce a polished, self-contained HTML "readout" document under ~/.readouts (with an auto-maintained index page), either by snapshotting the findings accumulated in the current conversation or — when invoked fresh, e.g. "/readout on how github webhook events are processed" — by sharpening scope with clarifying questions and researching the codebase before documenting. The work runs in a child agent so the main conversation's context stays clean. Use whenever the user invokes /readout, says "write this up", "turn this into a doc/page", "make a readout", or asks for a readable, shareable document capturing findings or explaining how something works.
Doctor Strange — forward mental simulation via parallel universe subagents. Walks through how a future event might unfold step by step, like a human mentally rehearsing a scenario. Stores simulations as persistent memory for later recall. TRIGGER when: user explicitly asks to simulate / rehearse / play out a scenario; user says "推演", "模拟", "预演", "imagine", "what if", "run through", "play this out", "what could go wrong"; user faces a high-stakes upcoming decision and is uncertain how it will unfold. DO NOT TRIGGER when: user wants factual lookup or research; user wants analysis of a past event (use regular memory); user wants a simple recommendation without simulation; user is debugging code or doing technical work unrelated to decision-making. Three modes: SIMULATE (run a new forward simulation), RECALL (surface past simulations as soft priors), MANAGE (list/void/re-run stored simulations).