Total 44,264 skills, AI & Machine Learning has 7037 skills
Showing 12 of 7037 skills
Agent skill for pagerank-analyzer - invoke with $agent-pagerank-analyzer
Expert knowledge for Azure Translator development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Translator text/document APIs, custom models, glossaries, Docker containers, or Power Automate flows, and other Azure Translator related development tasks. Not for Azure AI Language (use azure-language-service), Azure AI Speech (use azure-speech), Azure AI Immersive Reader (use azure-immersive-reader), Azure AI Search (use azure-cognitive-search).
Expert knowledge for Azure AI Search development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing indexes, skillsets, vector/semantic search, indexers, private endpoints, or RAG apps, and other Azure AI Search related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics).
CrewAI architecture decisions and project scaffolding. Use when starting a new crewAI project, choosing between LLM.call() vs Agent.kickoff() vs Crew.kickoff() vs Flow, scaffolding with 'crewai create flow', setting up YAML config (agents.yaml, tasks.yaml), wiring @CrewBase crew.py, writing Flow main.py with @start/@listen, or using {variable} interpolation.
Build AI agents with Pydantic AI — tools, capabilities, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, stream output, define agents from YAML, or test agent behavior.
Run a free 35B AI coding agent on Apple Silicon Macs using local LLMs via llama.cpp or MLX with web search, shell, and file tools.
ASR (Automatic Speech Recognition) — enhanced speech-to-text built on Doubao large model, with audio preprocessing, denoising, and extended analysis capabilities. Async API. Choose this skill when: - Input is a video file (mp4/mov/mkv) — auto-extracts audio track - Audio needs denoising before recognition - File exceeds 512MB or 5 hours (no size limit) - Audio source is a TOS internal path (tos://bucket/key) - Need structured JSON output with timestamped utterances and metadata - Need speaker diarization, emotion/gender detection, speech rate, or sensitive word filtering Supports 99 languages, multiple formats (wav/mp3/m4a/aac/flac/ogg/mp4/mov/mkv), and auto language detection.
OpenRouter AI integration — list available models, get integration code examples for different environments, and send prompts to any OpenRouter-compatible model. Requires OPENROUTER_API_KEY env var for chat operations.
Sharpen, refine, and optimize AI agent skills through real usage — learn from mistakes, review quality, and improve over time. Observes skill execution in the current conversation, analyzes three sources (conversation history, file diffs, user feedback), and proposes concrete improvements to the target skill's SKILL.md. Works with Claude Code and any SKILL.md-based agent framework. Use after executing any skill: `/skill-sharpen [name]` for a specific skill, or `/skill-sharpen` to auto-detect the last used. Three modes: interactive (propose one by one), observe-only (dump to LESSONS.md), review (process pending lessons).
This skill should be used when the user asks to "fix the issues", "optimize existing content", "create new content for AI visibility", "run Morphiq Build", "generate schema markup", "create an llms.txt file", "run the content lab", or mentions building content fixes, generating schema, rewriting content for AI citations, or creating policy files. Consumes a Prioritized Roadmap (or user prompt, or existing content) and produces build artifacts through a 6-step content lab pipeline.
Elite full-stack developer for rapid spec and implementation. Use when the user asks to talk to Barry or requests the quick flow solo dev.
This skill should be used when the user asks to "run a tracking cycle", "measure AI visibility", "check share of voice", "run Morphiq Track", "track citations", "check GEO score", "generate prompts", "run content creation workflow", or mentions monitoring LLM mentions, running content creation workflows, measuring brand visibility, or generating query fanout content. Queries multiple LLM providers, produces delta reports, and maintains MORPHIQ-TRACKER.md as the persistent state file for the entire pipeline.