Total 53,999 skills, AI & Machine Learning has 8981 skills
Showing 12 of 8981 skills
Use when diagnosing CopilotKit issues -- runtime connectivity failures, agent not responding, streaming errors, tool execution problems, transcription failures, version mismatches, and AG-UI event tracing.
Deep briefing on one matter — current posture, what's changed, next deadline, open questions, and a risk re-assessment check, ready before a GC update or outside counsel call. Use when the user says "brief me on [matter]", "where are we on [matter]", or needs a read on a specific matter.
CenterPose for keypoint / pose estimation. Detects object centers and regresses keypoint locations for 6-DoF object pose estimation. Use when training, evaluating, exporting, or running inference for a TAO CenterPose model. Trigger phrases include "train CenterPose", "6-DoF object pose", "keypoint estimation", "object pose regression".
Install and update manually hosted skills in this repository, keeping canonical skill sources under `skills/`, installed output under `.agents/skills`, and `skills-lock.json` in sync. Use when asked to add, refresh, verify, or dogfood repo-local skills for this shared agent setup.
Morph, blend, and transform faces using each::sense AI. Create face morphs, celebrity blends, family resemblance predictions, gender swaps, and animated transitions between faces.
Use this skill when the user wants any MCP-capable agent or IDE assistant to interact with Google ADK agents through the adk-agent-extension MCP server. Trigger for requests like wiring ADK tools into Codex/Claude Code/Cursor/Cline/Gemini, registering a stdio MCP server, listing ADK servers/agents, creating sessions, and chatting with ADK agents.
Enables Claude to read, compose, and manage emails in Microsoft Outlook via Playwright MCP
Search bioRxiv biology preprints with natural language queries. Semantic search powered by Valyu.
Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".
Jeffrey Emanuel's multi-agent implementation workflow using NTM, Agent Mail, Beads, and BV. The execution phase that follows planning and bead creation. Includes exact prompts used.
Deep-dive Amazon review analysis. Extract sentiment patterns, recurring complaints, feature requests, and competitive insights from product reviews. Turn customer feedback into product improvement and marketing opportunities.
Python port of Claude Code agent harness — tools, commands, task orchestration, and CLI entrypoint via oh-my-codex