Total 30,670 skills, AI & Machine Learning has 4954 skills
Showing 12 of 4954 skills
Implements agents using Deep Agents. Use when building agents with create_deep_agent, configuring backends, defining subagents, adding middleware, or setting up human-in-the-loop workflows.
Jeffrey Emanuel's comprehensive markdown planning methodology for software projects. The 85%+ time-on-planning approach that makes agentic coding work at scale. Includes exact prompts used.
Manage OpenClaw bot configuration - channels, agents, security, and autopilot settings
Generate professional product photography and hero shots using AI. Use when creating product images for e-commerce, landing pages, or marketing materials.
Extract and analyze Agentforce session tracing data from Salesforce Data 360. Supports high-volume extraction (1-10M records/day), Polars-based analysis, and debugging workflows for agent sessions.
Use when users say "create a skill", "make a new skill", "build a skill", "skill for X", "package this as a skill", or when refactoring/updating/auditing existing skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations.
Route Alibaba Cloud Model Studio requests to the right local skill (Qwen Image, Qwen Image Edit, Wan Video, Wan R2V, Qwen TTS and advanced TTS variants). Use when the user asks for Model Studio without specifying a capability.
Manage Alibaba Cloud Platform for Artificial Intelligence PAI - AIWorkspace (AIWorkSpace) via OpenAPI/SDK. Use for listing resources, creating or updating configurations, querying status, and troubleshooting workflows for this product.
Build vector retrieval with DashVector using the Python SDK. Use when creating collections, upserting docs, and running similarity search with filters in Claude Code/Codex.
Generate ComfyUI workflow JSON from natural language descriptions. Validates against installed models/nodes before output. Use when building custom ComfyUI workflows from scratch or modifying existing ones.
Optimizes Claude Code memory files in 4 interactive steps: removes duplicates, migrates rules to CLAUDE.md/rules files, compresses remaining entries, validates with cleanup. Typical reduction: 30-50% on token count.
Maintains awareness across sessions. Spawns observer agent on start, loads context, notifies of evolution opportunities.