Total 50,634 skills, AI & Machine Learning has 8486 skills
Showing 12 of 8486 skills
Data engineering, machine learning, AI, and MLOps. From data pipelines to production ML systems and LLM applications.
Unified planning skill - 4-phase planning workflow, plan verification, and interactive replanning. Triggers on "workflow:plan", "workflow:plan-verify", "workflow:replan".
Zero Framework Cognition Principles
Multi-agent distributed context preservation protocol using cryptographic sharding, gossip propagation, and Byzantine fault tolerance to maintain coherent shared memory across dynamic agent networks.
Retrieves MLflow traces using CLI or Python API. Use when the user asks to get a trace by ID, find traces, filter traces by status/tags/metadata/execution time, query traces, or debug failed traces. Triggers on "get trace", "search traces", "find failed traces", "filter traces by", "traces slower than", "query MLflow traces".
Central authority for Claude Code subagents (sub-agents). Covers agent file format, YAML frontmatter, tool access configuration, model selection (inherit, sonnet, haiku, opus), automatic delegation, agent lifecycle, resumption, command-line usage (/agents), Agent SDK programmatic agents, priority resolution, and built-in agents (Plan subagent). Assists with creating agents, configuring agent tools, understanding agent behavior, and troubleshooting agent issues. Delegates 100% to docs-management skill for official documentation.
Configure auto-configure Ollama when user needs local LLM deployment, free AI alternatives, or wants to eliminate hosted API costs. Trigger phrases: "install ollama", "local AI", "free LLM", "self-hosted AI", "replace OpenAI", "no API costs". Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
ML inference latency optimization, model compression, distillation, caching strategies, and edge deployment patterns. Use when optimizing inference performance, reducing model size, or deploying ML at the edge.
Sequential subagent execution with two-stage review gates for implementation plans. Use when executing multi-task plans in current session, when tasks need fresh subagent context to avoid pollution, when formal review cycles (spec compliance then code quality) are required between tasks, or when you need diff-based validation of each task before proceeding.
The slogan unpacked — seven readings of 'Manufacturing Intelligence'
This skill should be used when creating agents, writing agent frontmatter, configuring subagents, or when "create agent", "agent.md", "subagent", or "Task tool" are mentioned.
Flask Ml Api Creator - Auto-activating skill for ML Deployment. Triggers on: flask ml api creator, flask ml api creator Part of the ML Deployment skill category.