Total 50,313 skills, AI & Machine Learning has 8452 skills
Showing 12 of 8452 skills
Apply when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw payloads.
Context layer for data and analytics AI agents with semantic layer, skills, and memory via MCP
Show ContextShield status and waste protection stats
EMIT phase. Pre-emit debug, write files, post-emit verify from disk. Any new unknown triggers immediate snake back to planning — restart chain.
EXECUTE phase. Resolve all mutables via witnessed execution. Any new unknown triggers immediate snake back to planning — restart chain from PLAN.
[BETA] Execute work plans with external delegate support. Same as ce:work but includes experimental Codex delegation mode for token-conserving code implementation.
Author ZenML pipelines: @step/@pipeline decorators, type hints, multi-output steps, dynamic vs static pipelines, artifact data flow, ExternalArtifact, YAML configuration, DockerSettings for remote execution, custom materializers, metadata logging, secrets management, and custom visualizations. Use this skill whenever asked to write a ZenML pipeline, create ZenML steps, make a pipeline work on Kubernetes/Vertex/SageMaker, add Docker settings, write a materializer, create a custom visualization, handle "works locally but fails on cloud" issues, or configure pipeline YAML files. Even if the user doesn't explicitly mention "pipeline authoring", use this skill when they ask to build an ML workflow, data pipeline, or training pipeline with ZenML.
AI voice assistants with custom instructions, knowledge bases, and tool integrations.
AI voice assistants with custom instructions, knowledge bases, and tool integrations.
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends agent capabilities with specialized knowledge, workflows, or tool integrations.
Coaches end-to-end ML system design interviews covering inference pipelines, recommendation systems, RAG, feature stores, and monitoring. Use for L6+ design rounds, ML architecture whiteboarding, system design practice, serving tradeoff analysis. Activate on "ML system design", "ML interview", "recommendation system design", "RAG architecture", "feature store design", "model serving". NOT for coding interviews, behavioral questions, ML theory quizzes, or paper implementations.
Agent skill for repo-architect - invoke with $agent-repo-architect