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Found 5,658 Skills
Debug container agent issues. Use when things aren't working, container fails, authentication problems, or to understand how the container system works. Covers logs, environment variables, mounts, and common issues.
agent-team: Read messages for one recipient agent.
Run a heavy neural-trader job (long walk-forward, big Monte-Carlo, parameter sweep, model training) on the Anthropic Managed Agent cloud runtime instead of locally
Orchestrate parallel AI coding agents across git worktrees for autonomous CI fixes, code reviews, and PR management
Interactive workflow to generate a full-lifecycle AGENTS.md using semantic AST/LSP analysis and chained user interviews.
Execute deterministic, event-sourced security audits using ESAA-Security's LLM-based agent architecture with 95 checks across 16 security domains
Give AI agents eyes into React apps - inspect component trees, props, state, hooks, and profile rendering performance from the command line
This skill should be used when the user asks to "create an agent", "make an agent", "write an agent", "build a subagent", "add an agent to a plugin", "design an autonomous agent", "generate an agent file", "write a system prompt for an agent", "what frontmatter does an agent need", "create a specialized agent". Not for skills or commands — use create-skill.
For use when students **have completed WG-12 to WG-21** (single-file consolidation blueprint) and are working on **WG-22 Code Splitting** (`agent_core.py` + `main.py`). **First message in a new session**: Display PEAS brand screen and confirm readiness first; after confirmation, **lay out the context** before proceeding to requirement clarification. If **`prompts/` or `templates/`** are missing, copy them from `references/project_assets/` to the project root. Process: Spec Alignment (2d′) → Six-column Contract → **In-session Handoff Implementation** → Acceptance. Starting point: starter_main_wg21.py; Standard reference: reference_agent_core.py + reference_main.py. Triggers: peas-workshop-advanced-coach, PEAS workshop advanced coach, WG-22, code splitting coach, Agent.chat.
Context layer for data and analytics AI agents with semantic layer, skills, and memory via MCP
Build production-ready GenAI agents with stateful workflows, vector memory, deployment, and orchestration using LangGraph and LangChain
Guidelines for creating well-structured AI agent skills. Use when building a new skill, reviewing skill quality, or unsure how to organize a skill.