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Found 5,658 Skills
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.
Read a story file and implement it. Loads the full context (story, GDD requirement, ADR guidelines, control manifest), routes to the right programmer agent for the system and engine, implements the code and test, and confirms each acceptance criterion. The core implementation skill — run after /story-readiness, before /code-review and /story-done.
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.
Design and build multi-agent harness architectures for long-running AI application development. GAN-inspired Generator-Evaluator pattern, Sprint Contract negotiation, context management, quality criteria calibration. Based on Anthropic Engineering patterns. Use when: "build a harness", "multi-agent architecture", "agent orchestration", "generator-evaluator", "long-running app", "harness design", "agent pipeline", "quality evaluation loop", "sprint contract", "build app with agents", "Claude Agent SDK architecture", or when building complex full-stack apps that need planning → generation → evaluation cycles. Also use when discussing context degradation, self-evaluation bias, or assumption testing in AI workflows.
Use this skill when the user wants to audit Agent Skills, SKILL.md files, imported skills, prompts, tools, scripts, or skill repositories for safety, prompt injection risk, secret leakage, unsafe commands, unclear permissions, untrusted external references, or repo policy violations. Trigger phrases include "audit this skill," "skill security," "review imported skills," "prompt injection risk," "unsafe skill," "scan skills," and "security audit for skills."
Use when building custom agent backends, implementing the AG-UI protocol, debugging streaming issues, or understanding how agents communicate with frontends. Covers event types, SSE transport, AbstractAgent/HttpAgent patterns, state synchronization, tool calls, and human-in-the-loop flows.