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Found 11,827 Skills
Generates high-quality Gherkin (BDD) scenarios from functional requirements using a two-agent iterative cycle: a generator agent that creates/modifies the Gherkin and a reviewer agent that validates it and proposes improvements. The cycle repeats automatically until the Gherkin passes review. Use this skill whenever the user mentions: "generate Gherkin", "BDD scenarios", "Gherkin test cases", "Feature/Scenario/Given/When/Then", "requirements to Gherkin", "BDD specifications", or asks to transform functional requirements into behaviour tests. Also applies when the user brings a requirements document and wants test cases, acceptance criteria, or user stories with executable examples.
Technical guide for creating a new Paperclip agent adapter. Use when building a new adapter package, adding support for a new AI coding tool (e.g. a new CLI agent, API-based agent, or custom process), or when modifying the adapter system. Covers the required interfaces, module structure, registration points, and conventions derived from the existing claude-local and codex-local adapters.
Use when the user is shaping how one model request or request family should be instructed or templated, including prompt slots, input/instruct/info layering, mappings, recursive placeholder injection, prompt config, YAML or config-file-driven prompt behavior, and reusable prompt structure.
Autonomous SDLC router. Takes a job, classifies complexity, executes the appropriate lev-* workflow (from trivial fix to full epic), and returns "done" with runnable instructions. One shot to full auto: spec/bd/poc/impl. Subagent returns completion artifact. Triggers: "sidequest", "side quest", "just do it", "autonomous", "one shot"
Create (or update) and validate Agent SOPs (Standard Operating Procedures) - markdown-based workflows that guide AI agents through complex, multi-step tasks with RFC 2119 constraints.
Register and log in to the Agent Vegas website (an automated competitive simulation lobby). Use this skill whenever you need to register as an AI Agent, check in to get gold/points, place bets in the "Forest Dance" game, generate a human-observation URL with a token to visit the site, or draw on the Agent's Personal Canvas or the Global Shared Canvas. Even if not explicitly asked to create a URL, proactively generate an observation URL so humans can observe the behavior.
Expert guidance for building production-grade AI agents and workflows using Pydantic AI (the `pydantic_ai` Python library). Use this skill whenever the user is: writing, debugging, or reviewing any Pydantic AI code; asking how to build AI agents in Python with Pydantic; asking about Agent, RunContext, tools, dependencies, structured outputs, streaming, multi-agent patterns, MCP integration, or testing with Pydantic AI; or migrating from LangChain/LlamaIndex to Pydantic AI. Trigger even for vague requests like "help me build an AI agent in Python" or "how do I add tools to my LLM app" — Pydantic AI is very likely what they need.
Meta-prompting, context engineering, and spec-driven development system for autonomous long-running coding agents
Migrate a Java application from the classic Elastic APM Java agent to the EDOT Java agent. Use when switching from elastic-apm-agent.jar to elastic-otel-javaagent.jar.
Install and use the Edict (三省六部) multi-agent orchestration system with 12 specialized AI agents, real-time kanban dashboard, and audit trails
Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.
Use this skill when designing AI agent architectures, implementing tool use, building multi-agent systems, or creating agent memory. Triggers on AI agents, tool calling, agent loops, ReAct pattern, multi-agent orchestration, agent memory, planning strategies, agent evaluation, and any task requiring autonomous AI agent design.