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Found 5,018 Skills
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"
Use when an AI agent should run protocols or workflow tests against kairos-dev (KAIROS MCP in this repo's dev environment). Covers AI–MCP integration and workflow-test flows; MCP-only, reports/ output.
Interact with GitLab via the glab CLI. Supports five MR workflows — Read (summarize), Review (full code/security/QA review), Fix (review + implement), CI Fix (fix pipeline failures), and Feedback (address review comments). Trigger whenever the user provides a GitLab MR URL or says anything like "อ่าน MR", "ดู MR", "check MR", "review MR", "ช่วย review MR นี้", "ตรวจ MR", "แก้ตาม MR", "fix MR", "fix CI", "fix pipeline", "แก้ pipeline", "แก้ตาม comment", "แก้ตาม feedback", "address feedback", or just pastes a GitLab MR URL. Also supports listing MRs, viewing MR status, checking CI/CD pipelines, approving MRs, and other glab operations. Trigger on "check pipeline", "list open MRs", "pipeline failed", or any GitLab-related task.
Design and enforce AI-friendly verification for a GRACE project. Use when modules need stronger automated tests, traceable logs, execution-trace checks, or verification that is robust enough for autonomous and multi-agent workflows.
Autonomous AI Project Agent & Cron Task Runner. Orchestrates repetitive AI-driven engineering tasks with state persistence (Memory) and advanced workflow controls.
Manage video production projects including character profiles, project manifests, workflow history, and asset tracking. Use when creating new projects, managing characters, or tracking production state.
Interactive model selection workflow with paginated navigation. Use when users want to select a model interactively - guides them through provider selection then model selection using the question tool with pagination support for large lists.
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.
Let's Enhance integration. Manage data, records, and automate workflows. Use when the user wants to interact with Let's Enhance data.
Build and publish Domo apps with dist workflow and first-publish ID handling.
Alpha scouting workflow using Messari x402. Scans mindshare gainers, trending topics, and news to surface emerging narratives and high-momentum assets. Total cost ~$1.25 USDC per run.
Architect and provision enterprise Azure infrastructure from workload descriptions. For cloud architects and platform engineers planning networking, identity, security, compliance, and multi-resource topologies with WAF alignment. Generates Bicep or Terraform directly (no azd). WHEN: 'plan Azure infrastructure', 'architect Azure landing zone', 'design hub-spoke network', 'plan multi-region DR topology', 'set up VNets firewalls and private endpoints', 'subscription-scope Bicep deployment'. PREFER azure-prepare FOR app-centric workflows.