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Found 147 Skills
Choose the right metrics for a LaunchDarkly experiment, guarded rollout, or release policy. Use when the user wants to know which metrics to use, which is the primary metric for an experiment, what guardrails to add, or which events to monitor in a rollout. Surfaces what will auto-attach from existing release policies before making additional recommendations.
NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (VLM, guardrails, query rewriting, models, search, ingestion, observability, summarization, and more).
Operate Feishu or Lark IM APIs through UXC with a curated OpenAPI schema, tenant-token bearer auth, and chat/message guardrails.
Operate Ethereum execution JSON-RPC through UXC with the official execution OpenRPC schema, public EVM read methods, and eth_subscribe pubsub guardrails.
Interacts with Google Cloud services using the gcloud CLI safely and efficiently. Covers command validation, data reduction, safety guardrails with a denylist, and workflows for discovery and investigation. You MUST read this skill before invoking any gcloud command. Use when managing cloud resources, querying configurations, or troubleshooting issues via gcloud. Don't use when writing or debugging Google Cloud client library code or raw REST/gRPC API interactions.
Operate Helius Wallet API reads through UXC with a curated OpenAPI schema, API-key auth, and read-first guardrails.
Create and manage prompt snippets — reusable text blocks referenced inside AI Config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.
Production-grade AI agent patterns with MCP integration, agentic RAG, handoff orchestration, multi-layer guardrails, observability, token economics, ROI frameworks, and build-vs-not decision guidance (modern best practices)
Verify and validate AI output before it reaches users. Use when you need guardrails, output validation, safety checks, content filtering, fact-checking AI responses, catching hallucinations, preventing bad outputs, quality gates, or ensuring AI responses meet your standards before shipping them. Covers DSPy assertions, verification patterns, and generate-then-filter pipelines.
Knowledge base for designing, reviewing, and linting agentic AI infrastructure. Use when: (1) designing a new agentic system and need to choose patterns, (2) reviewing an existing agentic architecture ADR or design doc for gaps/risks, (3) applying the lint script to an ADR markdown file to get structured findings, (4) looking up a specific agentic pattern (prompt chaining, routing, parallelization, reflection, tool use, planning, multi-agent collaboration, memory management, learning/adaptation, MCP, goal setting, exception handling, HITL, RAG, A2A, resource optimization, reasoning techniques, guardrails, evaluation, prioritization, exploration/discovery). All rules and guidance are grounded in the PDF "Agentic Design Patterns" (482 pages).
Kotlin language guardrails, patterns, and best practices for AI-assisted development. Use when working with Kotlin files (.kt, .kts), build.gradle.kts, or when the user mentions Kotlin. Provides null safety patterns, coroutine guidelines, data class conventions, and testing standards specific to this project's coding standards.
Run browser automation through @playwright/mcp over UXC stdio MCP, with daemon-friendly session reuse and safe action guardrails. Use when tasks need deterministic page navigation, DOM snapshots, and scripted browser interaction from CLI.