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Found 2,174 Skills
Comprehensive guide to why and how AI agents should use email. Use when evaluating whether an agent needs email, comparing email infrastructure options (AgentMail vs Gmail API vs Resend vs SendGrid vs SES), understanding security risks like prompt injection via email and OAuth credential exposure, or exploring common agent email use cases such as customer support agents, sales outreach, verification flows, and browser automation.
Expert skill for using wanman, the open-source local agent matrix runtime that coordinates multiple Claude Code or Codex agents on your machine.
Use the local `5dive` CLI on a 5dive runtime VM to spawn, inspect, send to, and tear down sibling agents. Trigger this skill whenever the user asks for a worker, sub-agent, side task, parallel run, "another agent", "fan out", "delegate", or anything that needs more than one Claude/Codex/Gemini process running at once on the host. Also trigger when the user asks to inspect, restart, or pair an existing agent, when they mention `/var/lib/5dive/`, or when they need a machine-readable health check (`5dive doctor --json`). Always prefer `5dive` over running coding CLIs by hand — it is the only sanctioned way to keep agents under systemd.
Use this skill when users need to create Custom Lightning Types (CLTs) for Einstein Agent actions or structured input/output schemas. Trigger when users mention CLT, Custom Lightning Types, JSON schemas for agents, type definitions, lightning__objectType, or editor/renderer configurations. This is complex - always use this skill for CLT work.
MCP Server connecting AI agents to 70+ Brazilian public APIs for government data, economy, legislation, transparency, judiciary, elections, environment, health, education, and more
Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol.
Use when the user wants a task done much faster through parallel work, concurrent agents, batched tool calls, isolated worktrees, or many independent verification lanes without losing correctness.
Fast, accurate code search for AI agents using ~98% fewer tokens than grep+read. Indexes any local or remote repository in under a second (~250ms on CPU, no GPU or API key needed). Supports natural-language and symbol queries, semantic similar-code discovery, and MCP server integration for Claude Code, Codex, Cursor, and OpenCode. Python library available for programmatic use. Triggers on: semble, code search, semantic code search, semble search, token-efficient search, find code, code search mcp, agent code search, semble find-related, semble savings.
Measure and improve the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For deployment, use agent-platform-deploy.
Vision intake conversation followed by generation of three product documents — `docs/product-vision.md` (strategy and brand), `docs/prd.md` (technical spec for coding agents), and `docs/product-roadmap.md` (phased build plan with task checkboxes). Also captures the founder's answers as `docs/VISION.md`. Use when the founder says "plan my product", "plan a product", "define my vision", "generate a PRD", "create a roadmap", "spec out my idea", "help me build something", or wants to convert an idea into shippable spec documents.
Create, rewrite, shorten, or review /goal prompts for Codex, Claude Code, and other coding agents. Use only when the user explicitly requests /goal text or asks to convert a task into /goal; never start the goal automatically.
Use when a developer asks their coding agent to initialize or work with moldea; plan an AI- or agent-enabled system and decide what should be agents versus deterministic software, services, tools, or human control; create or refine an AI agent or its behavioral system, including instructions, descriptions, handoff descriptions, tools, skills, schemas, variables and providers, routing or handoffs, bindings, or runtime integration; evaluate, reconcile, or validate an existing moldea system; or make ordinary behavior-affecting repository changes that may require maintaining an adopted moldea system. Loading the skill does not adopt moldea: initial adoption still requires explicit developer intent, while relevance-triggered maintenance applies once a repository uses or is adopting moldea.