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Found 1,204 Skills
The Agent Tool Contract — 5 principles for designing tools agents call reliably: predictable signature, rich errors, token-efficient output, idempotency, graceful degradation. Includes anti-pattern table with 8 common mistakes.
Uses persistent markdown files for general planning, progress tracking, and knowledge storage (Manus-style workflow). Use for multi-step tasks, research projects, or general organization WITHOUT mentioning PRD. For PRD-specific work, use prd-planner skill instead.
Team-wide memory routing skill — routes agent queries to the optimal knowledge source (QMD hybrid search, daily memory, MEMORY.md) and enforces citation. Use when any agent needs to retrieve prior work, system config, skill docs, project status, or decisions. Triggers on "查知识库", "memory router", "qmd query", "find in docs", "what was decided", "how does X work", "项目状态", "之前的决策".
Browser automation CLI with Nstbrowser integration for AI agents. Use when the user needs advanced browser fingerprinting, profile management, proxy configuration, batch operations on multiple browser profiles, or cursor-based pagination for large datasets. Triggers include requests to "use NST profile", "configure proxy for profile", "manage browser profiles", "batch update profiles", "start multiple browsers", "list profiles with pagination", or any task requiring Nstbrowser's anti-detection features.
PixVerse CLI — generate AI videos and images from the command line. Supports PixVerse, Veo, Sora, Kling, Hailuo, Wan, and more video models; Nano Banana (Gemini), Seedream, Qwen image models; and PixVerse's rich effect template library. Start here.
Turn your AI into a full design team — 17 specialists that research, strategize, write, design, build, and review.
How to write Cavekit-quality kits that AI agents can consume effectively. Covers implementation-agnostic cavekit design, testable acceptance criteria, hierarchical structure, cross-referencing, cavekit templates, greenfield and rewrite patterns, cavekit compaction, and gap analysis. Trigger phrases: "write kits", "create kits", "cavekit this out", "define requirements for agents", "how to write kits for AI"
Design, build, run, and test Restate durable services, virtual objects, workflows, and AI agents across TypeScript, Python, Java, and Go. This skill should be used when the user mentions "restate", "durable execution", "virtual object", "restate service", "restate workflow", or "durable agent" or wants to build resilient backend services, AI agents, or workflows with automatic failure recovery. Also use when converting existing applications or migrating from workflow orchestrators to Restate. Use proactively when a project contains restate dependencies in package.json, requirements.txt, pyproject.toml, pom.xml, build.gradle, or go.mod.
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; add tracing or @observe; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, tracing, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval.
Run adversarial review on a PM artifact via the pm-critic sub-agent. Dispatches natively on Claude Code with the pm-skills plugin (invokes @agent-pm-critic); on non-Claude clients (Codex CLI, Cursor, Windsurf, Copilot, Gemini CLI) reads subagents/pm-critic.md and executes the system prompt inline. Returns findings graded P0/P1/P2/P3 with concrete fix suggestions per finding, plus a layered Status Summary section and machine-readable Status YAML block per master plan D26.
Tabular review — one row per document, one column per data point, every cell cited to source. Built for M&A diligence ("review these 200 target contracts for change-of-control, assignment, and MAC clauses") but works for any batch review that needs a spreadsheet out the other end. Use when user says "tabular review", "review grid", "build a grid", "extract these fields from these contracts", "review these documents for X, Y, Z", "give me a spreadsheet of", "batch review", or points at a folder of documents and asks to compare them.
Integrate Resend email service via MCP protocol for AI agents to send emails with Claude Desktop, GitHub Copilot, and Cursor. Set up transactional and marketing emails, configure sender verification, and use AI to automate email workflows.