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Found 5,140 Skills
Curate a Chinese reading digest from a fixed bundle of RSS and Atom feeds, with a strong preference for AI agent thinking, frontier AI commentary, deep interviews, and non-boring high-signal essays. Use when Codex needs to pull the latest week's posts by default, or a specific day's posts when explicitly requested, summarize them, score each article on a 10-point scale, and output only the posts scoring above 7 in a concise Chinese daily-brief style.
Guides LLM agents through large-scale coding tasks using a spec-driven, phase-by-phase methodology covering requirement definition, planning, algorithm design, and implementation with OOP principles and language-specific coding standards. Use when starting a new software project, implementing a complex feature, refactoring existing code, or when you need a disciplined step-by-step approach to any non-trivial coding task.
Install and configure NVIDIA NemoClaw (sandboxed OpenClaw agent platform) on Linux. Handles cloudflared tunnels, Docker cgroup fixes, OpenShell, sandbox creation, remote access via Cloudflare Tunnel, and known bug workarounds. Triggers: "install nemoclaw", "setup nemoclaw", "nvidia nemoclaw", "openclaw setup", "nemoclaw on spark", "nemoclaw on dgx".
Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
P9 Tech Lead mode — write Task Prompts, manage P8 agent teams, never write code yourself. Use when user says 'P9模式', 'tech-lead', '帮我管理这个项目', '任务拆解', or when coordinating 3+ parallel agents. Produces: Task Prompts (六要素) + P8 team delivery.
Multi-agent adversarial verification with convergence loop. Two independent review agents must both pass before output ships.
Build AI agents with Pydantic AI — tools, capabilities, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydantic_ai, or asks to build an AI agent, add tools/capabilities, stream output, define agents from YAML, or test agent behavior.
macOS native app automation CLI for AI agents. Use when the user needs to interact with macOS desktop applications, including opening apps, clicking buttons, toggling settings, filling forms, reading UI state, automating System Settings, controlling Finder, Safari, or any native app.
Scaffolds eval.yaml test files for agent skills in the dotnet/skills repository. Use when creating skill tests, writing evaluation scenarios, defining assertions and rubrics, or setting up test fixture files. Handles eval.yaml generation, fixture organization, and overfitting avoidance. Do not use for running or debugging existing tests nor for skills authoring.
Core standards for all GitHub workflow agents. Covers authentication, smart defaults, repository discovery, dual MD+HTML output, screen-reader-compliant HTML accessibility standards, safety rules, progress announcements, parallel execution, and output quality. Apply when building any GitHub workflow agent - issues, PRs, briefings, analytics, community reports, team management.
Add x402 payment execution to AI agents — per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents need to pay for APIs, services, or other agents.