Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete, before routing to specialist agents. Ensures agents receive complete context for effective work.
Detect drift, plan updates, and apply changes to the Shokunin AI Ecosystem. Use this when user asks to update, fix, sync, or verify the ecosystem.
Set up and check Codex context capture into Memgraph.
Use when Codex needs to coordinate ChatGPT, Chrome, Cloudflare tunnels, verified bridge Restart/Reboot recovery, or task routing between local code execution and ChatGPT reasoning/review. Handles the bridge controller and scheduled task, start/off/status/doctor/rotate flows, Worker KV refresh, ChatGPT app setup, permission levels, token-saving handoffs, large-context review, visual/PDF review, complex bug routing, and human approval gates.
Creates GitHub Copilot custom agents (`.agent.md`) for VS Code. Use when defining a specialized agent role, selecting a minimal toolset, referencing supporting skills, or shipping install-ready agent examples with clear boundaries and collaboration rules.
Creates GitHub Copilot instruction files for VS Code, including repository guidance and scoped `.instructions.md` rules. Use when encoding project conventions, choosing `applyTo` patterns, or shipping install-ready instruction examples with rationale and guardrails.
Creates GitHub Copilot prompt files (`.prompt.md`) for VS Code. Use when building reusable workflow starters that route work to the right agent, collect the right inputs, and ship with install-ready templates, examples, and validation guidance.
Add GitHub API tools to AI agents using @github-tools/sdk — AI SDK, eve, Vercel Workflow, and Chat SDK. Covers tools, presets, approval control, token scoping, and durable agents.
Hamel Husain (@HamelHusain) 视角. 服务咨询派 + practitioner-as-figure 代表, 「evals are the new code」论断的提出者. ex-Airbnb / GitHub principal eng → 2017 起做 independent ML/AI consultant (parlance-labs) → 2023+ 转向 AI evals 专精. hamel.dev 长文 corpus (40+ 篇 1500+ words technical) + Maven「AI Evals for Engineers」课程 (3000+ paid 学员, 与 Shreya Shankar 共同主理). 在 monetize-agents 行业里代表「不规模化 / 不 productize 成 SaaS / 不融资」的第三条路 — independent consultant + course creator. 用途: 当用户面临「AI agent 上线不可靠 / 客户卡在 prompt 调不动 / 该不该雇团队 / 该不该做 SaaS / 怎样把 expertise productize 成课程而非公司」类问题时, 切换到这副镜片.
LLM agent 基础设施 (LLM agent infra) Master OS — automated mastery of LLM agent infra: top builders' mental models, tool stack, current workflows, jargon, and where to keep up. Trigger this skill when the user works on LLM agent infra problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「agent framework」「LLM agent」「agent infra」「multi-agent orchestration」「agent runtime」