> Claude as the trainer. Walks an SMB owner through connecting their first two tools, runs one recipe to prove immediate value, interviews them about their business (industry, size, top three headaches), stores that context persistently so every other skill benefits, and sets a weekly check-in "setup," "help me get set up," "get started," "help me get started," "get me started," "what can you do," "I'm new to this," or is in their first session.
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill smb-onboard
Four moves: connect two tools → run one recipe → capture business context → set a weekly rhythm. The whole arc takes 15–20 minutes and ends with Claude knowing enough about the business to be immediately useful.
User: "get me started"
→ Assess what's already connected; pick the best 2 tools to connect first
→ Guide connection of each tool (one at a time)
→ Run one recipe against live data to prove value
→ Ask 5 business questions one at a time; store answers to persistent memory
→ "Each Monday, say 'weekly check-in' — I'll pull your numbers and flag anything urgent."
Whenever a connector comes up — recommending one, naming what to try next, or clarifying mid-flow — describe what Claude will be able to do once it's connected, not what the platform itself is or sells. Owners already know what HubSpot, QuickBooks, Gmail, and Calendar do; they don't need a product pitch from us.
## Business context block already exists in the owner's CLAUDE.md or memory, read it first — then skip to the return-session path: show the existing profile, ask what's changed, update only the fields that changed. Do not re-interview from scratch.Name the two functions we want (e.g. "a place to track customers and deals" and "your inbox") — not the platform features. One short sentence each, max. Then ask whether the owner uses a supported tool for each.
For each function, branch:
Connect one tool at a time — never ask the owner to configure two simultaneously. See reference/gotchas.md for the failure pattern this replaces.
## Business context using the exact format in reference/onboard-checklist.md. If a memory file already exists, update only the ## Business context section — do not touch other content. Confirm: *"Saved. Every skill from here will know your business."*## Business context block already exists, show current vs. proposed before writing any changes.This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.
Run evaluations for one, multiple, or all skills using the agent orchestration framework. Make sure to use this skill whenever the user asks to run evals, test a skill's performance, run benchmarks, or compare baseline versus with-skill execution.
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.
Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.
亚马逊卖家专用的 skill 创建器(中文)。当用户想把一个亚马逊运营/自媒体/日常工作流程变成可复用的 skill 时使用。触发场景包括但不限于:用户说"我想做一个 skill""把这个流程变成 skill""帮我写个自动化""优化我已有的 skill""给这个工作流做个自动化",即使用户没用"skill"这个词,只要在描述"以后每次都这样做"的重复性工作时也应触发。本 skill 的核心差异:强制用户先回答 6 个业务问题(业务目标/过去做法/具体步骤/方法论/调用方式/期望输出)再进入创建流程,防止产出空洞 skill。Create new skills, improve existing skills, run evals and benchmarks — tailored for Amazon sellers with a Chinese-first workflow.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Take anthropics/smb-onboard from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.