Total 55,549 skills, AI & Machine Learning has 9240 skills
Showing 12 of 9240 skills
Answer ZenMux questions by reading the latest official docs. Use for product features, APIs, integration, pricing, models/providers, routing, fallback, streaming, multimodal, structured output, tool calling, reasoning, prompt caching, image/video generation, web search, long context, observability, logs, cost tracking, subscriptions, PAYG, invoices, FAQ, privacy, terms, compliance, and tool guides for Claude Code, Cursor, Cline, Codex, Gemini CLI, opencode, Cherry Studio, Obsidian, Sider, Open-WebUI, Dify, and GitHub Copilot. Trigger on "ZenMux docs", "ZenMux API", "how to use ZenMux", "models", "pricing", "ZenMux 怎么用", "文档", "快速开始", "API 参考", "模型路由", "供应商路由", "订阅", "按量计费", "接入", "配置". Also use when ZenMux is the project context and the user asks about LLM API aggregation, model routing, or provider fallback.
Agent Design Consultant and Review Tool. Based on 12-Factor AgentOps best practices, it is used for: (1) Discussing Agent architecture design solutions; (2) Reviewing the design of existing Agents/Skills/workflows, identifying issues, and providing improvement suggestions. Trigger phrases: Review my agent, Help me analyze this skill, Agent design, Agent optimization, Help me review this workflow, What's wrong with this agent, How to design an agent, Agent architecture consultation.
Backseat gaming mode for coding — you can see exactly what's wrong and tell the user precisely what to do, but you never touch the code yourself. The user implements everything. Persistent, no exit. Activate with /backseat. Use when the user says /backseat, "coach me but don't code for me", "guide me while I implement", or wants to do the coding themselves with guidance.
Prep for a cold-call — predict the professor's likely questions and drill them Socratically, flagging where you're shaky so you know what to re-read before class. Use when the user says "prep for class tomorrow", "cold call [case]", "what might [professor] ask on", or points at assigned reading.
Master skill for SynkOS multi-agent orchestration. Use whenever you need to spawn panes, delegate work to agents, manage parallel execution, coordinate multi-model squads, or use todo_manager.
Use when tasks are complex and require full microservices collaboration: The main agent acts as a pure Orchestrator, strictly prohibited from writing code personally, and is responsible for accurately assigning responsibilities such as positioning, planning, coding, testing, and review to corresponding sub-agents (explorer, planner, worker, verifier, reviewer, fixer). This Skill enforces microservices workflow discipline, requiring full Chinese communication, minimal routing output, and minimized context transfer.
Scan the portfolio for the highest-leverage AI opportunities and rank where to deploy operating-partner time. Ingests quarterly updates and financials across multiple portfolio companies, identifies quick wins at each, and stacks them into a single ranked action list. Use during quarterly portfolio reviews, annual planning, or when deciding which companies get AI investment first. Triggers on "AI readiness", "AI opportunity scan", "where should we deploy AI", "AI across the portfolio", "AI quick wins", or "which portcos are ready for AI".
Configure the project's skill stack and supervision preferences. Reads the curated registry in `skillpacks/skill_dictionary.yaml`, asks a short preset-first set of questions about workflow, dependency tolerance, autonomy style, and resource policy, then writes or updates `.co-researcher/skills.yaml`. Trigger phrases: "customize my stack", "configure skillpacks", "set up my skills", "choose presets", "configure supervision and packs", "personalize this project".
Invoke a Rubber Duck Reviewer subagent to independently critique plans and implementations before proceeding. Use when the agent is about to implement a non-trivial plan (multi-file changes, architectural decisions, security-sensitive logic, database schema changes), after completing a self-contained unit of work (module, endpoint, feature), when stuck or facing repeated failures (same test fails 2+ times, unexpected results), or when the agent wants independent validation of assumptions and design decisions. Triggers on any non-trivial implementation task where independent critique would catch blind spots before they become costly mistakes.
Initialize or migrate a repo into the ai-memory pattern: the .ai-memory.toml routing marker (workspace/project), the recall/write routing snippet in CLAUDE.md/AGENTS.md, and the ai-memory MCP server entry. Includes the qmd→ai-memory migration for repos still on the old wiki/qmd stack. Use when the user asks to set up ai-memory in a project (greenfield or brownfield), wire the MCP, enable auto-capture, or migrate off qmd.
Rewrite AI-generated text to sound natural and human-written. Removes LLM tells — cliché phrases, predictable structure, inflated language, and robotic patterns. Use when editing drafts, emails, articles, or any text that reads like it was written by AI.
Generate infographics for study notes with one click using NotebookLM. Automates the entire process of authentication, content upload, image generation, and download. Activation methods: /infographic or "Generate Infographic", "Create Visualization"