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概要と使いどころ
Runbook for turning a new HubSpot lead into a researched, personalized follow-up email and a proposed call slot for {{projectName}}. Covers company research, the sales playbook (positioning, tone, qualifying questions), HubSpot record updates, Google Calendar availability, and the human approval gate before anything sends.
日本語の概要は準備中です。原文の説明を表示しています。
Runbook for turning a HubSpot renewal date into a prepped packet and a drafted outreach email for {{projectName}}. Covers the 90/60/30-day windows, what belongs in a renewal packet, at-risk signals, Google Calendar cross-checking, and the human approval gate before anything sends or discounts.
日本語の概要は準備中です。原文の説明を表示しています。
Fast first-pass NDA review for {{nda_label}}. Saves a new inbound NDA into {{nda_folder}}, checks it clause-by-clause against our standard NDA positions, drafts redlines on what deviates, and drafts a flag summary for counsel — holding every redline and flag for counsel's sign-off. Never signs or sends.
日本語の概要は準備中です。原文の説明を表示しています。
How to CONNECT Slack (one command — `kortix channels connect`, prints a one-click install link) and how to answer in Slack as a teammate. Covers the live plan-block stream (`slack step` with --detail/--output, `slack send` to finalize the answer), file uploads, posting to other channels/threads, reactions, search, message editing/deletion, and the tone the bot should use. Load this when the user asks to connect/set up Slack, when the turn is triggered from Slack (the prompt mentions a Slack workspace/channel/thread, or `$SLACK_CHANNEL_ID` is set in the env), when this session messages someone in Slack and needs their reply to come back (thread binding, `slack bind-thread`), or when the user asks how to do anything in Slack.
日本語の概要は準備中です。原文の説明を表示しています。
The per-call runbook for turning a finished sales call's transcript — found in the connected Google Drive folder — into an updated HubSpot deal (or a flagged handoff when no deal matches), filed follow-up tasks in {{linear_team}}, and a drafted recap email — held for the rep's approval before it sends.
日本語の概要は準備中です。原文の説明を表示しています。
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
日本語の概要は準備中です。原文の説明を表示しています。
Research first, then draft. This skill never sends generic outreach - it always researches the prospect first to personalize the message. Works standalone with web search, super...
日本語の概要は準備中です。原文の説明を表示しています。
Guide a new Kortix user through their first session: set expectations about what Kortix is, turn a vague ask into a strong first prompt, deliver one real result, then surface deeper capabilities (connectors, memory, triggers, subagents, marketplace) and plant a retention hook. Use when the user is new, asks 'what can you do / how does this work / where do I start', sends a vague or exploratory first message, or has no prior history in this project. Do NOT use when they arrive with a specific, well-formed task — just do it.
日本語の概要は準備中です。原文の説明を表示しています。
Send prints to Bambu Lab printers through Bambu Connect, Bambu Lab's official app for printing from other software, or Bambu Studio. Use when the user wants to print a sliced `.gcode.3mf`, a Bambu `.gcode` or an unsliced model on a Bambu Lab printer, over Bambu Cloud or LAN. The agent opens the file in the app; the user picks the printer and starts the print there.
日本語の概要は準備中です。原文の説明を表示しています。
Drives cmux as an agent cockpit to boot, race, and monitor visible agent teams. Mac-only. USE WHEN cmux, agent cockpit, boot an agent team, orchestrate agents, three-tier orchestration, agent race, needle-in-haystack hotfix, agent fleet, 2x2 fleet, watch/monitor my agents, scale compute to scale impact, send a prompt to a running agent, multiplexer, terminal cockpit, orchestrator lead worker. NOT FOR one-shot in-harness subagents with no terminal to watch (use Agent/Workflow), the Pulse dashboard itself (cmux feeds it, use Pulse), browser deploy-verification (use Interceptor), or Linux/Windows (cmux is Mac-only — use tmux).
日本語の概要は準備中です。原文の説明を表示しています。
Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components only.
日本語の概要は準備中です。原文の説明を表示しています。
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; 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, traced evals, 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; for instrumenting an app with DeepEval tracing, @observe, or framework integrations (use the `deepeval-tracing` skill); or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill).
日本語の概要は準備中です。原文の説明を表示しています。
Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents, LlamaIndex, Pydantic AI, CrewAI, and others); choose between a native integration and manual instrumentation; set span types, tags, or metadata; or send DeepEval-SDK traces to Confident AI's Observatory. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill), or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill). This skill is purely DeepEval-SDK instrumentation — producing well-formed traces, not running evals.
日本語の概要は準備中です。原文の説明を表示しています。
Develop, debug, and test LangBot plugins. Use when creating new LangBot plugins, fixing plugin bugs, setting up a LangBot test environment, or testing plugins via WebSocket. Covers plugin component architecture (EventListener, Command, Tool), the plugin SDK API (invoke_llm, get_llm_models, send_message, plugin storage), common pitfalls, and automated WebSocket-based testing. Triggers on "langbot plugin", "lbp", "GroupChatSummary", "plugin debug", "langbot test".
日本語の概要は準備中です。原文の説明を表示しています。
tmux 自动化操控：用 scripts/auto-tmux.sh 安全读取、发送、巡检、救援、录制 session|window|pane，用 swarm-state.sh 管理蜂群任务/锁/状态，并基于 oh-my-tmux 组织多 AI 终端协作。触发：capture-pane、send-keys、批量巡检、蜂群 AI 协作、卡死救援、tmux 工作台初始化。
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes your recent Codex chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.
日本語の概要は準備中です。原文の説明を表示しています。
Connect Codex to any app via the Composio CLI. Send emails, create issues, post messages, update databases - take real actions across Gmail, Slack, GitHub, Notion, and 1000+ services from the terminal.
日本語の概要は準備中です。原文の説明を表示しています。
Connect Claude to external apps via the Composio CLI. Use this skill when the user wants to send emails, create issues, post messages, or take actions across Gmail, Slack, GitHub, Notion, and 1000+ services from the terminal.
日本語の概要は準備中です。原文の説明を表示しています。
Create Slack message drafts in the user's own composer, as the user, through their per-user Slack OAuth. Drafting only — the user reviews and sends from Slack.
日本語の概要は準備中です。原文の説明を表示しています。
Draft Gmail in the user's own voice from the voice profile built by email-voice-profile. Drafts only; the user reviews and sends.
日本語の概要は準備中です。原文の説明を表示しています。
Send a change to upstream qm without leaking organization-specific context. Use when asked to "upstream this", "open a PR against qm", "contribute this back", or when explicitly asked to contribute a downstream change.
日本語の概要は準備中です。原文の説明を表示しています。
Make a PR, wait for CI with bounded transport retries, independently review, and merge only when all gates pass.
日本語の概要は準備中です。原文の説明を表示しています。
Collect and submit feedback about Superset (bug reports, feature requests, or general feedback) privately to the Superset team or as a public GitHub issue. Use when the user wants to report a Superset bug, request a feature, or send feedback about Superset.
日本語の概要は準備中です。原文の説明を表示しています。
Spawns pedestrians that wander autonomously via WalkerAIController, and destroys them in the correct order. Pairs each walker.pedestrian.* with a controller.ai.walker, places them on the navigation mesh, and sends them to random navmesh points at walking speed. Use when the user asks to "spawn pedestrians/walkers", "add people/crowds", "make pedestrians walk around", or "remove the walkers". Reads the world settings first: if the world is already synchronous it never ticks it, and if it is asynchronous it makes it synchronous and drives the clock itself.
日本語の概要は準備中です。原文の説明を表示しています。