1.1천건 · 관련도순
Check whether the iMessage channel can read messages on a Mac and review who is allowed to contact the assistant. Get guidance on required permissions, current access rules, and the next setup step.
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Design hands-on workshops with short explanations, immediate exercises, and quick self-checks. Use brief dialogue between a teacher and an assistant to guide practice, then finish with a small project that combines what learners have tried.
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Connect NanoClaw to an existing Signal account through signal-cli, a command-line tool for Signal. Use personal chats, Note to Self, or group conversations to exchange messages with an AI assistant without registering a new number.
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Connect NanoClaw to Slack so you can chat with an AI assistant in direct messages and channels. The skill guides connection setup, credentials, installation, and checks, with support for Socket Mode or webhook event delivery.
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Guide a new user through optional account connections, a preferred assistant voice, and a review of current work. Save a lasting profile, then propose and set up specific help after confirming behavior and timing.
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Open the assistant’s latest rendered message in Plannotator and return specific annotations for revision. The skill distinguishes requested edits from approval, approval with notes, and a closed session, so each review result receives the appropriate response.
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Analyze Mixpanel data with Python to explore usage trends, conversion steps, returning users, and differences between user groups. Also manage business context documents that explain an organization's structure and goals to AI assistants.
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Create specialized assistants for Cursor that work with their own context, separate from the main conversation. Define responsibilities, instructions, output formats, model choices, and execution settings for reusable workflows such as verification, debugging, and documentation.
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Inspect a project's code, history, tests, and documentation, then ask three focused questions to recommend an AI-assisted development approach. Receive one combination of methods, reasons for the choice, and practical first steps tailored to the project.
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Create and refine YAML prompts for AI assistants using structured roles, requirements, and output instructions. Organize reusable templates with variable placeholders, and check indentation, braces, headings, and formatting for consistency.
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Refine AI-assisted academic drafts into natural scholarly prose while preserving citations, statistics, and methodological details. Choose how extensively to revise and which sections to edit, then review a report comparing the original and revised text.
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Guides migration, provisioning, search, log-analytics, trace-analytics, and Agentic AI Assistant workflows for Amazon OpenSearch Service and Serverless across six capabilities — migration (Solr/ES/self-managed into AOS/AOSS, schema/query translation, sizing, cutover); provisioning (domain + AOSS lifecycle, upgrades, FGAC, monitoring); search (vector / semantic / hybrid / RAG with Bedrock); log-analytics (PPL, OSI, anomaly detection, Dashboards); trace-analytics (OTel spans, service maps, Data Prepper); ai-assistant (natural language data exploration, incident investigation, root cause analysis). Triggers on OpenSearch, AOS, AOSS, Elasticsearch, Solr, vector/k-NN/semantic/hybrid search, RAG, log analytics, PPL, trace analytics, ISM, FAISS, HNSW, Migration Assistant, UltraWarm, OR1, query my data, analyze logs, investigate errors, root cause analysis.
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Design conversational assistants with context management, response generation, and LLM integration. Use when building chatbots or planning assistant application architecture.
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Evaluate performance fairly when output is AI-assisted — what still measures the human, what now measures the tooling, and how to run the review conversation. Use when reviewing someone whose work is heavily AI-assisted, when output volume stopped meaning anything, when calibrating a team with uneven AI adoption, or when writing review criteria for the AI era. Produces review guidance: a what-measures-whom analysis, rewritten criteria, calibration rules for mixed-adoption teams, and conversation scripts. For the general review document use performance-review; for redesigning the role itself use role-redesign-for-ai.
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Plan a trip that actually works with a disability or access need — confirm real accessibility (not just 'accessible' labels), book the assistance in advance, plan for equipment and medication, and build in the contingencies for when access breaks down. Use when someone says 'plan an accessible trip', 'travelling with a wheelchair/disability', 'book assistance for my flight', or 'will this hotel actually work for me'. Produces an access-verified itinerary, an assistance-booking checklist, an equipment/medication plan, and contingency scripts. Verify specifics with providers.
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Read entity states and call services on your Home Assistant instance. Service calls are confirmed first. Trigger phrases: home assistant, smart home, turn on the light.
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Configure Temps as an MCP (Model Context Protocol) server so AI assistants can interact with a Temps instance directly -- listing/inspecting projects and deployments, and (when write mode is enabled) triggering deployments with human confirmation. Use when the user wants to: (1) Set up the Temps MCP server, (2) Connect Claude Code/Desktop, Codex, Cursor, VS Code, Windsurf, or Zed to Temps, (3) Add Temps tools to an AI assistant, (4) Test the MCP wizard locally, (5) Manage multiple Temps MCP connections (dev, staging, prod, or several local dev slots) side by side. Triggers: "temps mcp", "configure temps tools", "add temps to claude", "temps ai assistant", "mcp server setup", "mcp add", "test the mcp wizard".
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Assistive Access implementation for cognitive accessibility including simplified scenes, navigation icons, runtime detection, and design principles. Use when optimizing apps for Assistive Access mode.
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Home Assistant YAML configuration and automation. The most common skill for general HA questions. Covers automations, blueprints, scripts, scenes, template sensors, Lovelace dashboards, Mushroom cards, packages, helpers, presence detection, voice Assist, calendar automation, Jinja2 templates, notification patterns, energy monitoring, Matter devices, automation debugging and traces, dashboard sections view, and trigger-based template entities.
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After scaffolded practice, run an unassisted check — a problem with no AI help. Separates what the learner can do with support from what they can do independently. Critical for preventing phantom attainment.
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Scan Claude Code session transcripts to find the strongest user reactions to assistant instruction-following failures, reconstruct the triggering assistant output, and render a shareable terminal-style PNG artifact. Use when you want to surface and share a moment where the assistant completely missed what was asked — captures what they were doing, what Claude said, and how the user reacted. Triggers on: "rtfp", "read the fucking prompt", "find my worst AI moment", "make a rage screenshot from this session".
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Design, create, update, and verify Vapi Squads and documented handoff tools through the public API. Use for choosing a single assistant versus a multi-assistant Squad, persistent or transient members, entry-member ordering, specialization boundaries, context engineering, variable extraction, model-specific handoff patterns, assistant-version pins, and safe Squad updates.
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Opt-in recreation of the Bun + TypeScript framework used to build Vapi's landing-page voice agents from a ROUGH_DRAFT.md spec, including a scenario registry, language stacks, prompt composer, assistant builder, and idempotent bootstrap script. Use only when the user explicitly invokes vapi-bootstrap-framework or specifically asks to reproduce or experiment with the landing-page-agent architecture. Do not use for ordinary Vapi assistant builds; use create-assistant, create-tool, create-squad, and vapi-prompt-builder instead. Targets Bun + TypeScript + @vapi-ai/server-sdk.
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Personal finance assistant for budgeting, savings goals, investment tracking, debt optimization, tax preparation, insurance review, net worth tracking, and financial scenario modeling. Supports multi-currency, bank statement import (CSV/MT940/OFX), and locale-based tax rules. Use for any personal finance question: budget planning, expense tracking, portfolio allocation, FIRE calculations, debt payoff strategies, mortgage comparisons, tax deductions, insurance coverage, retirement planning, and life events with financial impact such as marriage, buying a house, changing jobs, having a baby, or going freelance.
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