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Edit, trim, cut, caption, subtitle, reframe, combine, add background music to, or export video, audio, and image files through Cassette. Use this skill whenever the user asks to change, preview, or render a media file in the project — even if they never say "Cassette" or name a tool — for example "trim the intro off demo.mp4", "add subtitles to this clip", "make me a 30-second cut with music", "why is there dead air at the start". Drives the local Oh My Cassette stdio MCP tools in Codex or Claude as one multi-turn conversation with the Cassette agent, with per-turn timeline previews, guided questions, and rendering only on explicit export.

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Use when the user wants to know if something works — the answer requires running code, not analyzing it. Output is a verdict backed by evidence: passed, failed, or broken. Primary triggers: 'run the tests', 'does X still work after my change?', 'did the merge break anything?', 'verify the fix worked', 'check if the endpoint returns X', 'confirm nothing regressed', 'run tests/unit/test_foo.py', 'let me know the results', 'make sure my changes didn't break anything'. Hard stops — do NOT use for: reviewing test code for quality/coverage gaps, debugging why test infrastructure/databases/seed scripts are misbehaving, writing or fixing tests, diagnosing root causes of unexpected behavior. The deciding question: is the user asking for the result of executing something, or asking for help understanding/analyzing/improving something? If it's the latter, use diagnose or review-code instead.

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Use this skill for any Python backend work in this project: building FastAPI endpoints, writing service functions, defining Pydantic/SQLModel schemas, running Alembic migrations, or debugging 422 errors. Essential for authentication and authorization patterns — setting up get_current_user, is_superuser checks, admin-only guards, role-based access, and dependency injection chains like Depends(). Also covers middleware, background tasks, async SQLAlchemy sessions, ORM relationship loading, and request/response design. Activate whenever the question involves Python API code, FastAPI patterns, or backend architecture in this codebase. Not for frontend, Docker, CI/CD, or infrastructure.

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Use when the user wants to work on a Claude Code skill file (SKILL.md): writing one from scratch, testing whether an existing one works well, running evals or benchmarks, improving its instructions, or fixing why it isn't triggering. Triggers on: 'make a skill for X', 'test this skill', 'run evals on my SKILL.md', 'touch-skill', sharing a SKILL.md and asking if it's ready to ship. The key signal is intent to create, validate, or improve a skill — not just mention one. Do NOT trigger for general Claude Code questions, hook debugging, or CLAUDE.md configuration.

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This skill should be considered when you need to answer reflow machine maintenance questions or provide detailed guidance based on thermocouple data, MES data or defect data and reflow technical handbooks. This skill covers how to obtain important concepts, calculations, definitions, thresholds, and others from the handbook and how to do cross validations between handbook and datasets.

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Allows you to view the user's screen as well as several hours of history. Use when the user makes a reference to their recent work, for which it'd be helpful to see the screen. This skill MUST be used whenever you need to resolve ambiguity in a user request, where the user hasn't specified enough context to do the task. Examples include disambiguating the specific user/app/document/error the user is referring to. You must also use this skill if the user asks about any question regarding Chronicle or asks what you can see from the screen.

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Generate targeted clarifying questions (2-3 max) that challenge vague requirements and extract missing context. Use after request-analyzer identifies clarification needs, before routing to specialist agents. Helps cto-orchestrator avoid delegating unclear requirements.

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。

Uncover root causes through recursive Why questioning and consequence exploration. Use before acting on problems.

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。

Manages Neo4j Aura Agents via the v2beta1 REST API — create, list, get, update, delete, and invoke Aura agents backed by an AuraDB instance. Use when configuring Aura Agent tools (CypherTemplate, SimilaritySearch, Text2Cypher), setting system prompts, deploying agents to REST or MCP endpoints, invoking agents with natural language queries, or generating evaluation datasets (max 50 questions) for the Aura Agent evaluation feature. Covers OAuth2 auth, organization/project scoping, tool parameter schemas, and InvokeAgentResponse format. Does NOT cover AuraDB instance provisioning — use neo4j-aura-provisioning-skill. Does NOT cover vector index creation — use neo4j-vector-index-skill.

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Use when planning a delivery request as the only orchestration agent - one board-writing subtask, backlog first, one approval question, then move approved tasks to todo

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Use when a stakeholder sends a request - route it to backlog tasks, an analiz task, or a focused question, decide ask-vs-assume with a Definition of Ready, and ask only what a wrong guess would actually cost

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Use when asked to prove, disprove, investigate, or find evidence for a claim about people, concepts, ideas, or their relationships. Also use when asked to explore connections, find who influenced what, trace how ideas developed, or answer questions that require reasoning over multiple linked notes.

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Guides and best practices for working with Neon Serverless Postgres. Covers getting started, local development with Neon, choosing a connection method, Neon features, authentication (@neondatabase/auth), PostgREST-style data API (@neondatabase/neon-js), Neon CLI, and Neon's Platform API/SDKs. Use for any Neon-related questions.

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Manage the ChromaDB vector database that stores the ecosystem's persistent memory. Use when user asks to check memory storage, backup memory, search stored entries, delete entries, or reset the vector database. Do NOT use for general question answering about past sessions (use the memory skill for that).

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Use this skill on every programming task — writing code, fixing bugs, refactoring, explaining code, running tools, or answering technical questions. Apply token-saving and quality-preserving practices throughout. Activate even when the user doesn't explicitly ask for efficiency — this skill is always relevant for software development work. Do not skip this skill for "simple" tasks; the rules apply at every scale.

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Use only after explicit Codex `$superloopy:superloopy-research` or Claude Code `/superloopy:superloopy-research` invocation, a research task started with a leading `loopy` or `루피` (such as `loopy research`), or an active Superloopy loop explicitly routing a research deliverable here. Evidence-backed Superloopy research orchestration with automatic advisory usage targets, parallel read-only lanes, per-retrieval verdicts, graded and dated sources, empirical verification, a priced claim ledger, cited synthesis, and optional reports. Do not activate from research, investigate, look-up, summarize, deep-dive, report, or similar vocabulary alone, in any language; ordinary questions, debugging, and implementation context-gathering stay with their primary workflows.

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。

Edit English prose to remove repetitive AI phrasing, inflated language, and mechanical structure while preserving facts, meaning, and the author's voice. Use for requests to de-slop, humanize, rewrite naturally, fix tone, or review generic English drafts. Also compose from the author's English notes or transcripts and ask targeted questions when real details are missing. For Brazilian Portuguese, use the bundled humanizar 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。

Write and edit Brazilian Portuguese prose with natural rhythm and the author's voice while preserving facts, meaning, and intent. Use for PT-BR requests to humanizar, tirar cara de IA, remover AI slop, reescrever com voz, simplify language, or write a post, email, article, or bio. Also use the author's notes or transcripts and ask targeted questions when essential facts are missing. For English text, use the bundled human-ai 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。

Edit English prose to remove repetitive AI phrasing, inflated language, and mechanical structure while preserving meaning and the author's voice. Use for requests to humanize, de-slop, rewrite naturally, or fix tone. Also compose from the author's English notes or transcripts and ask targeted questions when real details are missing. For Brazilian Portuguese, use the companion humanizar 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。

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

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Skills for accessing and searching docs in DeepWiki/GitHub’s public code repositories can help users understand open-source project source codes, and users can also ask questions directly about the code docs.

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Aurora Smart Home orchestrator — routing layer for all smart home skills. Use this skill when the user asks ANY smart home question and you need to decide which skill to invoke, or when a task spans multiple skills (e.g., "build a sensor that shows on a dashboard and triggers automations"). Invoke aurora FIRST before reaching for a specific skill — it will route to the right specialist(s) and recommend the correct Claude model to keep token usage efficient. Trigger on: smart home, Home Assistant, ESPHome, automation, IoT, dashboard, ESP32, Node-RED, or any request about controlling or monitoring devices at home.

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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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用户需要评估一个资产/投资/项目的价值，或纠结某个东西"值不值"时。适用于任何能产生（或不产生）现金流的资产评估。 语言信号："这个值多少钱"/"这只股票贵不贵"/"这个项目值不值得投"/"应该用PE还是PB估值" 不适用于：不需要估值分析的纯情感/审美决策。

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。