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概要と使いどころ
Use when a Flutter screen needs app state or data - separate presentation from state, keep business logic out of widgets, and follow the app's existing state solution (Riverpod 3 Notifier/AsyncNotifier, or the official ChangeNotifier+ListenableBuilder MVVM pattern)
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Use when building or extending a Java service with Quarkus - layered architecture, JAX-RS resources, CDI beans, Panache persistence, and native-friendly patterns
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Prepare for challenging conversations using Prepare-Listen-Empathize-Clarify-Solve framework.
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Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.
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Neo4j Java Driver v6 — driver lifecycle, Maven/Gradle setup, executableQuery, executeRead/Write managed transactions, explicit transactions, async/reactive patterns, error handling, data type mapping, connection pool tuning, causal consistency/bookmarks. Use when writing Java or Kotlin code that connects to Neo4j via GraphDatabase.driver, executableQuery, SessionConfig, executeRead, executeWrite, or TransactionCallback. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT cover driver version upgrades — use neo4j-migration-skill. Does NOT cover Spring Data Neo4j (@Node, Neo4jRepository) — use neo4j-spring-data-skill.
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Run Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN, Node2Vec, FastRP, GraphSAGE) directly inside Snowflake without moving data. Use when running graph algorithms against Snowflake tables via the Neo4j Snowflake Native App ("GDS Snowflake", "graph algorithms in Snowflake", "Neo4j Graph Analytics"). Covers the explore → prepare projection views → project-compute-write flow, the strict view/column type rules the graph engine requires, exact SQL CALL syntax, and privilege setup in both modes — app-identity grants and execute-as-user / per-user PAT auth (programmatic access token, set_enable_custom_credentials, register_user_role, caller grants). Does NOT cover Cypher or Neo4j DBMS queries — use neo4j-cypher-skill. Does NOT cover Aura Graph Analytics — use neo4j-aura-graph-analytics-skill. Does NOT cover self-managed GDS — use neo4j-gds-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。
Inspect, stream, and explicitly adjust live Android UI tweak values or invoke app-owned actions through Snap-O. Use when a request mentions Snap-O Tweaks, tweak-enabled Android apps, runtime UI parameters, Compose tweak or action controls, tweak discovery, previously adjusted or currently inactive tweaks, applying all user-made tweak changes, typed values, defaults or resets, tweak batches, explicitly registered actions, the Tweaks REST/SSE API, or selecting between CLI, macOS tool, in-app overlay, and custom tweak interfaces.
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Phase C of a WinForms-to-Avalonia migration: implement platform and infrastructure adapters for dependencies discovered in Phase A/B, such as persistence, process execution, dialogs, notifications, or environment capabilities. Use only in an explicit migration when such dependencies leak into Forms/core or when the orchestrator routes here. Do not invent adapters for capabilities the product does not have, and do not implement main-window XAML.
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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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Persistent memory across AI sessions using ChromaDB vector database. Stores and retrieves context from past conversations, decisions, and code. Use when user asks to remember something, search past conversations, recall what was done before, save context for later, or find information from previous sessions. Do NOT use for git history or file-based notes.
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Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers design systems, anti-pattern detection, brand vs product registers, typography, color (OKLCH), spacing, motion, copy, and accessibility. By Paul Bakaus (ex-Google, ex-Disney, ex-Unity). Full version includes 23 sub-commands, CLI detection, Chrome extension, and E2E test suite: npx skills add pbakaus/impeccable
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Use only after explicit Codex `$superloopy:superloopy-backend` or Claude Code `/superloopy:superloopy-backend` invocation, a backend task beginning with leading `loopy` or `루피`, or an active Superloopy loop routes backend work here. Relevant work includes backend application boundaries, APIs, persistence, migrations, transactions, background jobs, caches, events, reliability, security, and runtime database-agent tools. Plain API, server, database, SQL, or agent vocabulary does not activate this workflow.
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Korean prose humanizer for Codex and Superloopy that rewrites Korean text so it sounds naturally human while preserving meaning, register, facts, protected tokens, and evidence. Use when the user asks to remove Korean AI tells, make Korean copy sound human, fix 번역투, remove ChatGPT/Claude/Gemini tone, polish Korean without changing meaning, or says "AI 티 없애줘", "AI 윤문", "번역투 고쳐", "사람이 쓴 것처럼", "humanize Korean", "한글 AI 티 제거", "글 자연스럽게 다듬어줘". Inspired by Korean AI-tell ideas from https://github.com/epoko77-ai/im-not-ai. Handles Korean rewriting only; not for translation, fact expansion, SEO rewriting, legal drafting, or generic proofreading.
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Generate UTM-tagged URLs for campaign tracking. Create consistent, organized tracking links for Google Analytics, HubSpot, and other analytics platforms. Supports bulk generation, naming conventions, and campaign documentation. Use when creating tracking links, campaign URLs, or organizing marketing attribution.
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Designs Amazon-compliant main product images and the 7 secondary image types (Infographic / Multi-angle / Detail Shot / Lifestyle / Variants / What's in Box / Size Reference) — with platform-mandatory main-image rules, conversion-rate-tuned secondary types per product category, and multi-image consistency anchored to the main image URL. Use when the user says "Amazon main image", "亚马逊主图", "product main image", "white background product image", "Amazon listing image", "亚马逊副图", "secondary images", "product carousel images", or any request for the 1:1 product image suite that lives on the Amazon detail page carousel. For A+ Content / detail-page modules (21:9 Hero Banner and 3:2 module layouts), see the `product-shots-detail-page` skill.
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Designs social-platform-native visuals (Feed, Story, Reel, Carousel formats) for Instagram, TikTok, Facebook, Pinterest, RedNote, LinkedIn, X/Twitter and other social platforms — with platform-correct dimensions, safe zones, industry visual DNA, engagement hooks, and cross-slide consistency. Use when the user says "social post", "Instagram post", "IG post", "TikTok post", "Facebook post", "Pinterest pin", "social carousel", "feed post", "story post", "make a post", "design a social visual", "社媒帖", "做一个社媒图", or any underspecified social-media visual request. Routed from `product-shots` for the organic-social branch (non-ad).
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Use when auditing self-media videos, scripts, covers, subtitles, voiceover, product links, account copy, comments, articles, or publishing packages for platform violation risk; especially before final delivery or publishing after video production or clipping. Covers e-commerce product consistency, Qianchuan material quality, regulated qualifications, efficacy claims, prices, gifts, activities, and pre-selection risk across WeChat, Douyin, Kuaishou, Bilibili, Xiaohongshu, TikTok, and related platforms.
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Lark/Feishu real-time event listening / subscribing / consuming: stream events as NDJSON via `lark-cli event consume <EventKey>` (covers IM message receive, reactions, chat member changes, etc.). Use for Lark bots, real-time message processing, long-running subscribers, streaming webhook/push handlers. Supports `--max-events` / `--timeout` bounded runs and a stderr ready-marker contract — designed for AI agents running as subprocesses.
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Register front, side, back, and top orthographic reference views into a shared Blender coordinate contract. Use when a 3D model must match multi-view wireframes/templates, when side/back/top silhouettes are off, or before adding depth to a front-locked contour model.
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Design and validate texture-state transitions for Blender animations without ugly whole-image crossfades, texture popping, misregistered morphs, or target-engine-incompatible Python-only swaps. Use when animating between multiple source textures, mascot/logo states, style states, lightmaps, decals, or UI skins.
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Guides native iOS with Swift, SwiftUI, UIKit interop, concurrency, and persistence. Use when building or reviewing iPhone/iPad apps after establishing runtime truth.
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Designs runtime topology in a chosen solution. Use when choosing monolith vs services, cells, consistency, ADRs. Not for cross-system design (software-solution-architecture).
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Use when an agent must stand up the OpenClaw↔Cognee integration with FalkorDB as the vector + graph store. This skill is SELF-CONTAINED — it tells you to generate every file (Dockerfile, sitecustomize.py, docker-compose.yaml) from the exact contents below, build a custom Cognee image that loads the FalkorDB adapter, run Cognee + FalkorDB together, self-verify the adapter registered, and point the OpenClaw cognee plugin at it (including per-agent graphs). No other files in this directory are required.
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Track, categorize, and prioritize technical debt across the codebase. Scans for debt indicators, maintains a debt register, and recommends repayment scheduling.
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。