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Found 1,035 Skills
MSW `.map` / `.ui` / `.gamelogic` / `.model` 에셋과 `world.yaml` 을 버전 고정된 `@choigawoon/msw-vfs-cli`(npx) 로 읽고·탐색·편집·변환하는 스킬. '맵 구조 확인', '맵 엔티티 목록', 'UI 계층', 'HP바/텍스트 조사', 'entity 값 수정', '컴포넌트 추가/삭제', '.model 값 편집', 'YAML export/import', 'world 빌드', '.map/.ui/.gamelogic/.model 파일 분석' 요청 시 사용. L1(경로 기반 VFS) + L2(entity 단위) + .model + YAML/World 모두 지원.
A single-frame motion-design composition with looping CSS animations — rotating type ring, animated globe, ticking timer, parallax labels. Renders as a hero video poster you can hand straight to HyperFrames or any keyframe-based exporter. Use when the brief asks for "motion design", "animated hero", "loop", "video poster", "title card", or pairs Open Claude Design with HyperFrames for a kinetic export.
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.
How to create, structure, and publish self-contained Convex components with proper isolation, exports, and dependency management
Guide AI agents through Godot 4.x GDScript coding best practices including scene organization, signals, resources, state machines, and performance optimization. This skill should be used when generating GDScript code, creating Godot scenes, designing game architecture, implementing state machines, object pooling, save/load systems, or when the user asks about Godot patterns, node structure, or GDScript standards. Keywords: godot, gdscript, game development, signals, resources, scenes, nodes, state machine, object pooling, save system, autoload, export, type hints.
App Store screenshot research, competitor analysis, and planning tool for iOS/macOS apps. Use this skill when working with App Store screenshots for any of these tasks: (1) Finding and analyzing competitor screenshots in your category, (2) Downloading competitor screenshots locally for reference, (3) Analyzing screenshot strategies (styles, captions, features), (4) Planning your screenshot sequence and messaging, (5) Generating a local preview website to view and compare screenshots, (6) Understanding screenshot requirements and best practices, (7) Creating exportable screenshot assets at correct dimensions.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Develops resources for FiveM using the Qbox Project (qbx_core). Covers the exports-based API, bridge compatibility, Ox integration (ox_lib, ox_inventory), and best practices. Use when the user works with FiveM, Qbox, qbx_core, or mentions `exports.qbx_core`, `QBX.PlayerData`, or `ox_lib`.
Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Use when creating new synthetic voices, fine-tuning existing Piper checkpoints, preparing audio datasets for TTS training, or deploying voice models to devices like Raspberry Pi or Home Assistant. Covers dataset preparation, Whisper-based validation, training configuration, and ONNX export.
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
Screen and analyze stocks through an ESG (Environmental, Social, Governance) lens, evaluating sustainability practices, controversy exposure, and responsible investing criteria. Use when the user asks about ESG investing, sustainable investing, socially responsible investing (SRI), impact investing, green stocks, carbon footprint analysis, governance quality assessment, controversy screening, exclusion lists, or ESG scoring of companies or portfolios.
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when you notice interesting anomalies or correlations during any data analysis task.