Creates images and video via Alibaba Wan 2.6 (DashScope), Google Gemini/Veo, and OpenAI GPT Image APIs, plus background extraction workflows. Triggers: image generation, video generation, dashscope, wan 2.6, alibaba, gemini, veo, gpt image, openai images, background removal, alpha extraction, transparent png.
Extract extreme response values with linked statistics from OrcaFlex simulations. Use for design load identification, max/min extraction with associated values, and extreme event characterization.
Publication-quality tables in Python with rich styling, formatting, conditional formatting, and export to HTML/images - inspired by R's gt package
Generates concise summaries of log content using type-specific summarization templates
获取A股数据(baostock)并缓存到本地CSV文件，避免MCP返回大量数据占用上下文。触发场景：(1)获取超过100条的K线数据 (2)需要多次查询同一股票数据 (3)需要用grep/awk分析数据 (4)用户提到"保存数据"或"缓存数据"
Fix markdown table alignment and spacing issues. Use when formatting tables in markdown files, aligning columns, normalizing cell padding, or ensuring proper table structure. Triggers: markdown table, table alignment, table formatting, md table, align columns.
Generates data cleaning pipelines for pandas/polars with handling for missing values, duplicates, outliers, type conversions, and data validation. Use when user asks to "clean data", "generate data pipeline", "handle missing values", or "remove duplicates from dataset".
Orchestrates complete single-document workflows with automatic validation and indexing in a write→validate→index pipeline
Coordinate FABER-DB operations within FABER workflow phases
Guide for working with IndexedDB database operations in the DEVS platform. Use this when asked to add database tables, modify schemas, or work with data persistence.
Orchestrates multi-log workflows with parallel execution for batch operations across many logs
データ変換パイプラインの設計・実装・検証を整理するスキル。 スキーママッピング、ETL設計、品質確認までの実務フローを提供する。 Anchors: • Designing Data-Intensive Applications / 適用: データモデリング / 目的: 変換の整合性確保 • Designing Data-Intensive Applications / 適用: スキーマ設計 / 目的: マッピングの明確化 • Designing Data-Intensive Applications / 適用: パイプライン設計 / 目的: 伸縮性と監視性の確保 Trigger: Use when designing data transformation pipelines, defining schema mappings, implementing ETL processes, or optimizing data flows. data transformation, schema mapping, etl design, pipeline optimization, data modeling
データベースの初期データ投入を安全に設計・実装・検証するスキル。 開発/テスト/本番のデータ分離、シード戦略、再現性の確保を整理する。 Anchors: • Designing Data-Intensive Applications / 適用: データ整合性 / 目的: 参照整合性の担保 • Database Reliability Engineering / 適用: 運用設計 / 目的: 本番投入の安全性 • Data Quality Principles / 適用: データ品質 / 目的: 再現性と検証性の確保 Trigger: Use when planning database seeding, generating test fixtures, separating datasets by environment, or validating seed data quality. database seeding, test data, fixtures, seed strategy, environment separation, data validation
Discipline epistemique stricte pour eliminer les derives cognitives de Claude. Activation SYSTEMATIQUE sur TOUTES les conversations. Impose: (1) Zero invention/supposition, (2) Honnetete absolue, (3) Vision holistique avec relecture du contexte, (4) Questions plutot que suppositions, (5) Remise en contexte perpetuelle avec checkpoints structures, (6) Documentation incrementielle sans doublons, (7) Vigilance workflow/CI-CD/securite, (8) TODO/plan obligatoire avant tout travail, (9) Documentation FR/EN scholar style signee Julien GELEE, (10) Zero credentials/API keys/donnees sensibles en clair. Posture ingenieur generaliste specialise IA.
@effect/ai integration patterns for categorical AI composition, typed error handling, and production prompt pipelines. Use when building AI applications with Effect-TS, composing LLM calls with typed errors, creating tool-augmented AI systems, implementing structured output generation, or integrating multiple AI providers (OpenAI, Anthropic) with categorical composition patterns.
OpenAI Agents SDK (Python) development. Use when building AI agents, multi-agent workflows, tool integrations, or streaming applications with the openai-agents package.
Use semantic memory from code-recall MCP to remember decisions, patterns, and learnings across sessions. Call get_briefing at session start, check_rules before major changes, and store_observation after important decisions.
Moderate YouTube live chat by detecting spam, toxic content, and enforcing rate limits. PROTOTYPE for testing pattern fidelity before deploying to native Qwen/Gemma.
