explorer-tidx
Explorer-only TIDX migration skill (PG-first, CH fallback for count-heavy paths)
Explorer-only TIDX migration skill (PG-first, CH fallback for count-heavy paths)
ๅๅปบๆๆๆ่ฝ็ๆๅใๅฝ็จๆทๆณ่ฆๅๅปบๆฐๆ่ฝ๏ผๆๆดๆฐ็ฐๆๆ่ฝ๏ผไปฅ้่ฟไธไธ็ฅ่ฏใๅทฅไฝๆตๆๅทฅๅ ท้ๆๆฉๅฑ Claude ็ๅ่ฝๆถ๏ผๅบไฝฟ็จๆญคๆ่ฝใ
Activates the Pickle Rick persona. Use this ONLY when the user explicitly requests to start the "Pickle Rick" mode or loop. DO NOT use this for general greetings (e.g., "hi") normal assistance.
ๅๅปบ้ซ่ดจ้ MCP๏ผๆจกๅไธไธๆๅ่ฎฎ๏ผๆๅกๅจ็ๆๅ๏ผไฝฟ LLM ่ฝๅค้่ฟ็ฒพๅฟ่ฎพ่ฎก็ๅทฅๅ ทไธๅค้จๆๅกไบคไบใๅจๆๅปบ MCP ๆๅกๅจไปฅ้ๆๅค้จ API ๆๆๅกๆถไฝฟ็จ๏ผๆ ่ฎบๆฏ Python (FastMCP) ่ฟๆฏ Node/TypeScript (MCP SDK)ใ
This skill should be used when the user asks to "ไบบๆบ้ฃๆด", "Human-Machine Brainstorm", "human storm", "ccb brainstorm", "้ๆฑๅฏน้ฝ่ฐๅบฆ", "spec convergence", or wants a CCB-based multi-model requirement alignment loop with Codex as the dispatcher.
Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.
Machine-readable workflow DAG for the multi-step agent pipeline. Defines node types, edge conditions, gates, and fan-out patterns. USE FOR: Orchestrator step routing, resume-from-graph, workflow validation. DO NOT USE FOR: Azure infrastructure, code generation, troubleshooting.
Guides Phase 2 Lambda Container Migration steps. Pass a specific step number (2-1 through 2-5) to get the goals, deliverables, validation criteria, and detailed design for that step.
Core data analytics concepts, Excel/Google Sheets fundamentals, and data collection techniques
Synthesize findings from multiple research sources into coherent insights. Use when combining data from different sub-agents or research threads.
Data visualization and chart components for Composable Svelte. Use when creating charts, graphs, or data visualizations. Covers chart types (scatter, line, bar, area, histogram), data binding, state-driven updates, interactive features (zoom, brush, tooltips), and responsive design from @composable-svelte/charts package built with Observable Plot and D3.
Identify groups and patterns in data using k-means, hierarchical clustering, and DBSCAN for cluster discovery, customer segmentation, and unsupervised learning
SQL for data analysis with exploratory analysis, advanced aggregations, statistical functions, outlier detection, and business insights. 50+ real-world analytics queries.
Analyzes data with rich metadata recommendations
Interactive data exploration and visualization skill. Use when users ask to visualize data, analyze datasets, create charts, or explore data files (CSV, Excel, Parquet, JSON). This skill guides through data exploration, proposes visualization strategies based on data characteristics, creates interactive Plotly charts in marimo notebooks, and generates analytical conclusions.
Analyzes long call butterfly spreads with 3 strikes and 4 legs for neutral outlook. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting minimal price movement, want low-cost defined-risk strategy, analyzing pinning opportunities, or evaluating tight-range neutral positions on stocks near technical levels.
Create entity-relationship diagrams with proper normalization, keys, and cardinality for logical data models.
Analyzes long strangle volatility plays with OTM call and put at different strikes. Requires numpy>=1.24.0, pandas>=2.0.0, matplotlib>=3.7.0, scipy>=1.10.0. Use when expecting very large price movement, want lower-cost alternative to straddle, analyzing high-volatility events, or evaluating wide-range breakout opportunities on stocks with elevated IV.