Best-practice kit for BI dashboard layout, storytelling, and adoption.

Trading dashboard P&L visualization with profit tracker integration, win-rate overlays, R-multiples, and configurable settings

Auto-discover dashboard symbols from loaded RL models. Trigger when: (1) dashboard shows old/wrong symbols, (2) symbols mismatch between live trader and dashboard, (3) adding new models to system, (4) dashboard shows NO_MODEL for all symbols.

Aggregate and merge data from multiple sources including App Store sales, GitHub commits, Skillz events, and more. Use when combining data for reports, dashboards, or analysis.

Master machine learning, data engineering, AI engineering, MLOps, and prompt engineering. Build intelligent systems from data pipelines to production AI applications with LLMs, agents, and modern frameworks.

Master machine learning, data engineering, AI engineering, LLMs, prompt engineering, and MLOps. Build intelligent systems with Python.

Use this skill when the user needs to analyze, clean, or prepare datasets. Helps with listing columns, detecting data types (text, categorical, ordinal, numeric), identifying data quality issues, and cleaning values that don't fit expected patterns. Invoke when users mention data cleaning, data quality, column analysis, type detection, or preparing datasets.

Clean and standardize vehicle insurance data following established business rules.

Data cleaning, preprocessing, and quality assurance techniques

Explore and analyze pilot data sets to uncover patterns, anomalies, and initial insights. Use when performing ad-hoc data investigations, validating data quality, or preparing exploratory visualizations for hypothesis generation.

SWR-based data fetching and caching patterns used throughout the monorepo. Use this skill when implementing API interactions, creating custom data hooks, handling loading/error states, or working with mock data. Covers SWR configuration, custom hook patterns (useUserInfo, useTimesSquarePage), error handling, and mock data setup.

Rooms as pipeline nodes, exits as edges, objects as messages

Data governance strategy, quality validation rules, and data dictionary management for vehicle insurance platform. Use when defining data quality standards, implementing validation rules, managing field mappings, resolving data conflicts, or establishing data governance processes. Covers data cleaning standards, quality metrics, and mapping management.

Procedures and playbooks for responding to data quality incidents, data loss, corruption, and pipeline failures.

Service-scoped data orchestration for TMNL. Invoke when implementing search, data streams, kernel systems, or Effect-based DAQ. Covers hybrid dispatch (fibers + workers), Atom-as-State pattern, and progressive streaming.

Metabase REST API automation and troubleshooting: authenticate (API key preferred, session fallback), export/upsert questions (cards) and dashboards, standardize visualization_settings, and run/export results.

Build orchestration pipelines with idempotency.

Monitor and troubleshoot dual-pipeline data collection systems on GCP. This skill should be used when checking pipeline health, viewing logs, diagnosing failures, or monitoring long-running operations for data collection workflows. Supports Cloud Run Jobs (batch pipelines) and VM systemd services (real-time streams).

Process JSON with jq and YAML/TOML with yq. Filter, transform, query structured data efficiently. Triggers on: parse JSON, extract from YAML, query config, Docker Compose, K8s manifests, GitHub Actions workflows, package.json, filter data.

Set up database replication for high availability and disaster recovery. Use when configuring master-slave replication, multi-master setups, or replication monitoring.

Documentation of available data science libraries (scipy, numpy, pandas, sklearn) and best practices for statistical analysis, regression modeling, and organizing analysis scripts. **CRITICAL:** All analysis scripts MUST be placed in reports/{topic}/scripts/, NOT in root scripts/ directory.

Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.

Connect your own data source to replace the demo unicorns data. Use when the user wants to use their own database URL or CSV file instead of the sample data. Triggers on requests to connect database, import CSV, change data source, use own data, or switch from demo data.

This skill should be used when reading any tabular data file (Excel, CSV, Parquet, ODS). It automatically detects and fixes common data issues including multi-level headers, encoding problems, empty rows/columns, and data type mismatches. Returns a clean DataFrame ready for analysis with zero user intervention.

Implementing comprehensive validation rules across database, application, and pipeline layers to ensure data integrity.

Creating effective data visualizations using charts, graphs, and visual representations to communicate insights clearly and accurately following Tufte and Few principles.

Provides expert design guidance for creating truthful, clear, beautiful data visualizations. Focuses on **DESIGN DECISIONS ONLY**—chart selection, color strategy, visual encoding, and validation. Assumes data is accurate and prepared. Auto-activates when user mentions: data viz, dashboard, chart type, visualization, infographic

Build mathematically correct, visually prominent data visualizations for time-series charts. Use this skill when creating charts with mathematical overlays (trendlines, patterns, indicators), fixing visual artifacts (wavy lines, domain mismatches), or validating chart correctness. Focuses on technical correctness and progressive validation, not aesthetic design.

Create visualizations that Seaborn users, Tufte readers, and everyone else will love. Marry NYT Graphics rigor with MoMA aesthetics, Nike energy, and On Kawara precision.

Create publication-quality plots and visualizations using matplotlib and seaborn. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).

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Role assignment for Claude Agent #1 - Database schema architect for Lead Hunter Prime. Build ONLY database schema (11 tables, RLS policies, seed data). Do NOT build APIs, dashboards, or N8N workflows.

Upload estimation results to Supabase storage and register with Estimator API. Final phase of the estimation workflow.

Expert in creating database diagrams and visual representations. Use when generating ERDs, schema diagrams, or visualizing database relationships with Mermaid.js.

Master database design (SQL, NoSQL), system architecture, API design (REST, GraphQL), and building scalable systems. Learn PostgreSQL, MongoDB, system design patterns, and enterprise architectures.

Modern deployment with Databricks Asset Bundles (DAB), supporting multi-environment configurations and CI/CD integration.

Expert-level Databricks platform, Apache Spark, Delta Lake, MLflow, notebooks, and cluster management

Query logs, metrics, monitors, and dashboards from Datadog. Search logs, check alert status, and investigate incidents.

Compare two datasets to find differences, added/removed rows, changed values. Use for data validation, ETL verification, or tracking changes.

Use when editing Planning Hubs, timelines, calendars, or any file with day-name + date combinations (Wed Nov 12), relative dates (tomorrow), or countdowns (18 days until) - validates day-of-week accuracy, relative date calculations, and countdown math with two-source ground truth verification before allowing edits

datum-system

Transform AI agents into experts on dbt project architecture and medallion layer patterns, providing

Transform AI agents into experts on dbt materializations, providing guidance on choosing the right

Transform AI agents into experts on writing production-quality dbt models, providing guidance on CTE

Web search via the DDGS metasearch library. Use for searching for unknown documentation, facts, or any web content. Lightweight, no browser required.

CRM integration for tracking deals through pipeline stages with automated status updates

Create or update a Decision Interface Charter for recurring decisions

決策樹助手工具。快速評估任務複雜度，提供派發建議。用於: (1) 任務複雜度快速評估, (2) 派發代理人建議, (3) 拆分策略建議, (4) 並行可行性評估

Expert guidance for DeepAgents framework - simplified agent creation with tool integration for LangChain/LangGraph workflows.