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Data & AI

Machine learning, LLMs, and data processing.

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data-engineering

113

analytics-engineering

Use this skill when building dbt models, designing semantic layers, defining metrics, creating self-serve analytics, or structuring a data warehouse for analyst consumption. Triggers on dbt project setup, model layering (staging, intermediate, marts), ref() and source() usage, YAML schema definitions, metrics definitions, semantic layer configuration, dimensional modeling, slowly changing dimensions, data testing, and any task requiring analytics engineering best practices.

NeverSight

data-ai

data-engineering

113

real-time-streaming

Use this skill when building real-time data pipelines, stream processing jobs, or change data capture systems. Triggers on tasks involving Apache Kafka (producers, consumers, topics, partitions, consumer groups, Connect, Streams), Apache Flink (DataStream API, windowing, checkpointing, stateful processing), event sourcing implementations, CDC with Debezium, stream processing patterns (windowing, watermarks, exactly-once semantics), and any pipeline that processes unbounded data in motion rather than data at rest.

NeverSight

data-ai

data-engineering

113

data-pipelines

Use this skill when building data pipelines, ETL/ELT workflows, or data transformation layers. Triggers on Airflow DAG design, dbt model creation, Spark job optimization, streaming vs batch architecture decisions, data ingestion, data quality checks, pipeline orchestration, incremental loads, CDC (change data capture), schema evolution, and data warehouse modeling. Acts as a senior data engineer advisor for building reliable, scalable data infrastructure.

NeverSight

data-ai

data-engineering

113

data-warehousing

Use this skill when designing data warehouses, building star or snowflake schemas, implementing slowly changing dimensions (SCDs), writing analytical SQL for Snowflake or BigQuery, creating fact and dimension tables, or planning ETL/ELT pipelines for analytics. Triggers on dimensional modeling, surrogate keys, conformed dimensions, warehouse architecture, data vault, partitioning strategies, materialized views, and any task requiring OLAP schema design or warehouse query optimization.

NeverSight

data-ai

data-engineering

113

implement

Full feature pipeline — pre-flight checks, TDD cycle, scope guard, quality commit. Combines pre-flight + tdd + scope-check + quality-commit into one flow. Use when implementing a feature, adding an endpoint, or building any non-trivial code change.

NeverSight

data-ai

data-engineering

113

data-quality

Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.

NeverSight

data-ai

data-engineering

113

deploy

Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.

databricks

data-ai

data-engineering

113

deploy

Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.

databricks

data-ai

data-engineering

113

deploy

Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.

databricks

data-ai

data-engineering

113

deploy

Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.

databricks

data-ai

data-engineering

113

deploy

Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.

databricks

data-ai

data-analysis

112

data-analyst

Analyses datasets with professional rigour — statistical summaries, clear narratives, and well-chosen visualisations.

siddsachar

data-ai

data-analysis

111

visualizing-with-mermaid

Creates professional Mermaid diagrams with semantic styling and visual hierarchy. Use when creating flowcharts, sequence diagrams, state machines, class diagrams, or architecture visualizations.

rileyhilliard

data-ai

llm-ai

111

doc-sync-tool

自动同步项目中的 Agents.md、claude.md 和 gemini.md 文件，保持内容一致性。支持自动监听和手动触发。

littleben

data-ai

llm-ai

109

seo-content-writer

Creates high-quality, SEO-optimized content that ranks in search engines. Applies on-page SEO best practices, keyword optimization, and content structure for maximum visibility and engagement.

aaron-he-zhu

data-ai

data-engineering

109

building-with-kafka-strimzi

Use when building event-driven systems with Apache Kafka on Kubernetes. Triggers include EDA patterns, Kafka producers/consumers, Strimzi operator deployment, Schema Registry, transactions, exactly-once semantics. NOT for general messaging (use Dapr pub/sub for abstraction).

panaversity

data-ai

llm-ai

109

skill-creator-pro

Creates production-grade, reusable skills that extend Claude's capabilities. This skill should be used when users want to create a new skill, improve an existing skill, or build domain-specific intelligence. Gathers context from codebase, conversation, and authentic sources before creating adaptable skills.

panaversity

data-ai

llm-ai

109

evaluation

Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.

panaversity

data-ai

llm-ai

109

building-with-claude-agent-sdk

Use when building AI agents with Anthropic's Claude Agent SDK (formerly Claude Code SDK). Triggers include creating autonomous agents, building agentic applications, SDK-based automation, implementing hooks/subagents/MCP servers, session management, or agent tool usage. NOT when using Claude API directly (use anthropic-sdk) or building MCP servers from scratch (use mcp-builder).

panaversity

data-ai

llm-ai

109

building-rag-systems

Build production RAG systems with LangChain orchestration and Qdrant vector store. Covers 8 RAG architectures (Simple, HyDE, CRAG, Self-RAG, Agentic), document processing, semantic chunking, retrieval chains, and evaluation with LangSmith/RAGAS. Use when implementing RAG pipelines, semantic search, or AI knowledge systems. NOT for simple keyword search.

panaversity

data-ai

llm-ai

109

context-optimization

Apply optimization techniques to extend effective context capacity. Use when context limits constrain agent performance, when optimizing for cost or latency, or when implementing long-running agent systems.

panaversity

data-ai

llm-ai

109

building-with-openai-agents

Use when building AI agents with OpenAI's Agents SDK. Triggers include creating agents, implementing tools, multi-agent handoffs, guardrails, MCP integration, tracing. Also for using LiteLLM to run agents on free/alternative models (Anthropic, Gemini). NOT for general OpenAI API usage (use openai-python SDK docs instead).

panaversity

data-ai

machine-learning

109

agent-evals

Design and implement evaluation frameworks for AI agents. Use when testing agent reasoning quality, building graders, doing error analysis, or establishing regression protection. Framework-agnostic concepts that apply to any SDK.

panaversity

data-ai

llm-ai

109

context-degradation

Recognize, diagnose, and mitigate patterns of context degradation in agent systems. Use when context grows large, agent performance degrades unexpectedly, or debugging agent failures.

panaversity

data-ai

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