Agent skills
Skills you can use with AI coding agents, indexed from public GitHub repositories.
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metadata-manager
Use this skill when creating or updating DAG configurations (dags.yaml), schema.yaml, and metadata.yaml files for BigQuery tables. Handles creating new DAGs when needed and coordinates test updates when queries are modified (invokes sql-test-generator as needed). Works with bigquery-etl-core, query-writer, and sql-test-generator skills.
mozilla/bigquery-etl-skills 6
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bigconfig-generator
Use this skill when creating or updating Bigeye monitoring configurations (bigconfig.yml files) for BigQuery tables. Works with metadata-manager skill.
mozilla/bigquery-etl-skills 6
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bigquery-etl-core
The core skill for working within the bigquery-etl repository. Use this skill when understanding project structure, conventions, and common patterns. Works with model-requirements, query-writer, metadata-manager, sql-test-generator, and bigconfig-generator skills.
mozilla/bigquery-etl-skills 6
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query-writer
Use this skill when writing or updating SQL queries (query.sql) or Python ETL scripts (query.py) following Mozilla BigQuery ETL conventions. ALWAYS checks for and updates existing tests when modifying queries. Coordinates downstream updates to schemas and tests. Works with bigquery-etl-core, metadata-manager, and sql-test-generator skills.
mozilla/bigquery-etl-skills 6
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schema-readme-generator
Use this skill to create or update README.md files for BigQuery ETL tables in the mozilla bigquery-etl repository. Follows layout conventions derived from comparing README files across the repo — rich style with emoji headings, Mermaid data flow diagram, graduated example queries, and concise metadata overview table. Requires schema.yaml with complete descriptions (run schema-enricher first if needed) and a complete metadata.yaml.
mozilla/bigquery-etl-skills 6
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sql-test-generator
ALWAYS use this skill when users ask to create, generate, or write UNIT TESTS for BigQuery SQL queries. Invoke proactively whenever the request includes "test" or "tests" with a query/table name. This skill is for unit testing ONLY (not data quality checks - use bigconfig-generator for Bigeye monitoring). Works with bigquery-etl-core skill to understand query patterns.
mozilla/bigquery-etl-skills 6
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glean-description-lookup
Use this skill when looking up field descriptions for Mozilla Glean telemetry tables (tables ending in _live or _stable, e.g. <app>_stable.<ping>_v1). Fetches descriptions from the Glean Dictionary (dictionary.telemetry.mozilla.org) using WebFetch with targeted field extraction — only the fields referenced in query.sql, never the full table schema.
mozilla/bigquery-etl-skills 6
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column-description-finder
Use this skill when looking up, auditing, or managing column descriptions from global, application-specific, and dataset-specific column definition YAML files (bigquery_etl/schema/global.yaml, bigquery_etl/schema/app_<name>.yaml, and bigquery_etl/schema/<dataset>.yaml). Use it to find a description for a specific column, list all columns in a base schema, audit which columns in a table's schema.yaml are covered by base schemas, or identify columns missing descriptions. Works with schema-enricher skill.
mozilla/bigquery-etl-skills 6
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schema-enricher
Use this skill to enrich schema.yaml files for BigQuery tables in the bigquery-etl repository. Handles creating schema.yaml when it doesn't exist, finding and filling missing column descriptions (from base schemas, upstream source schema, query context, or application context), validating columns against the query, and generating a summary with recommendations for where to add new descriptions (global.yaml, <dataset_name>.yaml, or app_<name>.yaml). Works with column-description-finder skill.
mozilla/bigquery-etl-skills 6
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skill-creator
ALWAYS use this skill when users ask to "create a skill", "make a skill for...", "add a new skill", or similar requests. This skill guides the creation of effective skills in the bigquery-etl repository that extend Claude's capabilities with specialized knowledge for BigQuery ETL workflows, Mozilla data platform conventions, or telemetry analysis. CRITICAL - First checks for conflicts with existing skills and recommends using/updating existing skills when appropriate. Do NOT attempt to create skills without invoking this skill first.
mozilla/bigquery-etl-skills 6
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data-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
wshobson/agents 32,911
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spark-optimization
Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.
wshobson/agents 32,911
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airflow-dag-patterns
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
wshobson/agents 32,911
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employment-contract-templates
Create employment contracts, offer letters, and HR policy documents following legal best practices. Use when drafting employment agreements, creating HR policies, or standardizing employment documentation.
wshobson/agents 32,911
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gdpr-data-handling
Implement GDPR-compliant data handling with consent management, data subject rights, and privacy by design. Use when building systems that process EU personal data, implementing privacy controls, or conducting GDPR compliance reviews.
wshobson/agents 32,911
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prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
wshobson/agents 32,911
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similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
wshobson/agents 32,911
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hybrid-search-implementation
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
wshobson/agents 32,911
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vector-index-tuning
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
wshobson/agents 32,911
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langchain-architecture
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
wshobson/agents 32,911
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rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
wshobson/agents 32,911
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embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
wshobson/agents 32,911
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llm-evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
wshobson/agents 32,911
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ml-pipeline-workflow
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
wshobson/agents 32,911