| name | einblick-analysis |
| description | Activate when the user asks to analyze Snowflake, Databricks, or MotherDuck queries, optimize data models, find expensive queries, recommend materialized views, analyze query patterns, reduce query costs, or mentions einblick.
|
| version | 0.1.0 |
Einblick Analysis
Analyze query history patterns from Snowflake, Databricks, or MotherDuck and generate data modeling recommendations.
Pipeline
${CLAUDE_PLUGIN_ROOT}/.venv/bin/einblick extract --platform <snowflake|databricks|motherduck> --days <N> --format json --output "/tmp/einblick-results-$(id -u).json"
Then read /tmp/einblick-results-$(id -u).json (resolve $(id -u) to a numeric value via Bash first, then pass the literal path to Read). The uid suffix avoids cross-user collisions on shared boxes.
Context Gathering
Before analyzing results, use AskUserQuestion to understand the user's environment:
- Which platform? (Snowflake, Databricks, or MotherDuck)
- Where does source data come from? (ETL, dbt, streaming, manual)
- What data freshness is required? (real-time, hourly, daily, weekly)
- Is there an existing transformation layer? (dbt, Tasks/Workflows, Airflow, none)
Analysis Framework
Evaluate each query pattern cluster against these criteria. Apply platform-specific recommendations:
Both Platforms
- Materialization candidates: High execution count aggregations
- Denormalization candidates: Repeated multi-table joins
- Cost hotspots: High total credits/DBUs
- Access patterns: Who queries what, governance concerns
- Biggest offenders: Users burning the most cost, slowest patterns, heaviest scanners
Snowflake-Specific
- Clustering keys: ALTER TABLE CLUSTER BY on filtered columns (tables >1GB)
- Dynamic Tables: Automatic lag-based refresh for SQL transformations
- Streams + Tasks: CDC for incremental processing
- Transient tables: Skip Fail-Safe on staging tables
- Search Optimization Service: Point lookups on large tables
Databricks-Specific
- OPTIMIZE + ZORDER: Compact small files and co-locate data by filter columns
- Liquid clustering: Automatic incremental clustering (replaces ZORDER)
- Delta Live Tables: Declarative pipelines with dependency tracking
- Materialized views: Auto-refreshing pre-computed results
- Photon: Queries that would benefit from Photon-accelerated compute
- Caching: Delta caching and result caching patterns
Structured Proposed dbt Changes
Every report ends with a ## Proposed dbt Changes section. It's not free-form prose -- the LLM calls a typed tool named emit_dbt_proposals which einblick validates and renders. Three recommendation shapes:
new_model -- brand-new dbt model with proposed SQL, tests, source tables, layer, and materializationmodify_existing -- a config change to an existing dbt model (e.g. materialized: view -> incremental, add clustering key, add unique_key)access_pattern -- users querying raw tables that a curated mart already covers (surface only; einblick never auto-applies governance changes)
Each proposal carries metrics_addressed (einblick pattern fingerprints it absorbs) and einblick_schema_version for forward compatibility.
Claude Code users with dbt-mcp installed can act on these proposals directly: create the model files, validate with dbt parse / compile / run / test, and open a PR -- all without leaving the conversation. See docs/dbt-integration.md in the einblick repo for the full flow. This lands phase-by-phase.
Ships in this release: --dbt-aware (or the "Use dbt project context?" question in the interactive flow) queries the dbt Discovery API to cross-reference each pattern's source tables against actual models in the user's dbt project. The cross-reference is injected into the LLM prompt so recommendations can differentiate new_model / modify_existing / access_pattern from real data, not guesses. Requires DBT_HOST / DBT_TOKEN / DBT_PROD_ENV_ID. Misconfigured auth or network errors fall back to the non-dbt-aware path gracefully -- the run never hard-fails.
Reference Files
references/system-prompt.md - Data modeling expertise for both platformsreferences/analysis-prompt.md - Template for structured analysis (instructs the LLM to call emit_dbt_proposals)
