Name: lakehouse
Author: asabocinski

Prompt file imported from asabocinski/bravo-edd (.codex/prompts/lakehouse.md). Copyright stays with the author.

Lakehouse Command

Get guidance on Iceberg, Delta Lake, catalogs, and open table formats

Usage

/lakehouse <description-or-question>

Examples

/lakehouse "Set up Iceberg tables with partition evolution"
/lakehouse "Migrate from Hive to Iceberg on AWS"
/lakehouse "Compare Delta Lake vs Iceberg for our use case"
/lakehouse "Configure Unity Catalog for multi-cloud"

What This Command Does

Invokes the lakehouse-architect agent
Analyzes your table format or catalog requirements
Loads KB patterns from lakehouse and cloud-platforms domains
Generates:
- Table DDL with partition strategies
- Catalog configuration (Unity, Polaris, Nessie)
- Migration scripts from legacy formats
- Compaction and maintenance procedures

Agent Delegation

Agent	Role
`lakehouse-architect`	Primary — Iceberg, Delta, catalog governance
`data-platform-engineer`	Escalation — when infra provisioning is needed
`spark-engineer`	Escalation — when Spark read/write optimization is needed

KB Domains Used

lakehouse — Iceberg v3, Delta Lake, catalog wars, DuckLake
cloud-platforms — platform-specific Iceberg/Delta patterns
spark — Spark table format integration

Output

The agent generates table definitions, catalog configs, and operational runbooks for your lakehouse setup.

Install lakehouse and it becomes one more candidate the router can pick - when it fits.

A listing is a routing target with a manifest: what it does, which domains it covers, what it costs and who publishes it. Once installed it sits beside your own models and tools, is scored like any other candidate for each request, and shows up in the trace when it wins. Ratings come from workspaces that installed it, one per account.

{ "prompt": "# Lakehouse Command\n\n> Get guidance on Iceberg, Delta Lake, catalogs, and open table formats\n\n## Usage\n\n```bash\n/lakehouse <description-or-question>\n```\n\n## Examples\n\n```bash\n/lakehouse \"Set up Iceberg tables with partition evolution\"\n/lakehouse \"Migrate from Hive to Iceberg on AWS\"\n/lakehouse \"Compare Delta Lake vs Iceberg for our use case\"\n/lakehouse \"Configure Unity Catalog for multi-cloud\"\n```\n\n---\n\n## What This Command Does\n\n1. Invokes the **lakehouse-architect** agent\n2. Analyzes your table format or catalog requirements\n3. Loads KB patterns from `lakehouse` and `cloud-platforms` domains\n4. Generates:\n - Table DDL with partition strategies\n - Catalog configuration (Unity, Polaris, Nessie)\n - Migration scripts from legacy formats\n - Compaction and maintenance procedures\n\n## Agent Delegation\n\n| Agent | Role |\n|-------|------|\n| `lakehouse-architect` | Primary — Iceberg, Delta, catalog governance |\n| `data-platform-engineer` | Escalation — when infra provisioning is needed |\n| `spark-engineer` | Escalation — when Spark read/write optimization is needed |\n\n## KB Domains Used\n\n- `lakehouse` — Iceberg v3, Delta Lake, catalog wars, DuckLake\n- `cloud-platforms` — platform-specific Iceberg/Delta patterns\n- `spark` — Spark table format integration\n\n## Output\n\nThe agent generates table definitions, catalog configs, and operational runbooks for your lakehouse setup.", "variables": [], "notes": "Table format and catalog guidance — delegates to lakehouse-architect agent", "metadata": { "source": { "provider": "github-codex-prompts", "repository": "https://github.com/asabocinski/bravo-edd", "path": ".codex/prompts/lakehouse.md", "ref": "ae4c539580470e782ac67d2dc4a4eabe9fc68336", "url": "https://github.com/asabocinski/bravo-edd/blob/ae4c539580470e782ac67d2dc4a4eabe9fc68336/.codex/prompts/lakehouse.md", "key": "asabocinski/bravo-edd/.codex/prompts/lakehouse.md" } } }

lakehouse

About

Lakehouse Command

Usage

Examples

What This Command Does

Agent Delegation

KB Domains Used

Output

Install lakehouse and it becomes one more candidate the router can pick - when it fits.

Use it

Manifest

Reviews