Live database ingest

Skill Kaelio/ktx/packages/cli/src/skills/live_database_ingest

ktx is an executable context layer for data and analytics agents 🐙 Allow Claude Code, Codex, or other AI agents to query analytical databases accurately and with full context of your company

Install

npx -y skills add Kaelio/ktx --skill live_database_ingest

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What its author says it does

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Capture semantic-layer and knowledge updates from a live database schema snapshot.

SKILL.md

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Live Database Ingest

Use this skill when the ingest work unit contains raw files under raw-sources/<connectionId>/live-database/<syncId>/.

Workflow

Read the table JSON file listed in the work unit.
Read connection.json to understand the snapshot metadata.
Read foreign-keys.json when the table has a foreign key or when joins are needed for the semantic-layer source.
Create or update one semantic-layer source for the table with sl_write_source.
Use the physical table name from the raw JSON as the source table field.
Preserve database comments as descriptions.db on tables and columns.
Add joins only when the foreign key index names both sides.
Write wiki pages only for durable business meaning that is present in table or column comments.
Run sl_validate for the table source before the work unit completes.

Sample values come from the scan record; do not invent values not present in relationship-profile.json.

Identifier Verification Protocol

Before writing a wiki page or SL source on any topic:

discover_data({query: "<topic>"}) - see what wikis, SL sources, and raw tables already exist. Prefer updating existing pages over creating new ones.

Before emitting any schema.table or schema.table.column into a wiki body, SL source, tables: frontmatter, sl_refs, or emit_unmapped_fallback:

entity_details({connectionId, targets: [{display: "<identifier>"}]}) - confirm the identifier resolves; inspect native types, FK/PK, and sampleValues.
For literal values from the source, such as status codes or plan tiers, check whether they appear in entity_details sampleValues for the relevant column. If sampleValues is short or the sample may have missed real values, run a sql_execution probe with the same warehouse connection id: sql_execution({connectionId, sql: "SELECT DISTINCT <col> FROM <ref> LIMIT 50"}).
If the candidate identifier still does not resolve, do one of:
- Use sql_execution({connectionId, sql: "SELECT 1 FROM <ref> LIMIT 0"}). If it errors, the identifier is fictional.
- Wrap the identifier in [unverified - from <rawPath>] in the wiki body, citing the exact raw path that mentioned it.
- When recording emit_unmapped_fallback with no_physical_table, include the failing probe error in clarification.
Never copy <schema>.<table> placeholder strings from these instructions into output.

Source shape

For a raw table with this shape:

{
  "name": "orders",
  "db": "public",
  "columns": [
    { "name": "id", "type": "integer", "nullable": false, "primaryKey": true }
  ]
}

Write a semantic-layer source with this shape:

name: orders
table: public.orders
grain: id
columns:
  - name: id
    type: number

Use string, number, time, or boolean for column types. When a database type is ambiguous, use string.

Boundaries

The raw snapshot is structural evidence. Do not invent measures, segments, business definitions, or joins that are not present in the snapshot files.