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Data
Skill

/live_database_ingest

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

From plugin
ktx
1.6k17 skills
Install
$ npx -y skills add Kaelio/ktx --skill live_database_ingest --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/live_database_ingest

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

live_database_ingest.SKILL.md
name: live_database_ingest
description: Capture semantic-layer and knowledge updates from a live database schema snapshot.
callers: [memory_agent]

Live Database Ingest

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

Workflow

1. Read the table JSON file listed in the work unit. 2. Read `connection.json` to understand the snapshot metadata. 3. Read `foreign-keys.json` when the table has a foreign key or when joins are needed for the semantic-layer source. 4. Create or update one semantic-layer source for the table with `sl_write_source`. 5. Use the physical table name from the raw JSON as the source `table` field. 6. Preserve database comments as `descriptions.db` on tables and columns. 7. Add joins only when the foreign key index names both sides. 8. Write wiki pages only for durable business meaning that is present in table or column comments. 9. 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:

1. `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`:

2. `entity_details({connectionId, targets: [{display: "<identifier>"}]})` - confirm the identifier resolves; inspect native types, FK/PK, and sampleValues. 3. 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"})`. 4. 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`. 5. 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.

Read more
Ships withktx

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

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TypeScript
Language
Apache-2.0
License
6d ago
Last commit
4mo ago
Created

Repo: Kaelio/ktx

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