analytics
Use when answering a question that needs data from a ktx-connected database - investigating, analyzing, "how many", "show me", "what's the breakdown of",…
Capture semantic-layer and knowledge updates from a live database schema snapshot.
$ npx -y skills add Kaelio/ktx --skill live_database_ingest --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/live_database_ingestContext 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.
name: live_database_ingest description: Capture semantic-layer and knowledge updates from a live database schema snapshot. callers: [memory_agent]
Use this skill when the ingest work unit contains raw files under `raw-sources/<connectionId>/live-database/<syncId>/`.
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.
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:
If it errors, the identifier is fictional.
citing the exact raw path that mentioned it.
the failing probe error in `clarification`. 5. Never copy `<schema>.<table>` placeholder strings from these instructions into output.
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: numberUse `string`, `number`, `time`, or `boolean` for column types. When a database type is ambiguous, use `string`.
The raw snapshot is structural evidence. Do not invent measures, segments, business definitions, or joins that are not present in the snapshot files.
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
Repo: Kaelio/ktx
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