本文へ移動
cccskills
無料GitHub で公開

live_database_ingest

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

インストール方法を見る

含まれるファイル(1)

  • SKILL.md3.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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:

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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.

日本語の概要は準備中です。原文の説明を表示しています。

Kaelio/ktx1,6162026年9月11日 更新

Synthesize durable KTX wiki pages from staged Google Drive document pulls. Load when a WorkUnit contains Google Doc raw files from `docs/**`.

日本語の概要は準備中です。原文の説明を表示しています。

Kaelio/ktx1,6162026年9月11日 更新

Identify recurring cross-table historic-SQL analytical intents from a bounded pattern shard and emit typed pattern evidence for deterministic wiki projection.

日本語の概要は準備中です。原文の説明を表示しています。

Kaelio/ktx1,6162026年9月11日 更新

Convert one changed historic-SQL table usage bucket into typed table usage evidence for deterministic _schema projection.

日本語の概要は準備中です。原文の説明を表示しています。

Kaelio/ktx1,6162026年9月11日 更新

Classify and resolve conflicts detected during bundle ingest (structural duplicates, definitional contradictions, near-duplicate clusters, re-ingest changes, evictions).

日本語の概要は準備中です。原文の説明を表示しています。

Kaelio/ktx1,6162026年9月11日 更新

ktx

無料

Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install agent rules, ingest schema, or troubleshoot a local ktx install.

日本語の概要は準備中です。原文の説明を表示しています。

Kaelio/ktx1,6162026年9月11日 更新

Kaelio のスキルをすべて見る

このスキルの問題を報告する