metabase_ingest

metabase_ingest is a skill for Claude Code, Codex from Kaelio/ktx. It costs 90 tokens per session (4,779 once invoked), scanned A, original, Apache-2.0.

A conversion guide for turning Metabase questions, models, and metrics into ktx semantic-layer sources, which give shared meaning to data fields and measures.

In plain words
What is it for?
It supports importing Metabase cards, mapping result fields, detecting keys and duplicate sources, breaking down pre-aggregations, and maintaining earlier import history.
Why use it?
It helps preserve metadata such as column types, relationships, collections, filters, and links between saved Metabase cards during conversion.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It supports importing Metabase cards, mapping result fields, detecting keys and duplicate sources, breaking down pre-aggregations, and maintaining earlier import history.

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Install with agentmods
npx agentmods add skills/kaelio/ktx/metabase_ingest
About the project

Kaelio/ktx is a context layer that helps AI agents query analytical databases using company knowledge, approved metrics, table metadata, and relationships between columns. Data teams use it to make warehouse queries more accurate and consistent with their organization's definitions. Its catalogue add-ons teach agents how to use ktx and its data-querying interfaces.

Kaelio/ktx · 1,579 stars · on GitHub · docs.kaelio.com

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Kaelio/ktx --skill metabase_ingest
Clone the repo
git clone --depth 1 https://github.com/Kaelio/ktx

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for metabase_ingest

README.md
[![agentmods](https://agentmods.dev/badge/skills/kaelio/ktx/metabase_ingest.svg)](https://agentmods.dev/skills/kaelio/ktx/metabase_ingest)
Your own site
<a href="https://agentmods.dev/skills/kaelio/ktx/metabase_ingest"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/metabase_ingest.svg" alt="Measured on agentmods" height="20"></a>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,779 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.04779
Opus 5 $0.00045 $0.02389
Sonnet 5 $0.00018 $0.00956
Haiku 4.5 $0.00009 $0.00478

Measured 9d ago against content hash a4cb5082d743, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

metabase_ingest scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

packages/cli/src/skills/metabase_ingest/SKILL.md · 254 lines

How it starts

The opening of the file, as written. The whole thing — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Metabase to ktx Semantic Layer

Each WorkUnit represents one Metabase collection's cards for one Metabase database (mapped to exactly one ktx connection). Every cards/<id>.json file carries the resolved SQL, result_metadata, card type, collection path, and referenced-card ids. The WU's sync-config.json tells you which sync mode is active and which selections apply. databases/<id>.json tells you the target ktx connection.

Context format

Each card JSON looks like:

{
  "metabaseId": 7,
  "name": "Daily orders",
  "description": "Orders by day",
  "type": "model",
  "databaseId": 42,
  "collectionId": 5,
  "resolvedSql": "SELECT ...",
  "templateTags": [{"name": "ref", "type": "card", "cardReference": 10}],
  "resultMetadata": [
    {"name": "day", "base_type": "type/DateTime", "semantic_type": "type/CreationTimestamp"},
    {"name": "order_count", "base_type": "type/Integer"}
  ],
  "collectionPath": ["Data", "Orders Team"],
  "referencedCardIds": [10]
}

Use resultMetadata to:

  • Map base_type to KSL column type: type/Integer, type/Float, type/Decimal, type/BigIntegernumber; type/Text, type/TextLikestring; type/DateTime, type/Date, type/DateTimeWithTZtime; type/Booleanboolean.
  • Identify grain candidates: columns with semantic_type: type/PK.
  • Identify join candidates: columns with semantic_type: type/FK plus fk_target_field_id.
  • Identify time columns: semantic_type: type/CreationTimestamp or type/UpdatedTimestamp → set role: time.
  • Use display_name for measure descriptions when available.

Additional card metadata

  • parameters: list of card-level parameters with widget types and defaults. When SQL resolution fell back to unresolved SQL, use this to drive Step A of the SQL-translation workflow (drop optional clauses): knowing each {{ var }} is type: "date/range" vs type: "category" tells you what kind of clause it is.
  • resultMetadata[i].field_ref: Metabase's canonical reference to the source warehouse field. Shape ["field", <field_id>, <options>]. When this is set, the column maps directly to a warehouse field, which is useful for declaring joins from FK metadata without re-parsing SQL.
  • lastRunAt: ISO timestamp of the card's last execution. If null or very old, the card may be dead; prefer skipping over creating a source.
  • dashboardCount: number of dashboards referencing the card. Cards with dashboardCount: 0 and a stale lastRunAt are strong skip signals.

Read the full file on GitHub · 254 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 254 lines · 90 tokens per session scan A a4cb5082d743

Subscribe to this mod's changes

metabase_ingest is a skill published in the GitHub repository Kaelio/ktx (1,579 stars, last pushed 5d ago), licensed Apache-2.0. It adds 90 tokens to every session and 4,779 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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