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looker_ingest

looker_ingest is a skill for Claude Code, Codex from Kaelio/ktx. It costs 44 tokens per session (3,083 once invoked), scanned A, original, Apache-2.0.

A process for turning staged Looker dashboard, report, and data-exploration JSON into durable knowledge for a shared semantic layer. Looker is a business-intelligence tool used to define and query company data.

In plain words
What is it for?
Use it to extract metric formulas, segment definitions, field meanings, and domain conventions, then prepare evidence-backed knowledge candidates and query results.
Why use it?
It separates raw dashboard evidence from reusable definitions, helping prevent duplicated or unsupported metric knowledge.

Skill for Claude CodeCodex

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

not rated 1.6krepo +8 6d ago A scan SkillSpector: pass 44 tokens original Apache-2.0

Good fit Use it to extract metric formulas, segment definitions, field meanings, and domain conventions, then prepare evidence-backed knowledge candidates and query results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kaelio/ktx/looker_ingest
View source ↗ Kaelio/ktx docs.kaelio.com
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 · 1,581 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 looker_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 looker_ingest

README.md
[![agentmods](https://agentmods.dev/badge/skills/kaelio/ktx/looker_ingest/github.svg)](https://agentmods.dev/skills/kaelio/ktx/looker_ingest)
Your own site
<a href="https://agentmods.dev/skills/kaelio/ktx/looker_ingest"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/looker_ingest/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for looker_ingest

Your own site · 80×15
<a href="https://agentmods.dev/skills/kaelio/ktx/looker_ingest"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/looker_ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,083 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 $0.00044 $0.03083
Opus 5 $0.00022 $0.01541
Sonnet 5 $0.00009 $0.00617
Haiku 4.5 $0.00004 $0.00308

Measured 10d ago against content hash 2f151e67e72b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

looker_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 10d 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/looker_ingest/SKILL.md · 249 lines

How it starts

The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Looker Runtime Ingest

Looker runtime ingest turns API-staged dashboards, Looks, and explores into durable ktx memory. Runtime entities are evidence. They are not themselves the final knowledge shape.

Required Workflow

  1. Read every rawFiles entry for the WorkUnit.
  2. Read relevant dependencyPaths before making a decision. For dashboard and Look WUs this usually includes the referenced explore JSON, signal files, folders/tree.json, and users/<id>.json.
  3. Treat signals/*.json, owners, folders, schedules, and favorites as prioritization or provenance context only.
  4. Extract generalizable metric formulas, segment definitions, field semantics, and domain conventions.
  5. Use wiki_search, sl_discover, and sl_read_source before writing so new content merges with existing memory instead of duplicating it.
  6. Use context_evidence_search or context_evidence_read to obtain evidence chunk IDs for any wiki-bound knowledge candidate.
  7. Use context_candidate_write for durable wiki-bound knowledge. Do not call wiki_write from a Looker WorkUnit; Stage 4 reconciliation promotes candidates and writes wiki pages.
  8. Use looker_query_to_sl for each Look query or dashboard tile query that has a query object.
  9. Write SL from Looker runtime evidence only through the staged warehouse target contract. For explores and inherited dashboard/Look queries, branch on targetTable.ok; when it is true, write on targetWarehouseConnectionId and use targetTable.canonicalTable as source.table. When it is false or missing, write wiki knowledge candidates and record emit_unmapped_fallback with the staged reason.
  10. Run sl_validate after every SL write. If validation fails, fix the source or roll it back before the WorkUnit ends.

For every Looker field reference, call entity_details on the underlying schema.table.column before promoting it to sl_refs or quoting it in wiki body.

Identifier Verification Protocol

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

Read the full file on GitHub · 249 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. 10d ago First seen · 249 lines · 44 tokens per session scan A 2f151e67e72b

Subscribe to this mod's changes

looker_ingest is a skill published in the GitHub repository Kaelio/ktx (1,581 stars, last pushed 6d ago), licensed Apache-2.0. It adds 44 tokens to every session and 3,083 once invoked, about $0.0002 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.

Analytics and metricsagentsanalytics-engineeringcontext-layerdata-engineeringsemantic-layer
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