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.
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.
npx skills add Kaelio/ktx --skill lookml_ingestgit clone --depth 1 https://github.com/Kaelio/ktxWrote 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.
[](https://agentmods.dev/skills/kaelio/ktx/lookml_ingest)<a href="https://agentmods.dev/skills/kaelio/ktx/lookml_ingest"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/lookml_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.
<a href="https://agentmods.dev/skills/kaelio/ktx/lookml_ingest"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/lookml_ingest.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.02749 |
| Opus 5 | $0.00032 | $0.01375 |
| Sonnet 5 | $0.00013 | $0.00550 |
| Haiku 4.5 | $0.00006 | $0.00275 |
Grade A, and why
lookml_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.
How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LookML to ktx Semantic Layer
LookML views map to SL sources, measure: to measures, explore: { join: } to the join graph. This skill lays out the mapping and the three capture shapes.
Mapping table
| LookML | ktx form | Notes |
|---|---|---|
view: X { sql_table_name: …; measure:/dimension:/join: } |
Overlay named X with measures, computed-only columns, column_overrides, joins, segments |
Manifest-backed; inherit grain/columns |
view: X { derived_table: { sql: … } } |
Standalone with top-level sql:, explicit grain: + columns: |
No manifest entry exists |
view: X { sql_always_where: <p> } |
Standalone with sql: SELECT * FROM <base> WHERE <p> |
Enforcement, not opt-in |
explore: { join: Y { sql_on: …; relationship: … } } |
joins: entry { to: Y, on: "<local> = Y.<col>", relationship: … } |
On the overlay or standalone |
conditionally_filter / always_filter |
segments: [{ name, expr }] |
Callers reference by name |
| Manifest entry | _schema/*.yaml |
Never edit - auto-imported |
Type map: date/datetime/timestamp → time; yesno → boolean; number → number; string → string. Ignore drill_fields: (UI only).
Decision rules
LookML writes target the run connection directly. Unlike Looker runtime ingestion, the LookML adapter is configured on the warehouse ktx connection, so do not look for targetWarehouseConnectionId and do not route through a mapping array.
Before any SL write, inspect the WorkUnit notes.
If notes contain:
[LOOKML SL WRITES DISALLOWED]
reason: lookml_connection_mismatch
...
[/LOOKML SL WRITES DISALLOWED]
this is a hard gate. The model's declared Looker connection: does not match the warehouse connection's configured expectedLookerConnectionName. Continue wiki extraction and context candidates. Do not call sl_write_source or sl_edit_source for that WorkUnit. The runner also removes those write tools for this WorkUnit; treat the missing tools as expected. Preserve the mismatch reason in any emit_unmapped_fallback you create.
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.
- 10d ago First seen · 220 lines · 63 tokens per session scan A 6e63c8945046
lookml_ingest is a skill published in the GitHub repository Kaelio/ktx (1,581 stars, last pushed 6d ago), licensed Apache-2.0. It adds 63 tokens to every session and 2,749 once invoked, about $0.0003 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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