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 sigma_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/sigma_ingest)<a href="https://agentmods.dev/skills/kaelio/ktx/sigma_ingest"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/sigma_ingest.svg" alt="Measured on agentmods" 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 | $0.00036 | $0.02632 |
| Opus 5 | $0.00018 | $0.01316 |
| Sonnet 5 | $0.00007 | $0.00526 |
| Haiku 4.5 | $0.00004 | $0.00263 |
Grade A, and why
sigma_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 8d 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sigma Ingest
Sigma ingest turns staged data model specs and workbook summaries into durable ktx wiki knowledge. The deterministic project() step has already written semantic-layer YAML for all warehouse-table data model elements before this skill runs — do not re-write those SL sources.
Work unit structure
Sigma produces at minimum two work units per ingest run:
sigma-data-modelsorsigma-data-models-NrawFiles:data-models/<id>.jsonfiles (one per data model in this batch)peerFileIndex:workbooks/<id>.jsonfiles +sigma-manifest.json+sigma-projection-config.json- When the workspace has more than 50 data models, split into batches:
sigma-data-models-0,sigma-data-models-1, … withdisplayLabellike"Sigma: data models (1/8)". When ≤50 data models, the unitKey is simplysigma-data-modelswith no suffix.
sigma-workbooksorsigma-workbooks-NrawFiles:workbooks/<id>.jsonfiles (one per workbook in this batch)peerFileIndex:data-models/<id>.jsonfiles +sigma-manifest.json+sigma-projection-config.json- When the workspace has more than 2000 workbooks, split into batches:
sigma-workbooks-0,sigma-workbooks-1, … withdisplayLabellike"Sigma: workbooks (1/4)". When ≤2000 workbooks, the unitKey is simplysigma-workbookswith no suffix.
sigma-manifest.json and sigma-projection-config.json are never in rawFiles. They live at the staged dir root and always appear in peerFileIndex.
Staged file shapes
data-models/<id>.json — one per data model (in rawFiles for data-model units):
{
"sigmaId": "abc-123",
"name": "Revenue Model",
"path": "Finance/Revenue Model",
"latestVersion": 3,
"updatedAt": "2026-01-15T00:00:00Z",
"isArchived": false,
"spec": {
"name": "Revenue Model",
"pages": [{
"id": "p1",
"name": "Main",
"elements": [{
"id": "elem1",
"kind": "table",
"name": "Opportunities",
"hidden": false,
"source": {
"kind": "warehouse-table",
"connectionId": "<sigma-internal-uuid>",
"path": ["DATABASE", "SCHEMA", "OPPORTUNITIES"]
},
"columns": [
{ "id": "c1", "name": "Deal Amount", "formula": "[OPPORTUNITIES/Amount]", "description": "Net contract value in USD" },
{ "id": "c2", "name": "Total ARR", "formula": "Sum([OPPORTUNITIES/ARR])", "description": "Annualised recurring revenue" }
]
}]
}]
}
}
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.
- 8d ago First seen · 190 lines · 36 tokens per session scan A b3fbe53ff9fa
sigma_ingest is a skill published in the GitHub repository Kaelio/ktx (1,579 stars, last pushed 5d ago), licensed Apache-2.0. It adds 36 tokens to every session and 2,632 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.
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