sigma_ingest

sigma_ingest is a skill for Claude Code, Codex from Kaelio/ktx. It costs 36 tokens per session (2,632 once invoked), scanned A, original, Apache-2.0.

A data-ingestion tool that turns staged Sigma data-model specifications and workbook summaries into lasting knowledge for a KTX wiki. Sigma is a data and analytics platform; workbooks are its saved reports or dashboards.

In plain words
What is it for?
Use it when importing Sigma data models or workbook summaries into KTX wiki knowledge, including large batches split into multiple work units.
Why use it?
It preserves useful information from Sigma exports in a searchable knowledge system instead of leaving it only in temporary staged files.

Skill for Claude CodeCodex

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

Good fit Use it when importing Sigma data models or workbook summaries into KTX wiki knowledge, including large batches split into multiple work units.

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Install with agentmods
npx agentmods add skills/kaelio/ktx/sigma_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 sigma_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 sigma_ingest

README.md
[![agentmods](https://agentmods.dev/badge/skills/kaelio/ktx/sigma_ingest.svg)](https://agentmods.dev/skills/kaelio/ktx/sigma_ingest)
Your own site
<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>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,632 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 $0.00036 $0.02632
Opus 5 $0.00018 $0.01316
Sonnet 5 $0.00007 $0.00526
Haiku 4.5 $0.00004 $0.00263

Measured 8d ago against content hash b3fbe53ff9fa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

packages/cli/src/skills/sigma_ingest/SKILL.md · 190 lines

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-models or sigma-data-models-N
    • rawFiles: data-models/<id>.json files (one per data model in this batch)
    • peerFileIndex: workbooks/<id>.json files + 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, … with displayLabel like "Sigma: data models (1/8)". When ≤50 data models, the unitKey is simply sigma-data-models with no suffix.
  • sigma-workbooks or sigma-workbooks-N
    • rawFiles: workbooks/<id>.json files (one per workbook in this batch)
    • peerFileIndex: data-models/<id>.json files + sigma-manifest.json + sigma-projection-config.json
    • When the workspace has more than 2000 workbooks, split into batches: sigma-workbooks-0, sigma-workbooks-1, … with displayLabel like "Sigma: workbooks (1/4)". When ≤2000 workbooks, the unitKey is simply sigma-workbooks with 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" }
        ]
      }]
    }]
  }
}

Read the full file on GitHub · 190 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. 8d ago First seen · 190 lines · 36 tokens per session scan A b3fbe53ff9fa

Subscribe to this mod's changes

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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