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 slgit 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/sl)<a href="https://agentmods.dev/skills/kaelio/ktx/sl"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/sl/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/sl"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/sl.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 | $0.00087 | $0.02540 |
| Opus 5.5 | $0.00035 | $0.01016 |
| Sonnet 5.5 | $0.00017 | $0.00508 |
| Haiku 4.5 | $0.00009 | $0.00254 |
Grade A, and why
sl 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 1mo 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 — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Semantic Layer
ktx's semantic layer (SL) is a structured catalog. Each source represents a table, a SQL view, or an overlay that enriches a manifest-backed table with measures, computed columns, joins, and named segments. The catalog is the single source of truth for reusable business metrics.
This skill covers two parts:
- Part 1 - Schema reference (what an SL source looks like).
- Part 2 - Querying via
sl_query.
Capture (when and how to add new patterns to the SL) is a separate concern handled by the memory-agent - see the sl_capture skill if you are running in capture mode. The research agent reads and queries the SL via the tools described here; it does not write to it.
For capture-time identifier verification, load sl_capture. Synthesis writer
skills must verify warehouse identifiers with discover_data,
entity_details, and sql_execution before emitting table or column names.
Part 1 - Schema reference
An SL source is a YAML file under semantic-layer/<connectionId>/. The file's name: field is the source's identity — it mirrors the warehouse identifier verbatim (e.g. Snowflake's uppercase SIGNED_UP); the filename is only a derived label. Always address sources by name through the sl_* tools, never by file path. There are three flavors:
Overlay sources
Enrich a manifest-backed table with measures, computed columns, joins, and segments. No table or sql field. The base table's columns and grain are inherited from the manifest.
name: fct_orders # must match an existing manifest table
descriptions:
user: "Overlay adding business measures to the orders fact table."
measures:
- name: total_revenue
expr: sum(amount)
description: Total order revenue - filter by status or region at query time
columns: # computed dimensions only
- name: is_large_order
type: boolean
expr: "amount > 1000"
column_overrides: # metadata patches for inherited columns
- name: status
descriptions:
user: "Order lifecycle status."
segments:
- name: paid_non_refunded
expr: "is_paid = true AND is_refunded = false"
joins:
- to: customers
on: "customer_id = customers.id"
relationship: many_to_one
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.
- 1mo ago First seen · 248 lines · 87 tokens per session scan A ab35024574c5
sl is a skill published in the GitHub repository Kaelio/ktx (1,618 stars, last pushed 3d ago), licensed Apache-2.0. It adds 87 tokens to every session and 2,540 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
warehouse-init
Initialize warehouse schema discovery. Generates .astro/warehouse.md with all table metadata for instant lookups. Run once per project, refresh when schema changes. Use when user says "/astronomer-data:warehouse-init" or asks to set up data discovery.
analyzing-data
Queries the data warehouse with SQL and answers business questions about data. Use when answering anything that needs warehouse data - counts, metrics, trends, aggregations, joins across tables, data lookups, or ad-hoc SQL analysis (for example "who uses X", "how many Y", "show me Z", "find customers", "what is the…
tracing-downstream-lineage
Trace downstream data lineage and impact analysis. Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.
tracing-upstream-lineage
Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.
profiling-tables
Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.
bi-sql-knowledge
Use Seshat's public SQL knowledge for source grain, joins, aggregation, validation, reconciliation, and transformation reasoning without executing a database.