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 dbt_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/dbt_ingest)<a href="https://agentmods.dev/skills/kaelio/ktx/dbt_ingest"><img src="https://agentmods.dev/badge/skills/kaelio/ktx/dbt_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 | $0.00103 | $0.01588 |
| Opus 5 | $0.00051 | $0.00794 |
| Sonnet 5 | $0.00021 | $0.00318 |
| Haiku 4.5 | $0.00010 | $0.00159 |
Grade A, and why
dbt_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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbt → ktx (bundle ingest)
Use this skill for uploaded dbt projects (dbt_project.yml at stage root, models/**, sources/**, schema.yml). There is no fetch() in v1 - scheduled dbt parse / manifest.json pulls are out of scope; host-provided dbt sync may still backfill structured test metadata into _schema on the next sync.
Mapping (models / sources → SL)
| dbt | ktx | Notes |
|---|---|---|
models: entry with columns: |
Overlay on the manifest table with the same name (after discover_data / entity_details) |
One SL source per physical table; model name may differ from DB name - resolve with read_raw_file + warehouse context. |
sources: → tables: |
Same as models; use identifier when present instead of logical name. |
Schema + name must match how the connection sees tables. |
Column description |
column_overrides[].descriptions.user on the overlay |
Do not overwrite dbt description keys from sync. |
data_tests: not_null / unique |
Short hint in column descriptions or notes: “dbt: not null”, “dbt: unique” |
Full structured metadata lands in manifest via sync; the skill keeps bundle-time SL text useful for the agent. |
accepted_values |
Add a brief line in the column description: allowed values (truncate long lists) | Also mention enum-like use in discover_data / filters. |
relationships |
Add or confirm joins: on the overlay only when to resolves to a real table via read_raw_file + discover_data / entity_details |
If the ref cannot be resolved, capture the intent in a wiki page instead. |
Physical schema grounding
dbt YAML is documentation and test metadata; it is not permission to invent physical columns. Before writing any table-backed SL source, confirm the real warehouse shape with discover_data, sl_discover, or entity_details and use only confirmed column names in column_overrides:, computed-only columns:, grain:, joins:, segments:, and measures[].expr.
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 · 77 lines · 103 tokens per session scan A 6f4d62084b72
dbt_ingest is a skill published in the GitHub repository Kaelio/ktx (1,579 stars, last pushed 5d ago), licensed Apache-2.0. It adds 103 tokens to every session and 1,588 once invoked, about $0.0005 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.
Other skills, from other repositories
hunk-launch-video
Produces Hunk videos by driving the real TUI headlessly in a PTY, compositing captioned 1080p frames in Chromium, and encoding with ffmpeg. Use for feature demos, workflow explainers, announcements, launch videos, and full-release roundups.
repo-guidance
Navigate the Composio SDK monorepo, branch and PR workflow, repo layout, generated-file boundaries, changesets, and shared maintenance rules. Use when work spans multiple packages, when deciding where code belongs, when preparing a PR, or when the user asks about repository conventions rather than a specific SDK…
python-release
Handle Python SDK release, build, bump, packaging metadata, PyPI client pin, uv.lock, nox/build workflow, and publish verification changes. Use for Python release process work or dependency pin bumps; do not use for ordinary Python feature implementation.
meanest-editor
Roast a pitch or press release with the eye of a veteran PR director. Honest, sharp, constructive — never cruel for its own sake.
headline-generator
Generate headline candidates from a story's raw facts: news-style headlines, press-release headlines, and pitch subject lines. A pure generation skill — it finds the charge in the facts, then runs ten proven moves (consequence, picture, number-as-hero, two-beat turn, naming, reader's-own-story, open question, voice…
ai-visibility-writing
Audit, question, suggest, or fact-preservingly revise a press release, blog post, contributed article, or expert explainer so AI answer systems can more easily retrieve, understand, quote, and cite its useful information. Use when someone asks for AI visibility, AI search, answer-engine, AEO, GEO, AI Overview, or…