answering-natural-language-questions-with-dbt

answering-natural-language-questions-with-dbt is a skill for Claude Code from Kilo-Org/kilo-marketplace. It costs 84 tokens per session (1,801 once invoked), scanned A, a copy of answering-natural-language-questions-with-dbt, Apache-2.0.

A guide for answering business questions with warehouse data through dbt’s Semantic Layer or SQL. A data warehouse is a system that stores organized data for analysis, while metrics are defined business measurements such as revenue or customer count.

In plain words
What is it for?
Use it to answer questions about sales, customers, revenue, regions, KPIs, and other analytical measures.
Why use it?
It turns questions written in everyday language into data-backed answers using existing metrics and models where possible.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Good fit Use it to answer questions about sales, customers, revenue, regions, KPIs, and other analytical measures.

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Install with agentmods
npx agentmods add skills/kilo-org/kilo-marketplace/answering-natural-language-questions-with-dbt
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 Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt
Clone the repo
git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace

Made for: Claude Code.

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 answering-natural-language-questions-with-dbt

README.md
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Your own site
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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.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/kilo-org/kilo-marketplace/answering-natural-language-questions-with-dbt"><img src="https://agentmods.dev/badge/skills/kilo-org/kilo-marketplace/answering-natural-language-questions-with-dbt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,801 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.
Origin 100% copy Near-identical to another mod 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 $0.00084 $0.01801
Opus 5 $0.00042 $0.00901
Sonnet 5 $0.00017 $0.00360
Haiku 4.5 $0.00008 $0.00180

Measured 9d ago against content hash 78f639fac280, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

answering-natural-language-questions-with-dbt 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 9d 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.

Origin

This is a copy

100% identical to answering-natural-language-questions-with-dbt — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/dbt/skills/answering-natural-language-questions-with-dbt/SKILL.md · 202 lines

How it starts

The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Answering Natural Language Questions with dbt

Overview

Answer data questions using the best available method: semantic layer first, then SQL modification, then model discovery, then manifest analysis. Always exhaust options before saying "cannot answer."

Use for: Business questions from users that need data answers

  • "What were total sales last month?"
  • "How many active customers do we have?"
  • "Show me revenue by region"

Not for:

  • Validating model logic during development
  • Testing dbt models or semantic layer definitions
  • Building or modifying dbt models
  • dbt run, dbt test, or dbt build workflows

Decision Flow

flowchart TD
    start([Business question received])
    check_sl{Semantic layer tools available?}
    list_metrics[list_metrics]
    metric_exists{Relevant metric exists?}
    get_dims[get_dimensions]
    sl_sufficient{SL can answer directly?}
    query_metrics[query_metrics]
    answer([Return answer])
    try_compiled[get_metrics_compiled_sql<br/>Modify SQL, execute_sql]
    check_discovery{Model discovery tools available?}
    try_discovery[get_mart_models<br/>get_model_details<br/>Write SQL, execute]
    check_manifest{In dbt project?}
    try_manifest[Analyze manifest/catalog<br/>Write SQL]
    cannot([Cannot answer])
    suggest{In dbt project?}
    improvements[Suggest semantic layer changes]
    done([Done])

    start --> check_sl
    check_sl -->|yes| list_metrics
    check_sl -->|no| check_discovery
    list_metrics --> metric_exists
    metric_exists -->|yes| get_dims
    metric_exists -->|no| check_discovery
    get_dims --> sl_sufficient
    sl_sufficient -->|yes| query_metrics
    sl_sufficient -->|no| try_compiled
    query_metrics --> answer
    try_compiled -->|success| answer
    try_compiled -->|fail| check_discovery
    check_discovery -->|yes| try_discovery
    check_discovery -->|no| check_manifest
    try_discovery -->|success| answer
    try_discovery -->|fail| check_manifest
    check_manifest -->|yes| try_manifest
    check_manifest -->|no| cannot
    try_manifest -->|SQL ready| answer
    answer --> suggest
    cannot --> done
    suggest -->|yes| improvements
    suggest -->|no| done
    improvements --> done

Read the full file on GitHub · 202 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. 9d ago First seen · 202 lines · 84 tokens per session scan A 78f639fac280

Subscribe to this mod's changes

answering-natural-language-questions-with-dbt is a skill published in the GitHub repository Kilo-Org/kilo-marketplace (175 stars, last pushed 22d ago), licensed Apache-2.0. It adds 84 tokens to every session and 1,801 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to answering-natural-language-questions-with-dbt, differing in 0 lines, and is treated as a copy.

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