Agent skill

Answering Natural Language Questions With Dbt

by Kilo-Org in Kilo-Org/kilo-marketplace

Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions.

Apache-2.0Auto-check passedData & Analytics

Install Answering Natural Language Questions With Dbt

skills CLI

$ npx skills add Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install Kilo-Org/kilo-marketplace answering-natural-language-questions-with-dbt --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dbt/skills/answering-natural-language-questions-with-dbt .claude/skills/answering-natural-language-questions-with-dbt && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "answering-natural-language-questions-with-dbt" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/dbt/skills/answering-natural-language-questions-with-dbt into .claude/skills/answering-natural-language-questions-with-dbt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "answering-natural-language-questions-with-dbt", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/dbt/skills/answering-natural-language-questions-with-dbt

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install Kilo-Org/kilo-marketplace answering-natural-language-questions-with-dbt --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/dbt/skills/answering-natural-language-questions-with-dbt .agents/skills/answering-natural-language-questions-with-dbt && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "answering-natural-language-questions-with-dbt" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/dbt/skills/answering-natural-language-questions-with-dbt into .agents/skills/answering-natural-language-questions-with-dbt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "answering-natural-language-questions-with-dbt", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install Kilo-Org/kilo-marketplace answering-natural-language-questions-with-dbt --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/dbt/skills/answering-natural-language-questions-with-dbt .cursor/skills/answering-natural-language-questions-with-dbt && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "answering-natural-language-questions-with-dbt" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/dbt/skills/answering-natural-language-questions-with-dbt into .cursor/skills/answering-natural-language-questions-with-dbt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "answering-natural-language-questions-with-dbt", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/Kilo-Org/kilo-marketplace.git --path skills/dbt/skills/answering-natural-language-questions-with-dbt

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install Kilo-Org/kilo-marketplace answering-natural-language-questions-with-dbt --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/dbt/skills/answering-natural-language-questions-with-dbt .gemini/skills/answering-natural-language-questions-with-dbt && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "answering-natural-language-questions-with-dbt" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/dbt/skills/answering-natural-language-questions-with-dbt into .gemini/skills/answering-natural-language-questions-with-dbt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "answering-natural-language-questions-with-dbt", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install Kilo-Org/kilo-marketplace answering-natural-language-questions-with-dbt

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/dbt/skills/answering-natural-language-questions-with-dbt .github/skills/answering-natural-language-questions-with-dbt && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "answering-natural-language-questions-with-dbt" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/dbt/skills/answering-natural-language-questions-with-dbt into .github/skills/answering-natural-language-questions-with-dbt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "answering-natural-language-questions-with-dbt", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install Kilo-Org/kilo-marketplace answering-natural-language-questions-with-dbt --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/dbt/skills/answering-natural-language-questions-with-dbt .opencode/skills/answering-natural-language-questions-with-dbt && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "answering-natural-language-questions-with-dbt" agent skill from https://github.com/Kilo-Org/kilo-marketplace/tree/main/skills/dbt/skills/answering-natural-language-questions-with-dbt into .opencode/skills/answering-natural-language-questions-with-dbt/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "answering-natural-language-questions-with-dbt", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: answering-natural-language-questions-with-dbt
GitHub stars: 190
Token cost: ~1.9k tokens
SKILL.md length: 633 words
Files: 1
Skills in repo: 86
Repo updated: Sep 28, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions.

Works in 3 steps: list_metrics - find relevant metric → get_dimensions - verify required… → query_metrics - execute with appropriate…
A user asks about analytics
SKILL.md covers Overview, Decision Flow, Quick Reference and Approach 1: Semantic Layer Query, plus 7 more sections
Calls dbt and jq

What it does

Answering Natural Language Questions With Dbt is an agent skill from Kilo-Org/kilo-marketplace. Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, KPIs, or data (e.g., "What were total sales last quarter?", "Show me top customers by revenue"). NOT for validating, testing, or building dbt models during development.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Data pipelines and ETL and SQL. It works with dbt and SQL. The repository describes itself as: Kilo Marketplace - A curated collection of Skills, MCP Servers, and Modes for enhancing AI agent capabilities across the Kilo ecosystem—including Kilo Code (VS Code extension)… The licence is Apache-2.0.

When your agent uses it

A user asks about analytics
What were total sales last quarter?
Show me top customers by revenue)

Example prompts

“What were total sales last quarter?”
“Show me top customers by revenue”
“Use the answering-natural-language-questions-with-dbt skill to write and executes SQL queries against the data warehouse using dbt's Semantic Layer…”
“/answering-natural-language-questions-with-dbt”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

list_metrics - find relevant metric
get_dimensions - verify required dimensions exist
query_metrics - execute with appropriate filters

What it can do on your machine

Read from SKILL.md and the folder at commit ff51758. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- dbt
- jq
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Answering Natural Language Questions With Dbt loads about 1.9k tokens when it runs. Until then it costs about 98 tokens; SKILL.md has 633 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~98
When it runs · the whole SKILL.md, loaded when a task matches: ~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Kilo-Org/kilo-marketplace at commit ff51758, republished under its Apache-2.0 licence (© Kilo-Org). 633 words, ~1,942 tokens.

Download SKILL.mdSave it as .claude/skills/answering-natural-language-questions-with-dbt/SKILL.md (or your agent's skills folder).

name: answering-natural-language-questions-with-dbt
description: Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Use when a user asks about analytics, metrics, KPIs, or data (e.g., "What were total sales last quarter?", "Show me top customers by revenue"). NOT for validating, testing, or building dbt models during development.
user-invocable: false
metadata.author: dbt-labs

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

mermaid

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

Quick Reference

Priority	Condition	Approach	Tools
1	Semantic layer active	Query metrics directly	`list_metrics`, `get_dimensions`, `query_metrics`
2	SL active but minor modifications needed (missing dimension, custom filter, case when, different aggregation)	Modify compiled SQL	`get_metrics_compiled_sql`, then `execute_sql`
3	No SL, discovery tools active	Explore models, write SQL	`get_mart_models`, `get_model_details`, then `show`/`execute_sql`
4	No MCP, in dbt project	Analyze artifacts, write SQL	Read `target/manifest.json`, `target/catalog.json`

Approach 1: Semantic Layer Query

When list_metrics and query_metrics are available:

list_metrics - find relevant metric
get_dimensions - verify required dimensions exist
query_metrics - execute with appropriate filters

If semantic layer can't answer directly (missing dimension, need custom logic) → go to Approach 2.

Approach 2: Modified Compiled SQL

When semantic layer has the metric but needs minor modifications:

Missing dimension (join + group by)
Custom filter not available as a dimension
Case when logic for custom categorization
Different aggregation than what's defined

get_metrics_compiled_sql - get the SQL that would run (returns raw SQL, not Jinja)
Modify SQL to add what's needed
execute_sql to run the raw SQL
Always suggest updating the semantic model if the modification would be reusable

sql

-- Example: Adding sales_rep dimension
WITH base AS (
    -- ... compiled metric logic (already resolved to table names) ...
)
SELECT base.*, reps.sales_rep_name
FROM base
JOIN analytics.dim_sales_reps reps ON base.rep_id = reps.id
GROUP BY ...

-- Example: Custom filter
SELECT * FROM (compiled_metric_sql) WHERE region = 'EMEA'

-- Example: Case when categorization
SELECT
    CASE WHEN amount > 1000 THEN 'large' ELSE 'small' END as deal_size,
    SUM(amount)
FROM (compiled_metric_sql)
GROUP BY 1

Note: The compiled SQL contains resolved table names, not {{ ref() }}. Work with the raw SQL as returned.

Approach 3: Model Discovery

When no semantic layer but get_all_models/get_model_details available:

get_mart_models - start with marts, not staging
get_model_details for relevant models - understand schema
Write SQL using {{ ref('model_name') }}
show --inline "..." or execute_sql

Prefer marts over staging - marts have business logic applied.

Approach 4: Manifest/Catalog Analysis

When in a dbt project but no MCP server:

Check for target/manifest.json and target/catalog.json
Filter before reading - these files can be large

bash

# Find mart models in manifest
jq '.nodes | to_entries | map(select(.key | startswith("model.") and contains("mart"))) | .[].value | {name: .name, schema: .schema, columns: .columns}' target/manifest.json

# Get column info from catalog
jq '.nodes["model.project_name.model_name"].columns' target/catalog.json

Write SQL based on discovered schema
Explain: "This SQL should run in your warehouse. I cannot execute it without database access."

Show full SKILL.md (245 more words)Show less

Suggesting Improvements

When in a dbt project, suggest semantic layer changes after answering (or when cannot answer):

Gap	Suggestion
Metric doesn't exist	"Add a metric definition to your semantic model"
Dimension missing	"Add `dimension_name` to the dimensions list in the semantic model"
No semantic layer	"Consider adding a semantic layer for this data"

Stay at semantic layer level. Do NOT suggest:

Database schema changes
ETL pipeline modifications
"Ask your data engineering team to..."

Rationalizations to Resist

You're Thinking...	Reality
"Semantic layer doesn't support this exact query"	Get compiled SQL and modify it (Approach 2)
"No MCP tools, can't help"	Check for manifest/catalog locally
"User needs this quickly, skip the systematic check"	Systematic approach IS the fastest path
"Just write SQL, it's faster"	Semantic layer exists for a reason - use it first
"The dimension doesn't exist in the data"	Maybe it exists but not in semantic layer config

Red Flags - STOP

Writing SQL without checking if semantic layer can answer
Saying "cannot answer" without trying all 4 approaches
Suggesting database-level fixes for semantic layer gaps
Reading entire manifest.json without filtering
Using staging models when mart models exist
Using this to validate model correctness rather than answer business questions

Common Mistakes

Mistake	Fix
Giving up when SL can't answer directly	Get compiled SQL and modify it
Querying staging models	Use `get_mart_models` first
Reading full manifest.json	Use jq to filter
Suggesting ETL changes	Keep suggestions at semantic layer
Not checking tool availability	List available tools before choosing approach

Files

Just SKILL.md in skills/dbt/skills/answering-natural-language-questions-with-dbt of Kilo-Org/kilo-marketplace.

Open the folder on GitHubat commit ff51758

Compare with similar skills

Answering Natural Language Questions With Dbt next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Answering Natural Language Questions With Dbt compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Answering Natural Language Questions With Dbt this skillKilo-Org/kilo-marketplace	190	—	~1.9k	Automated safety check: Pass	Apache-2.0	12 days ago
Analytics Engineerborghei/Claude-Skills	891	—	~3.4k	Automated safety check: Pass	MIT	4 days ago
Monte Carlo Validation Notebooksickn33/agentic-awesome-skills	47k	1 repos	~1.1k	Automated safety check: Pass	MIT	yesterday
dbt Model BuilderAltimateAI/data-engineering-skills	128	—	~890	Automated safety check: Pass	MIT	3 days ago
dbt Error DebuggingAltimateAI/data-engineering-skills	128	—	~1.1k	Automated safety check: Pass	MIT	3 days ago
Migrating SQL To DbtAltimateAI/data-engineering-skills	128	—	~762	Automated safety check: Pass	MIT	3 days ago

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Works with

Questions about Answering Natural Language Questions With Dbt

What does Answering Natural Language Questions With Dbt do?

Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions. Answering Natural Language Questions With Dbt is an agent skill from Kilo-Org/kilo-marketplace. Writes and executes SQL queries against the data warehouse using dbt's Semantic Layer or ad-hoc SQL to answer business questions.

When should I use Answering Natural Language Questions With Dbt?

Answering Natural Language Questions With Dbt fits situations like: A user asks about analytics; what were total sales last quarter?; show me top customers by revenue).

How do I install Answering Natural Language Questions With Dbt in Claude Code?

Run `npx skills add Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt -a claude-code`. Or copy the skill folder (skills/dbt/skills/answering-natural-language-questions-with-dbt in Kilo-Org/kilo-marketplace) into .claude/skills/answering-natural-language-questions-with-dbt in your project. Claude Code loads it when a task matches its description.

How do I install Answering Natural Language Questions With Dbt in Codex?

Run `npx skills add Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt -a codex`. Or copy the skill folder (skills/dbt/skills/answering-natural-language-questions-with-dbt in Kilo-Org/kilo-marketplace) into .agents/skills/answering-natural-language-questions-with-dbt in your project. Codex loads it when a task matches its description.

Can I use Answering Natural Language Questions With Dbt in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbt -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/answering-natural-language-questions-with-dbt, .gemini/skills/answering-natural-language-questions-with-dbt, .github/skills/answering-natural-language-questions-with-dbt and .opencode/skills/answering-natural-language-questions-with-dbt in your project.

What does Answering Natural Language Questions With Dbt need to run?

Going by SKILL.md and its folder, Answering Natural Language Questions With Dbt needs the command-line tools its instructions call (dbt and jq).

Does Answering Natural Language Questions With Dbt access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Answering Natural Language Questions With Dbt safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Answering Natural Language Questions With Dbt use?

Answering Natural Language Questions With Dbt is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Answering Natural Language Questions With Dbt use?

About 1.9k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Answering Natural Language Questions With Dbt?

Skills that share tags, products or a category with Answering Natural Language Questions With Dbt: Analytics Engineer (borghei/Claude-Skills, 891 stars), Monte Carlo Validation Notebook (sickn33/agentic-awesome-skills, 47k stars), dbt Model Builder (AltimateAI/data-engineering-skills, 128 stars) and dbt Error Debugging (AltimateAI/data-engineering-skills, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Answering Natural Language Questions With Dbt?

Kilo-Org (a GitHub organization) maintains it in Kilo-Org/kilo-marketplace, which has 190 GitHub stars. The repository holds 86 skills in this directory. The repository was last updated on September 28, 2026.

Source: Kilo-Org/kilo-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Answering Natural Language Questions With Dbt

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What it can do on your machine

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Runs code

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SKILL.md

Answering Natural Language Questions with dbt

Overview

Decision Flow

Quick Reference

Approach 1: Semantic Layer Query

Approach 2: Modified Compiled SQL

Approach 3: Model Discovery

Approach 4: Manifest/Catalog Analysis

Suggesting Improvements

Rationalizations to Resist

Red Flags - STOP

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