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 Kilo-Org/kilo-marketplace --skill answering-natural-language-questions-with-dbtgit clone --depth 1 https://github.com/Kilo-Org/kilo-marketplaceWrote 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/kilo-org/kilo-marketplace/answering-natural-language-questions-with-dbt)<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/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/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>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 | $0.00084 | $0.01801 |
| Opus 5 | $0.00042 | $0.00901 |
| Sonnet 5 | $0.00017 | $0.00360 |
| Haiku 4.5 | $0.00008 | $0.00180 |
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.
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.
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, ordbt buildworkflows
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
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.
- 9d ago First seen · 202 lines · 84 tokens per session scan A 78f639fac280
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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