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 khalilbenaz/claude-skills-collection --skill dbt-guidegit clone --depth 1 https://github.com/khalilbenaz/claude-skills-collectionWrote 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/khalilbenaz/claude-skills-collection/dbt-guide)<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/dbt-guide"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/dbt-guide/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/khalilbenaz/claude-skills-collection/dbt-guide"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/dbt-guide.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 | $0.00074 | $0.02033 |
| Opus 5 | $0.00037 | $0.01017 |
| Sonnet 5 | $0.00015 | $0.00407 |
| Haiku 4.5 | $0.00007 | $0.00203 |
Grade A, and why
dbt-guide 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.
How it starts
The opening of the file, as written. The whole thing — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guide dbt
Workflow
1. Initialiser et structurer le projet
dbt init mon_projet # crée le squelette
cd mon_projet
dbt debug # vérifie la connexion au warehouse
Arborescence cible :
models/
staging/ ← nettoyage source, renommage, cast ; materialisation = view
intermediate/ ← jointures, logique métier complexe ; materialisation = view ou ephemeral
marts/ ← modèles finaux consommables ; materialisation = table
macros/
tests/
seeds/
snapshots/
dbt_project.yml — materialisations par dossier :
models:
mon_projet:
staging:
+materialized: view
+schema: staging
intermediate:
+materialized: view
marts:
+materialized: table
+schema: marts
2. Déclarer les sources
# models/staging/sources.yml
version: 2
sources:
- name: raw_crm
schema: raw
freshness:
warn_after: {count: 12, period: hour}
error_after: {count: 24, period: hour}
loaded_at_field: _loaded_at
tables:
- name: orders
description: "Commandes brutes issues du CRM"
columns:
- name: order_id
tests: [unique, not_null]
Référencer dans un modèle :
select * from {{ source('raw_crm', 'orders') }}
Tester la fraîcheur :
dbt source freshness
3. Développer les modèles
Staging — un modèle par table source, préfixe stg_ :
-- models/staging/stg_orders.sql
with source as (
select * from {{ source('raw_crm', 'orders') }}
),
renamed as (
select
order_id::varchar as order_id,
customer_id::int as customer_id,
created_at::timestamp as created_at,
status::varchar as status
from source
)
select * from renamed
Mart — utilise ref() pour chaque dépendance :
-- models/marts/fct_orders.sql
with orders as (
select * from {{ ref('stg_orders') }}
),
customers as (
select * from {{ ref('stg_customers') }}
)
select
o.order_id,
o.created_at,
c.customer_name,
o.status
from orders o
left join customers c on o.customer_id = c.customer_id
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 · 289 lines · 74 tokens per session scan A 4bdd502ad0b1
dbt-guide is a skill published in the GitHub repository khalilbenaz/claude-skills-collection (22 stars, last pushed 19d ago), licensed MIT. It adds 74 tokens to every session and 2,033 once invoked, about $0.0004 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 2026-09-03.
Other skills, from other repositories
model-routing-patterns
Multi-model pipelines (Haiku/Sonnet/Opus): cost routing, escalation, fallback chains. Triggers: model routing, Haiku, Sonnet, Opus, escalation, fallback chain.
rag-patterns
RAG: embeddings, chunking, hybrid search (BM25+vector), reranking, CRAG, multi-hop. Triggers: RAG, embedding, pgvector, Qdrant, Pinecone, Weaviate, reranker, semantic search.
evaluate
Evaluates RAG retrieval and LLM-as-judge metrics (faithfulness, relevancy, context precision). Triggers: measure RAG quality, knowledge gap, RAG eval, golden dataset.
json-mode-patterns
Structured JSON output from Claude: tool-use-as-JSON, schema, parsing, partial recovery. Triggers: JSON mode, structured output, schema validation, JSON parsing.
index
Reindexes KB for semantic search via vector store (Qdrant). Triggers: reindex KB, rebuild index, vector reindex, refresh embeddings.
ai-portable-setup
Erstellt einen portablen KI-Arbeitsbereich auf einem USB-Stick oder beliebigen Laufwerk. RAG-Pipeline mit lokalen LLM-Modellen (Ollama), Vektordatenbank (ChromaDB) und vorkonfigurierten Prompts.