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 G1Joshi/Agent-Skills --skill dbtgit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote 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/g1joshi/agent-skills/dbt)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/dbt"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/dbt.svg" alt="Measured on agentmods" 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 | $0.00015 | $0.00267 |
| Opus 5 | $0.00008 | $0.00133 |
| Sonnet 5 | $0.00003 | $0.00053 |
| Haiku 4.5 | $0.00002 | $0.00027 |
Grade A, and why
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 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.
What it actually says
dbt (Data Build Tool)
dbt manages data transformation in the warehouse using SQL. v2.0 introduces the Fusion Engine (Rust) for performance.
When to Use
- Data Modeling: Converting raw tables into "Gold" tables.
- Testing:
not_null,uniquetests defined in YAML. - Documentation: Auto-generating data dictionaries.
Core Concepts
Models (.sql)
Select statements that dbt compiles into CREATE VIEW/TABLE.
Refs ({{ ref('users') }})
Dependency management. dbt builds the DAG automatically.
Semantic Layer
Defining metrics ("Revenue") in code so all BI tools use the same definition.
Best Practices (2025)
Do:
- Use Git: Treat data models like software code.
- Use Incremental Models: Only process new data to save cost.
- Use dbt Mesh: For cross-project dependencies in large orgs.
Don't:
- Don't put logic in BI tools: Put it in dbt.
References
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 · 45 lines · 15 tokens per session scan A 1ebf543fdb12
dbt is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 267 once invoked, about $0.0001 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
deprecation-and-migration
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when migrating a database schema in production, such as renaming or dropping a column without downtime (expand/contract). Use when deciding whether to maintain or sunset…
backup-restore
PostgreSQL backup and restore with pgBackRest — full/incremental/WAL, PITR, K8s CronJob scheduling, and restore verification.
db-performance
PostgreSQL query performance — EXPLAIN ANALYZE, index design, pgstatstatements, slow query detection, connection pool tuning.
migration-safety
Safe database migrations in production — expand-and-contract, lock-safe DDL, timing estimation, rollback SQL.
redis-operations
Redis operational runbooks — memory management, eviction policy, persistence config, Sentinel/Cluster, K8s-hosted Redis ops.
database-modeling
Design relational schemas, write efficient queries, plan indexes, and implement safe migrations.