Borrowing it
Nothing to install: this file belongs to AltimateAI/altimate-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/AltimateAI/altimate-code/main/.opencode/skills/dbt-develop/SKILL.mdgit clone --depth 1 https://github.com/AltimateAI/altimate-codeWrote 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/altimateai/altimate-code/dbt-develop)<a href="https://agentmods.dev/skills/altimateai/altimate-code/dbt-develop"><img src="https://agentmods.dev/badge/skills/altimateai/altimate-code/dbt-develop/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/altimateai/altimate-code/dbt-develop"><img src="https://agentmods.dev/badge/skills/altimateai/altimate-code/dbt-develop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 79 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 407 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00294 | $0.05370 |
| Opus 5.5 | $0.00118 | $0.02148 |
| Sonnet 5.5 | $0.00059 | $0.01074 |
| Haiku 4.5 | $0.00029 | $0.00537 |
Grade A, and why
dbt-develop 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 1mo 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 — 425 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbt Model Development
Requirements
Agent: builder or migrator (requires file write access)
Tools used: bash (runs altimate-dbt commands), read, glob, write, edit, schema_search, dbt_profiles, sql_analyze, altimate_core_validate, altimate_core_column_lineage
When to Use This Skill
Use when the user wants to:
- Create a new dbt model (staging, intermediate, mart, OBT)
- Add or modify SQL logic in an existing model
- Generate sources.yml or schema.yml from warehouse metadata
- Reorganize models into layers (staging/intermediate/mart or bronze/silver/gold)
- Convert a model to incremental materialization
- Scaffold a new dbt project structure
Do NOT use for:
- Adding tests to models → use
dbt-test - Writing model/column descriptions → use
dbt-docs - Debugging build failures → use
dbt-troubleshoot - Analyzing change impact → use
dbt-analyze
Core Workflow: Plan → Discover → Write → Validate
1. Plan — Understand Before Writing
Before writing any SQL:
- Read the task requirements carefully
- Enumerate every concrete deliverable the task asks for — write down each
model name, every column/test/config change mentioned, and any "create N
models" count. This list becomes the checklist you verify against in
step 4. A task asking for four models is not done if only three exist on
disk. If the task references a
schema.yml,_models.yml, or similar spec file, every entry there is a deliverable. - Identify which layer this model belongs to (staging, intermediate, mart)
- Check existing models for naming conventions and patterns
- Check dependencies: If
packages.ymlexists, check fordbt_packages/orpackage-lock.yml. Only rundbt depsif packages are declared but not yet installed.
altimate-dbt info # project name, adapter type
altimate-dbt parents --model <upstream> # understand what feeds this model
altimate-dbt children --model <downstream> # understand what consumes it
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 425 lines · 294 tokens per session scan A 4fabb7dba684
dbt-develop is a skill published in the GitHub repository AltimateAI/altimate-code (813 stars, last pushed today), licensed MIT. It adds 294 tokens to every session and 5,370 once invoked, about $0.0012 per session on Opus 5.5. 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
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
aeon
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard…
geopandas
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving…
hdf5-pde-data-loading
Patterns for loading PDE simulation datasets (PDEBench, PhiFlow, JAX-CFD) from HDF5 files. Handles layout detection (single tensor vs separate variables), spatial/temporal downsampling, multi-variable systems, HuggingFace and DaRUS data sources, and efficient PyTorch DataLoader creation. Use when preparing PDE data…
markitdown
Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.
polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.