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-schema-verify/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-schema-verify)<a href="https://agentmods.dev/skills/altimateai/altimate-code/dbt-schema-verify"><img src="https://agentmods.dev/badge/skills/altimateai/altimate-code/dbt-schema-verify/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-schema-verify"><img src="https://agentmods.dev/badge/skills/altimateai/altimate-code/dbt-schema-verify.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 | $0.00216 | $0.01681 |
| Opus 5.5 | $0.00086 | $0.00672 |
| Sonnet 5.5 | $0.00043 | $0.00336 |
| Haiku 4.5 | $0.00022 | $0.00168 |
Grade A, and why
dbt-schema-verify 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbt schema-verify
When to invoke this skill — every time
Run altimate-dbt schema-verify --model <name> before declaring any of the
following tasks complete:
- Creating a new dbt model that has (or will have) a
schema.ymlentry - Modifying an existing model whose
schema.ymldeclares columns - Refactoring a CTE into its own intermediate model
- Renaming columns or changing their order
- Changing materialization config in a way that re-creates the table
- Any task that says "match the schema", "produce these columns", "the
output should have columns X, Y, Z", or references a
_models.yml - Any task with
AUTO_*_equalityorAUTO_*_existencetests on a model
If the task touched N models, run schema-verify on all N of them, not
just the last one. A build is not a verify.
How to run it
altimate-dbt schema-verify --model <name>
Note: altimate-dbt build --model <name> already runs schema-verify
automatically after a successful build and includes the verdict in its
response under a schema_verify field. You will see the diff in the same
result that reported the build outcome — read it there before deciding
the task is done. If you need to re-check after editing, call
schema-verify directly.
Returns a structured JSON result:
{
"model": "int_asana__project_user_agg",
"verdict": "mismatch",
"expected_columns": ["project_id", "users", "number_of_users_involved"],
"actual_columns": ["project_id", "users"],
"columns_extra": [],
"columns_missing": ["number_of_users_involved"],
"columns_reordered": [],
"type_mismatches": []
}
How to read the verdict
| verdict | meaning | what to do |
|---|---|---|
match |
actual columns match the spec exactly (case-insensitive on names) | DONE — proceed |
mismatch |
one or more of columns_extra, columns_missing, columns_reordered, type_mismatches is non-empty |
NOT DONE — read the diff, fix the model SQL, rebuild, re-run schema-verify |
no-spec |
the model has no columns declared in schema.yml |
DONE for shape-fidelity purposes — no contract to verify against |
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 · 147 lines · 216 tokens per session scan A 1ba97178e061
dbt-schema-verify is a skill published in the GitHub repository AltimateAI/altimate-code (813 stars, last pushed today), licensed MIT. It adds 216 tokens to every session and 1,681 once invoked, about $0.0009 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…
llm-as-judge-evaluation
Evaluate LLM outputs using frontier models as judges. Use for pairwise model comparison, quality scoring with custom rubrics, and automated evaluation pipelines. Covers position bias mitigation, statistical significance, and generating preference data for DPO/RLHF.
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.