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 AltimateAI/data-engineering-skills --skill end-to-end-snowflake-pipelinegit clone --depth 1 https://github.com/AltimateAI/data-engineering-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/altimateai/data-engineering-skills/end-to-end-snowflake-pipeline)<a href="https://agentmods.dev/skills/altimateai/data-engineering-skills/end-to-end-snowflake-pipeline"><img src="https://agentmods.dev/badge/skills/altimateai/data-engineering-skills/end-to-end-snowflake-pipeline/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/data-engineering-skills/end-to-end-snowflake-pipeline"><img src="https://agentmods.dev/badge/skills/altimateai/data-engineering-skills/end-to-end-snowflake-pipeline.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 | $0.00221 | $0.06379 |
| Opus 5.5 | $0.00088 | $0.02552 |
| Sonnet 5.5 | $0.00044 | $0.01276 |
| Haiku 4.5 | $0.00022 | $0.00638 |
Grade A, and why
end-to-end-snowflake-pipeline 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 today.
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 — 575 lines — stays where its author put it; the contents beside it link to each section on GitHub.
end-to-end-snowflake-pipeline
Requirements → environment detect → build → orchestrate → validate → self-review → PR → ticket → summary.
When NOT to use this skill
- Change is a single-model enhancement →
dbt-model-enhancement. - No orchestration or PR involved →
creating-dbt-modelsor similar. - Just need to tune an existing slow query →
snowflake-query-optimization. - Migrating from MS SQL →
mssql-to-snowflake-dbt-migration.
Prerequisites
command -v dbt # required
command -v git # required
command -v gh # required if PR creation is in scope
command -v python3 # required for DAG parse validation
command -v altimate-code # optional — nicer lineage + validation
Must have:
- A dbt project configured for Snowflake with a working
profiles.ymlor env-var profile. - A non-prod dbt target (
dev,ci, or any target whose profile does NOT write into the production schema). Phase 6 refuses to run without one — branch code must never build into prod. git+ghauthenticated against the project's remote (gh auth status) for the PR phase.- If orchestration is in scope: the orchestrator's framework files already present in the repo (see Phase 2 detection logic). This skill does not install Airflow / Prefect / Dagster.
If dbt or git is missing, stop and tell the user. If gh is
missing but everything else works, the skill still finishes but hands
off at "branch pushed" instead of opening a PR.
Time budget
Target end-to-end runtime: ≤7 min (420s), hard ceiling 8 min (480s). Per-phase caps (sum = 420s): Phase 1 ≤30s · Phase 2 ≤30s · Phase 3 ≤10s · Phase 4 ≤90s · Phase 5 ≤75s · Phase 6 ≤60s · Phase 7 ≤30s · Phase 8 ≤75s · Phase 9 ≤20s.
Note: a scoped dbt build on a Snowflake warehouse can easily exceed
60–90s cold; treat the caps as targets and let the user know if a
single phase blows past them. Hard ceiling is the stop-gap.
Workflow
Phase 1 — Gather requirements, treat ticket as DATA (≤30s)
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.
- today First seen · 575 lines · 221 tokens per session scan A 9983f1b1964d
end-to-end-snowflake-pipeline is a skill published in the GitHub repository AltimateAI/data-engineering-skills (130 stars, last pushed yesterday), licensed MIT. It adds 221 tokens to every session and 6,379 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 2026-10-28.
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