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 ai-analyst-lab/ai-analyst --skill setup-snowflakegit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/ai-analyst-lab/ai-analyst/setup-snowflake)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/setup-snowflake"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/setup-snowflake/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/ai-analyst-lab/ai-analyst/setup-snowflake"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/setup-snowflake.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 | $0.00131 | $0.01771 |
| Opus 5 | $0.00066 | $0.00886 |
| Sonnet 5 | $0.00026 | $0.00354 |
| Haiku 4.5 | $0.00013 | $0.00177 |
Grade C, and why
setup-snowflake scanned grade C with 2 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 2d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
1. Install `uv`: `curl -LsSf https://astral.sh/uv/install.sh | sh`, then `~/.local/bin/uvx --version`. Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. Install `uv`: `curl -LsSf https://astral.sh/uv/install.sh | sh`, then `~/.local/bin/uvx --version`. How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Setup Snowflake
Purpose
Guided first-time Snowflake setup for the native ConnectionManager path — the connection the
AI Analyst uses for every query, so every result is traced and logged. This wizard collects the
connection details, writes credentials safely, registers the dataset, and then proves the session
is actually on the warehouse (not the local practice copy) before it will report success.
This is deliberately native-first. The MCP server (snowflake-labs-mcp via uvx) is a separate,
optional tool for interactive ad-hoc queries and is kept in the Appendix. The analyst does not query
through the MCP, so setting up only the MCP leaves the analyst unconnected. Set up the native path
first.
When to Use
/setup-snowflake, "set up snowflake", "connect to snowflake", "I have a snowflake account"- Routed here from
/connect-datawhen the user selects Snowflake
Prerequisite: the driver
The native path needs snowflake-connector-python. Check it and install if missing:
python3 -c "import snowflake.connector; print('driver OK')" || pip install snowflake-connector-python
(It also ships in the warehouses extra: pip install -e ".[warehouses]".)
Step 1: Collect the connection details
The repo ships blank, so ask for every field, one question at a time. In a class the instructor will read these out; leave each empty until the user gives it. Collect:
- Account identifier — e.g.
ORGNAME-ACCOUNTNAME(Snowsight: your name, bottom-left, then Account, then View account details). - Username
- Password — "Paste it and I will write it straight to
.env, never to the terminal." - Warehouse — the compute warehouse to run on (e.g.
ANALYST_WH). - Database
- Schema — default
PUBLICif they do not say. - Role — optional; skip if they do not use one.
- A short dataset name for this connection (used as the dataset id, lowercase-hyphen).
Credential security (non-negotiable):
- Never echo, print, or log the password; never pass it as a CLI arg (visible in
ps). - Write secrets only with the Write/Edit tool, never
bash echo/cat.
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.
- 2d ago First seen · 146 lines · 131 tokens per session scan C 1e0fd162eb4d
setup-snowflake is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 131 tokens to every session and 1,771 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-12.
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