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 agentmods add skills/wentorai/research-plugins/metabase-analytics-guidenpx skills add wentorai/research-plugins --skill metabase-analytics-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/metabase-analytics-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/metabase-analytics-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/metabase-analytics-guide.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 | $0.00018 | $0.01903 |
| Opus 5 | $0.00009 | $0.00951 |
| Sonnet 5 | $0.00004 | $0.00381 |
| Haiku 4.5 | $0.00002 | $0.00190 |
Grade A, and why
metabase-analytics-guide 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 6d 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metabase Analytics Guide
Overview
Metabase is a powerful open-source business intelligence and analytics tool with over 46K stars on GitHub. It allows researchers and data analysts to explore data, create visualizations, and build dashboards without writing SQL, though it fully supports custom SQL queries for advanced users. Metabase connects to a wide variety of databases and provides a browser-based interface that makes data exploration accessible to team members regardless of their technical background.
For academic research groups and labs, Metabase serves as an excellent self-hosted platform for tracking experimental data, monitoring research progress, and creating shared dashboards for collaborative projects. Its ability to connect directly to PostgreSQL, MySQL, SQLite, and many other databases means it can be pointed at existing research data stores without data migration. Researchers can set up automated reports, scheduled email digests, and shared dashboards that keep the entire team informed.
Metabase's no-code query builder is particularly valuable in interdisciplinary research teams where not all members are comfortable with SQL. Principal investigators, graduate students, and collaborators can all explore the same datasets through an intuitive visual interface while power users retain full SQL access for complex analyses.
Installation and Setup
Docker Deployment (Recommended)
# Quick start with Docker
docker run -d -p 3000:3000 \
--name metabase \
-v metabase-data:/metabase-data \
-e MB_DB_TYPE=postgres \
-e MB_DB_DBNAME=metabase_app \
-e MB_DB_PORT=5432 \
-e MB_DB_USER=$METABASE_DB_USER \
-e MB_DB_PASS=$METABASE_DB_PASS \
-e MB_DB_HOST=db-host \
metabase/metabase
# Access at http://localhost:3000
Docker Compose for Research Lab Setup
version: "3.9"
services:
metabase:
image: metabase/metabase:latest
container_name: research-metabase
ports:
- "3000:3000"
environment:
MB_DB_TYPE: postgres
MB_DB_DBNAME: metabase_app
MB_DB_PORT: 5432
MB_DB_USER: ${METABASE_DB_USER}
MB_DB_PASS: ${METABASE_DB_PASS}
MB_DB_HOST: postgres
MB_SITE_NAME: "Research Lab Analytics"
depends_on:
- postgres
volumes:
- metabase-data:/metabase-data
postgres:
image: postgres:16
environment:
POSTGRES_DB: metabase_app
POSTGRES_USER: ${POSTGRES_USER}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
volumes:
- pg-data:/var/lib/postgresql/data
volumes:
metabase-data:
pg-data:
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.
- 6d ago First seen · 243 lines · 18 tokens per session scan A 0333a1748f27
metabase-analytics-guide is a skill published in the GitHub repository wentorai/research-plugins (287 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 1,903 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.
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