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 connect-datagit 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/connect-data)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/connect-data"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/connect-data/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/connect-data"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/connect-data.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 | $0.00242 | $0.01908 |
| Opus 5 | $0.00121 | $0.00954 |
| Sonnet 5 | $0.00048 | $0.00382 |
| Haiku 4.5 | $0.00024 | $0.00191 |
Grade A, and why
connect-data 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 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.
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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Once a CSV folder is registered, it is SQL-queryable through
ConnectionManager().query(sql): each file becomes a table named after its file stem (orders.csv->orders), and the manifest'sfiles:list is the table inventory. Every query is auto-logged for provenance.
Skill: Connect Data
Purpose
This is an interactive setup wizard, not a documentation generator. Guide the user through the actual connection process by executing each step, creating files, testing connectivity, and setting up the knowledge system. Do not just explain what would happen — make it happen.
When to Use
- User says
/connect-dataor "connect my database" or "add a new dataset" - First-run welcome suggests connecting data
- After
/switch-datasetwhen the target dataset doesn't exist yet
Invocation
/connect-data — start the connection wizard
/connect-data type=postgres — skip type selection and go directly to Step 2
Parameter Handling: If the user provides type={connection_type} (e.g., type=bigquery, type=postgres), SKIP Step 1 entirely and proceed directly to Step 2 with that connection type already selected.
Instructions
Step 1: Choose Connection Type
Skip this step if type parameter was provided in the invocation.
Present options:
- CSV files — "I have CSV files in a local directory"
- DuckDB — "I have a local DuckDB database file"
- PostgreSQL — "I have a PostgreSQL database"
- Snowflake — "I have a Snowflake warehouse"
- BigQuery — "I have a Google BigQuery dataset"
- Databricks — "I have a Databricks SQL warehouse"
- Redshift — "I have an Amazon Redshift cluster"
- SQL Server — "I have a Microsoft SQL Server / Azure SQL database"
- MySQL — "I have a MySQL or MariaDB database"
Step 2: Collect Connection Details
For CSV:
- Ask: "What's the path to your CSV directory? (relative to this repo)"
- Verify the directory exists and contains .csv files
- List found files and ask to confirm
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 · 148 lines · 242 tokens per session scan A 546f1e255b1c
connect-data is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 242 tokens to every session and 1,908 once invoked, about $0.0012 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 2026-09-12.
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