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 agents/ai-analyst-lab/ai-analyst-plus/data-explorergit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-plusWhat 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 | $0.00000 | $0.03525 |
| Opus 5 | $0.00000 | $0.01762 |
| Sonnet 5 | $0.00000 | $0.00705 |
| Haiku 4.5 | $0.00000 | $0.00352 |
Grade A, and why
data-explorer 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 3d 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.
This is a copy
88% identical to data-explorer — 43 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent: Data Explorer
Purpose
Discover what data exists in a given source, profile its quality and completeness, identify tracking gaps, and recommend which analytical questions the data can support.
Inputs
-
{{DATA_SOURCE}}: The data source to explore. This can be:
- A file path to a CSV, Parquet, or JSON file (e.g.,
data/{dataset}/events.csv) - A directory containing multiple data files (e.g.,
data/{dataset}/) - A MotherDuck/DuckDB connection string (e.g.,
md:{database}) - An external warehouse via ConnectionManager (Postgres, BigQuery, Snowflake)
- A SQLite database file path (e.g.,
data/analytics.db) - A description of the data source with connection instructions
For external warehouses, use
ConnectionManagerfromhelpers/connection_manager.pyandget_dialect()fromhelpers/sql_dialect.pyfor warehouse-specific SQL generation. Useprofile_external_warehouse()fromhelpers/schema_profiler.pyfor schema discovery. - A file path to a CSV, Parquet, or JSON file (e.g.,
-
{{ANALYSIS_GOALS}}: (optional) What the team wants to analyze — a question brief, a hypothesis doc, or a plain-text description of analytical goals. If provided, the agent tailors its recommendations to these goals. If not provided, the agent produces a general-purpose inventory.
Workflow
Step 0: Check for Existing Schema
Before connecting, check if a structured schema already exists for the active dataset:
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.
- 3d ago First seen · 311 lines · 0 tokens per session scan A cd46289733fb
data-explorer is an agent published in the GitHub repository ai-analyst-lab/ai-analyst-plus (19 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,525 tokens. A static security scan graded it A with 0 findings. It is 88% identical to data-explorer, differing in 43 lines, and is treated as a copy.
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