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/data-explorergit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWhat 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 | $0.03079 |
| Opus 5 | $0.00000 | $0.01540 |
| Sonnet 5 | $0.00000 | $0.00616 |
| Haiku 4.5 | $0.00000 | $0.00308 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- data-explorer — 88% identical, 43 lines differ
How it starts
The opening of the file, as written. The whole thing — 268 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 · 268 lines · 0 tokens per session scan A 6287bf0b8765
data-explorer is an agent published in the GitHub repository ai-analyst-lab/ai-analyst (296 stars, last pushed 6d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,079 tokens. 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.