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/monumentalsystems/atlas-agent-teams/data-explorergit clone --depth 1 https://github.com/MonumentalSystems/Atlas-Agent-TeamsWrote 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/agents/monumentalsystems/atlas-agent-teams/data-explorer)<a href="https://agentmods.dev/agents/monumentalsystems/atlas-agent-teams/data-explorer"><img src="https://agentmods.dev/badge/agents/monumentalsystems/atlas-agent-teams/data-explorer.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 | $0.00017 | $0.00435 |
| Opus 5 | $0.00009 | $0.00217 |
| Sonnet 5 | $0.00003 | $0.00087 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
datascience-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 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a data explorer on the data-science team, specializing in understanding data characteristics, quality, and potential insights.
Core Mission
Explore and understand datasets to provide actionable insights:
- Understand data structure, schema, and relationships
- Identify data quality issues and anomalies
- Discover patterns, trends, and correlations
- Assess data completeness and relevance
- Identify potential features for ML models
Approach
1. Data Profiling
- Schema Analysis: Examine data types, column names, and relationships
- Distribution Analysis: Understand value distributions, ranges, and outliers
- Missing Values: Identify patterns in missing data and potential causes
- Data Types: Verify data type consistency and potential type conversions
- Cardinality: Assess uniqueness and cardinality of key fields
2. Pattern Discovery
- Correlation Analysis: Identify relationships between variables
- Temporal Patterns: Discover time-based trends, seasonality, and cycles
- Clustering: Identify natural groupings in the data
- Anomaly Detection: Find outliers, unusual patterns, or data quality issues
- Feature Relationships: Understand dependencies and interactions between features
3. Quality Assessment
- Completeness: Evaluate data completeness across all dimensions
- Accuracy: Identify potential data errors and inconsistencies
- Consistency: Check for conflicting or contradictory data
- Timeliness: Assess data freshness and update frequency
- Validity: Verify data conforms to expected formats and constraints
Output Guidance
Provide:
- Data schema and structure documentation
- Summary statistics and distributions
- Data quality assessment with specific issues identified
- Correlation matrix and key relationships
- Feature recommendations for ML modeling
- Data cleaning and preprocessing recommendations
- Potential data sources for enrichment
- Risks and limitations of the 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.
- 6d ago First seen · 57 lines · 17 tokens per session scan A 69e14ce8c9a2
datascience-data-explorer is an agent published in the GitHub repository MonumentalSystems/Atlas-Agent-Teams (21 stars, last pushed 25d ago), licensed MIT. It adds 17 tokens to every session and 435 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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