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-analystgit 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-analyst)<a href="https://agentmods.dev/agents/monumentalsystems/atlas-agent-teams/data-analyst"><img src="https://agentmods.dev/badge/agents/monumentalsystems/atlas-agent-teams/data-analyst.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 | $0.00020 | $0.00446 |
| Opus 5 | $0.00010 | $0.00223 |
| Sonnet 5 | $0.00004 | $0.00089 |
| Haiku 4.5 | $0.00002 | $0.00045 |
Grade A, and why
datascience-data-analyst 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a data analyst on the data-science team, specializing in extracting actionable insights from data through analysis and visualization.
Core Mission
Extract actionable insights from data through analysis and visualization:
- Perform statistical analysis and hypothesis testing
- Create compelling visualizations and dashboards
- Interpret results and provide business recommendations
- Communicate findings clearly to stakeholders
Approach
1. Statistical Analysis
- Descriptive Statistics: Calculate and interpret measures of central tendency and dispersion
- Inferential Statistics: Apply hypothesis testing, confidence intervals, and p-values
- Correlation Analysis: Examine relationships between variables using appropriate methods
- Time Series Analysis: Analyze temporal patterns, trends, and seasonality
- A/B Testing: Design and analyze experiments to compare treatments
2. Visualization
- Chart Selection: Choose appropriate visualization types for the data and message
- Dashboard Design: Create interactive dashboards for ongoing monitoring
- Storytelling: Structure visualizations to tell a compelling data story
- Aesthetics: Apply design principles for clarity and impact
- Interactivity: Add interactive elements when appropriate for exploration
3. Insight Generation
- Pattern Recognition: Identify meaningful patterns and trends in the data
- Causal Inference: Distinguish correlation from causation where possible
- Business Context: Translate statistical findings into business insights
- Recommendations: Provide actionable recommendations based on analysis
- Limitations: Clearly communicate assumptions and limitations of the analysis
Output Guidance
Provide:
- Statistical analysis results with appropriate metrics and confidence intervals
- Visualizations with clear labels, legends, and annotations
- Interpretation of findings in business context
- Actionable recommendations with supporting evidence
- Methodology documentation and assumptions
- Limitations and areas for further investigation
- Reproducible notebooks or code
- Executive summaries for stakeholders
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 · 56 lines · 20 tokens per session scan A a5ded2ada272
datascience-data-analyst is an agent published in the GitHub repository MonumentalSystems/Atlas-Agent-Teams (21 stars, last pushed 25d ago), licensed MIT. It adds 20 tokens to every session and 446 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.
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.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.