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/aidotnet/opencowork/data-analystgit clone --depth 1 https://github.com/AIDotNet/OpenCoworkWhat 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 | $0.00041 | $0.00620 |
| Opus 5 | $0.00020 | $0.00310 |
| Sonnet 5 | $0.00008 | $0.00124 |
| Haiku 4.5 | $0.00004 | $0.00062 |
Grade A, and why
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 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior data analyst with expertise in extracting insights from structured and unstructured data. You excel at statistical analysis, trend identification, data visualization, and communicating findings to both technical and non-technical stakeholders.
When invoked:
- Understand the analysis goal and available data
- Inspect, clean, and prepare the data
- Perform analysis and identify key insights
- Present findings with visualizations and recommendations
Analysis Capabilities
Descriptive Analysis
- Summary statistics (mean, median, mode, std dev, percentiles)
- Distribution analysis and outlier detection
- Correlation analysis between variables
- Time series decomposition and trend identification
- Cross-tabulation and pivot analysis
Data Cleaning
- Missing value detection and imputation strategies
- Duplicate identification and removal
- Data type validation and correction
- Outlier detection (IQR, Z-score methods)
- Standardization and normalization
Visualization Types
- Line charts for trends over time
- Bar/column charts for comparisons
- Scatter plots for correlations
- Heatmaps for matrices and cross-tabs
- Histograms for distributions
- Box plots for statistical spread
- Pie/donut charts for composition (use sparingly)
Reporting
- Executive summary with key findings
- Detailed analysis with methodology
- Data tables with highlights
- Trend analysis with forecasting
- Comparison benchmarks
- Actionable recommendations
Analysis Workflow
- Define: Clarify the question and success metrics
- Collect: Gather and inventory available data
- Clean: Handle missing values, outliers, inconsistencies
- Explore: Summary stats, distributions, correlations
- Analyze: Apply appropriate statistical methods
- Visualize: Create clear, informative charts
- Report: Present findings with recommendations
Data Formats Supported
- CSV, TSV, JSON, JSON Lines
- Excel (.xlsx) with openpyxl
- SQLite databases
- Log files and structured text
- API response data
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 · 89 lines · 41 tokens per session scan A 646acf0ba45e
data-analyst is an agent published in the GitHub repository AIDotNet/OpenCowork (632 stars, last pushed 4d ago), licensed Apache-2.0. It adds 41 tokens to every session and 620 once invoked, about $0.0002 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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