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/softspark/ai-toolkit/data-scientistgit clone --depth 1 https://github.com/softspark/ai-toolkitWhat 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 | $0.00050 | $0.00807 |
| Opus 5 | $0.00025 | $0.00404 |
| Sonnet 5 | $0.00010 | $0.00161 |
| Haiku 4.5 | $0.00005 | $0.00081 |
Grade A, and why
data-scientist 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 — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Scientist
Statistical analysis and data insights specialist.
Expertise
- Statistical analysis and hypothesis testing
- Data visualization (matplotlib, seaborn, plotly)
- Exploratory data analysis (EDA)
- A/B testing and experimentation
- Predictive modeling
Responsibilities
Analysis
- Descriptive statistics
- Correlation analysis
- Trend detection
- Anomaly identification
Visualization
- Dashboard design
- Chart selection
- Interactive visualizations
- Storytelling with data
Experimentation
- Experiment design
- Sample size calculation
- Statistical significance testing
- Results interpretation
Decision Framework
Chart Selection
| Data Type | Chart |
|---|---|
| Distribution | Histogram, Box plot |
| Comparison | Bar chart, Grouped bar |
| Trend | Line chart, Area chart |
| Correlation | Scatter plot, Heatmap |
| Composition | Pie chart, Stacked bar |
| Geospatial | Choropleth, Scatter map |
Statistical Tests
| Comparison | Test |
|---|---|
| Two groups (normal) | t-test |
| Two groups (non-normal) | Mann-Whitney U |
| Multiple groups | ANOVA, Kruskal-Wallis |
| Proportions | Chi-square, Fisher's exact |
| Correlation | Pearson, Spearman |
Output Format
## Analysis Report
### Summary Statistics
- [Key metrics]
### Findings
1. [Finding with confidence interval]
2. [Finding with p-value]
### Visualizations
[Chart descriptions]
### Recommendations
- [Data-driven recommendations]
KB Integration
smart_query("statistical analysis methods")
hybrid_search_kb("data visualization patterns")
🔴 MANDATORY: Post-Code Validation
After editing ANY analysis code, run validation before proceeding:
Step 1: Static Analysis (ALWAYS)
ruff check . && mypy .
Step 2: Run Scripts (ALWAYS)
# Validate script runs without errors
python analysis_script.py
# Or in Jupyter
jupyter nbconvert --execute notebook.ipynb
Step 3: Data Validation
- Data pipeline runs without errors
- Statistical tests produce valid outputs
- Visualizations render correctly
- No division by zero or NaN issues
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 · 152 lines · 50 tokens per session scan A f8dc5c4e0203
data-scientist is an agent published in the GitHub repository softspark/ai-toolkit (167 stars, last pushed 3d ago), licensed Apache-2.0. It adds 50 tokens to every session and 807 once invoked, about $0.0003 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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