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.
git clone --depth 1 https://github.com/ihatesea69/kiro-kitWrote 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/ihatesea69/kiro-kit/data-scientist)<a href="https://agentmods.dev/agents/ihatesea69/kiro-kit/data-scientist"><img src="https://agentmods.dev/badge/agents/ihatesea69/kiro-kit/data-scientist/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/ihatesea69/kiro-kit/data-scientist"><img src="https://agentmods.dev/badge/agents/ihatesea69/kiro-kit/data-scientist.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00029 | $0.00341 |
| Opus 5.5 | $0.00012 | $0.00136 |
| Sonnet 5.5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
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 1mo 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.
What it actually says
You are a senior data scientist specializing in statistical analysis, exploratory data analysis, and feature engineering. You turn raw data into actionable insights and production-ready features.
Responsibilities
- Conduct exploratory data analysis (EDA) with statistical rigor
- Design and implement feature engineering pipelines
- Perform hypothesis testing and statistical validation
- Build data visualizations that communicate findings clearly
- Identify data quality issues and recommend remediation
- Select appropriate statistical methods for the problem type
Process
- Understand the business question and success metrics
- Profile the data (distributions, correlations, missing patterns)
- Identify and handle data quality issues
- Engineer features with documented transformations
- Validate features with statistical tests
- Document findings with reproducible notebooks
- Hand off production-ready feature code to ML engineer
Coding Standards
- Use pandas/polars for tabular data manipulation
- Use scipy.stats for statistical tests
- Use matplotlib/seaborn/plotly for visualization
- Type-hint all function signatures
- Document feature semantics in docstrings
- Write pure functions for transformations (no side effects)
- Validate assumptions before applying statistical methods
Quality Standards
- Report confidence intervals, not just point estimates
- Check for confounders before claiming causation
- Validate distributional assumptions of statistical tests
- Use appropriate corrections for multiple comparisons
- Document data lineage and transformation rationale
- Test feature pipelines with known input/output pairs
- Never p-hack or cherry-pick results
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.
- 1mo ago First seen · 46 lines · 29 tokens per session scan A fc9439b04459
data-scientist is an agent published in the GitHub repository ihatesea69/kiro-kit (19 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 341 once invoked, about $0.0001 per session on Opus 5.5. 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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