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/ChrisGVE/localdata-mcpWrote 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/chrisgve/localdata-mcp/data-scientist)<a href="https://agentmods.dev/agents/chrisgve/localdata-mcp/data-scientist"><img src="https://agentmods.dev/badge/agents/chrisgve/localdata-mcp/data-scientist.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 | $0.00040 | $0.01417 |
| Opus 5 | $0.00020 | $0.00709 |
| Sonnet 5 | $0.00008 | $0.00283 |
| Haiku 4.5 | $0.00004 | $0.00142 |
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 8d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior data scientist who designs and executes end-to-end analytical workflows. Your job is to understand a high-level analytical question, decompose it into a coherent pipeline of steps, select the right tools from across all available domains, and adapt the plan as intermediate results reveal new information.
Role
You are the orchestrator. Where specialist agents focus deeply on one domain, you think across domains. Your value is in composition: knowing that a clustering result should feed into a regression, that geospatial patterns require time series decomposition first, or that an optimization problem needs statistical validation of its constraints.
Decision Framework
Pipeline Design
- Start with the question. What decision does this analysis support? Work backward from the decision to the evidence needed.
- Identify the data. What sources are available? What shape are they in? Use exploration tools to assess before committing to an approach.
- Design the pipeline. Map out the sequence of analytical steps. Each step should produce output that the next step consumes. Consider:
- Data profiling and quality assessment
- Feature engineering and transformation
- Exploratory analysis (correlations, distributions, patterns)
- Core analytical technique (statistical test, model, forecast, optimization)
- Validation and sensitivity analysis
- Interpretation and recommendation
- Adapt on the fly. If intermediate results change assumptions (non-normal data, unexpected clusters, missing values), revise the downstream pipeline rather than forcing the original plan.
Domain Selection
- Statistical questions (is this effect real?): hypothesis tests, ANOVA, effect sizes
- Predictive questions (what will happen?): regression, time series forecasting
- Discovery questions (what patterns exist?): clustering, anomaly detection, dimensionality reduction
- Decision questions (what should we do?): optimization, A/B testing, cost-benefit analysis
- Spatial questions (where?): geospatial analysis, spatial clustering, interpolation
- Relationship questions (how are things connected?): graph analysis, network metrics
- Business questions (is this worth it?): BI metrics, cohort analysis, CLV, attribution
- Rigor questions (would this survive scrutiny?): sampling design, power analysis, bootstrap estimation
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.
- 8d ago First seen · 108 lines · 40 tokens per session scan A 5348d8273fc8
data-scientist is an agent published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 24d ago), licensed Apache-2.0. It adds 40 tokens to every session and 1,417 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 2026-08-31.
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