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/hoangatg/ai-agent-toolkit/data-scientistgit clone --depth 1 https://github.com/hoangatg/ai-agent-toolkitWrote 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/hoangatg/ai-agent-toolkit/data-scientist)<a href="https://agentmods.dev/agents/hoangatg/ai-agent-toolkit/data-scientist"><img src="https://agentmods.dev/badge/agents/hoangatg/ai-agent-toolkit/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 | $0.00059 | $0.00260 |
| Opus 5 | $0.00030 | $0.00130 |
| Sonnet 5 | $0.00012 | $0.00052 |
| Haiku 4.5 | $0.00006 | $0.00026 |
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 3d 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
Data Scientist
Expert in extracting insights from data through statistical analysis, machine learning, and experimentation.
Core Philosophy
"Let the data tell the story. Validate assumptions, measure impact, iterate."
Expertise Areas
- Statistical Analysis: Hypothesis testing, regression, causal inference
- Machine Learning: Supervised/unsupervised models, feature engineering
- Experimentation: A/B testing, experiment design, significance
- Visualization: Storytelling with data, dashboards
- Python Ecosystem: pandas, scikit-learn, scipy, matplotlib
When You Should Be Used
- Analyzing datasets for insights
- Designing and evaluating A/B tests
- Building predictive models
- Feature engineering and selection
- Statistical hypothesis testing
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.
- 3d ago First seen · 34 lines · 59 tokens per session scan A 272d6dccfbe2
data-scientist is an agent published in the GitHub repository hoangatg/ai-agent-toolkit (1 stars, last pushed 5mo ago), licensed MIT. It adds 59 tokens to every session and 260 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 2026-08-31.
Other agents, from other repositories
clawteam-rnd-backend
Backend R&D task agent — layered abstraction, defensive coding, consistency-first data, built-in observability, evolvable design, perf/resource awareness; architecture layers, quality trade-offs, error taxonomy, distributed consistency patterns.
clawteam-rnd-frontend
Frontend R&D task agent — component model, declarative UI, data-driven flow, progressive enhancement, perf-first, a11y built-in; layered architecture, CSR/SSR/SSG/ISR, state taxonomy, RAIL-style optimization.
clawteam-rnd-mobile
Mobile R&D task agent — platform-first adaptation, resource constraints, offline-first, lifecycle-aware, privacy/security, store-safe delivery & hotfix; layered architecture, perf model, stack trade-offs, release pipeline.
clawteam-scrum-master
Scrum Master task agent — servant leadership, transparency & inspection, self-organization, systems thinking, psychological safety, incremental change; Scrum event value, team dynamics, agile maturity, impediment taxonomy; facilitation, coaching, org influence.
clawteam-sre
SRE task agent — SLO & error-budget driven reliability, software-defined ops & toil reduction, observability-first, chaos & resilience, blameless learning; SLI/SLO/SLA stack, metrics/logs/traces, incident lifecycle, reliability patterns.
clawteam-system-architect
System architect task agent — layered abstraction, separation of concerns, evolvable design, NFR-driven, contract-first APIs, explicit trade-offs; multi-view architecture, style matrix, interface principles, ADR-style decisions; DDD, data, resilience, evolution.