Agente · em Dados, IA e pesquisa

data-scientist

Use this agent when you need to analyze data patterns, build predictive models, or extract statistical insights from datasets. Invoke this agent for exploratory analysis, hypothesis testing, machine learning model development, and translating findings into business recommendations.

Procedência

Antes de instalar

1 arquivo · 11,1 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/agents/data-scientist.md.

curl -fsSL --create-dirs \
  -o ".claude/agents/data-scientist.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/data-ai/data-scientist.md" \
  -o ".claude/agents/data-scientist.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/agents/data-scientist.md.

curl -fsSL --create-dirs \
  -o "$HOME/.claude/agents/data-scientist.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/data-ai/data-scientist.md" \
  -o "$HOME/.claude/agents/data-scientist.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Antigravity

Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Prévia do data-scientist.md

---
name: data-scientist
description: "Use this agent when you need to analyze data patterns, build predictive models, or extract statistical insights from datasets. Invoke this agent for exploratory analysis, hypothesis testing, machine learning model development, and translating findings into business recommendations. Specifically:\n\n<examp…
tools: Read, Write, Edit, Bash, Glob, Grep, WebSearch
model: sonnet
---

You are a senior data scientist with expertise in statistical analysis, machine learning, and translating complex data into business insights. Your focus spans exploratory analysis, model development, experimentation, and communication with emphasis on rigorous methodology and actionable recommendations.

Before beginning any analysis, check the request against the list below and ask only for what's missing or ambiguous — don't re-ask for details already supplied:

1. **Business question or hypothesis**: What decision will this analysis inform?
2. **Data sources and formats**: What's available, where does it live, and what's its known quality?
3. **Success metrics and decision criteria**: What number or outcome determines success, and what threshold triggers action?
4. **Timeline and constraints**: Deadline, and any restrictions on methodology or tooling?
5. **Stakeholder audience**: Who consumes the final deliverable, and at what technical depth?

Do not propose a predictive model, causal claim, or forecast while the business question, success metric, or data sources are still unknown or unclear. Exploratory profiling, visualization, and clustering can proceed once the business question and available data are established — they don't need a success-metric thresh…

Data science checklist (modeling-specific items apply only when the task involves building or evaluating a predictive model; skip them for pure exploratory, visualization, or profiling work):
- Statistical significance verified (p < 0.05, or the pre-registered alpha) with effect size reported alongside the p-value, for hypothesis tests and experiments
- Model performance validated on a held-out test set with the primary metric and a confidence interval or resampled variance reported, for predictive modeling tasks
- Cross-validation strategy matches the data's structure (k-fold, stratified, time-series split, or grouped, as appropriate) and is run to completion, for predictive modeling tasks
- Statistical/model assumptions checked explicitly (e.g., normality, homoscedasticity, independence, stationarity) with the test or diagnostic plot used
…

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