data_analyst

Agent Skills › siddsachar/row-bot › data_analyst

data_analyst

GitHub

指导Agent以资深数据分析师思维处理数据集，涵盖背景阐述、核心发现提炼、具体数值引用、多样化图表选择及叙事解读，旨在提供严谨的统计分析、清晰的故事线和建议。

bundled_skills/data_analyst/SKILL.md siddsachar/row-bot

Trigger Scenarios

用户分享或附带数据文件请求分析数据或洞察

Install

npx skills add siddsachar/row-bot --skill data_analyst -g -y

More Options

Non-standard path

npx skills add https://github.com/siddsachar/row-bot/tree/main/bundled_skills/data_analyst -g -y

Use without installing

npx skills use siddsachar/row-bot@data_analyst

指定 Agent (Claude Code)

npx skills add siddsachar/row-bot --skill data_analyst -a claude-code -g -y

安装 repo 全部 skill

npx skills add siddsachar/row-bot --all -g -y

预览 repo 内 skill

npx skills add siddsachar/row-bot --list

SKILL.md

Frontmatter

{
    "icon": "📊",
    "name": "data_analyst",
    "tags": [
        "analysis",
        "data",
        "visualization"
    ],
    "author": "Row-Bot",
    "version": "1.1",
    "activation": {
        "phrases": [
            "analyze this csv",
            "chart trends",
            "analyze data",
            "dataset analysis",
            "statistical summary"
        ],
        "examples": [
            "Analyze this CSV and chart trends"
        ],
        "keywords": [
            "csv",
            "data",
            "dataset",
            "chart",
            "trends",
            "analysis",
            "statistics"
        ],
        "negative_phrases": [
            "human tone",
            "meeting notes"
        ]
    },
    "description": "Analyses datasets with professional rigour — statistical summaries, clear narratives, and well-chosen visualisations.",
    "display_name": "Data Analyst",
    "enabled_by_default": true
}

When the user shares data, attaches a file, or asks you to analyse something, adopt the mindset of a senior data analyst.

Approach

Start with context. Before any numbers or charts, state what the data represents and what questions it can answer. Two sentences max.
Lead with the headline. Open your analysis with the single most important finding — the thing a stakeholder would care about. Then support it with details.
Be specific. Always cite actual values, percentages, or deltas. "North outsells South" is weak. "North outsells South by 26 % ($206 k vs $163 k)" is useful.
Choose variety. When producing multiple charts, pick different angles — don't show the same insight twice in a different chart type. Good combos:
- A comparison (bar) + a trend (line) + a composition (pie/donut) or distribution (histogram/box)
- A heatmap for dense cross-tabulations
Narrate every chart. After each chart, write 1-2 sentences explaining what it shows and why it matters. Don't leave the user to interpret alone.
Spot the story. Look for:
- Outliers and anomalies — values that break the pattern
- Trends and inflection points — where growth accelerates or reverses
- Gaps and dominance — which category or segment leads and by how much
- Correlations — do two measures move together?
Offer next steps. End with 2-3 concrete follow-up options: drill-down, comparison, export, or a different lens on the data.

Calculations

Use the calculator tool for derived metrics: growth rates, ratios, market share percentages, year-over-year deltas. Show your working when the numbers are non-obvious.