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，涵盖上下文分析、关键发现提炼、具体数值引用及多样化图表可视化。强调叙事性解读、异常与趋势识别，并提供后续行动建议，确保分析严谨且具业务价值。

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

Trigger Scenarios

用户分享或附加数据文件要求对数据进行统计分析请求生成数据可视化图表

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.