smart-charts（智能图表）

Smart Charts

An intelligent chart-generation and data-analysis skill. It reads user-supplied data files, understands their structure and semantics, recommends the most appropriate chart types, generates interactive HTML reports powered by ECharts, and (when a saved report template exists) fills the template automatically.

Installation / 安装

pip install -r requirements.txt

Dependencies / 依赖

ECharts is loaded via CDN (jsdelivr) — no local installation required.

Activation Triggers

Load and run this skill when any of the following conditions are met:

• The user mentions: "analyze data", "generate chart", "data visualization", "chart", "visualization"

/ 用户提到：「分析数据」「生成图表」「数据可视化」「chart」「visualization」

• The user provides a data file (CSV / Excel / JSON / TXT) and asks for analysis or visualization

/ 用户上传或提供数据文件并要求分析或可视化

• The user asks to generate charts or a report from tabular data

/ 用户要求从表格数据生成图表或报告

User Guidance

When no data file is provided

Prompt the user:

Please upload the data file(s) you want to analyze. Supported formats: - CSV (.csv / .tsv / .txt) - Excel (.xlsx / .xls) - JSON (.json) You can drag files directly into the chat box. Multiple files are supported.

/ 请上传需要分析的数据文件。支持 CSV / Excel / JSON 格式，可同时上传多个文件。

When data files are provided

Step 1 — Parse and display a unified summary:

Files loaded: 3 | File | Rows | Cols | Key Fields | |------|------|------|------------| | east_sales.csv | 120 | 8 | date, revenue, profit… | | south_sales.csv | 98 | 8 | date, revenue, profit… | | products.xlsx | 45 | 5 | name, category, price… |

Step 2 — Infer file relationships and recommend an analysis strategy:

Step 3 — Execute after user confirmation.

Error handling

Execution Workflow

1. Obtain data file(s)
   └─ User uploads file(s) directly (primary method)
   └─ Or user provides file path(s)

2. Parse data
   └─ Call data_parser.py on all files
   └─ Single file  → parse directly
   └─ Multiple files → parse each, assess merge feasibility

3. Confirm & recommend
   └─ Display a summary table for all files
   └─ Recommend: merge / separate / join
   └─ Recommend chart type(s) based on data characteristics

4. Generate charts
   └─ Call chart_generator.py → produces ECharts HTML
   └─ Merged data  → cross-group comparison charts
   └─ Separate data → independent charts per file
   └─ Chart type is chosen by the LLM based on data shape

5. Check for a report template
   └─ Scan the templates/ subdirectory under the skill base
   └─ Read each meta.json; let the LLM judge relevance
   └─ No matching template → skip to free-form generation

6. Generate analysis report
   └─ Matching template found → fill template.md with data insights
   └─ No matching template    → LLM generates report freely

7. Present results
   └─ Interactive charts: use preview_url (HTML)
   └─ Markdown report:    use open_result_view

Configuration

output_dir:    output directory (optional; default: ./smart_charts_output)
templates_dir: report template directory (optional; default: ./templates)

Important: Never hard-code absolute paths. All paths must be provided by the user or resolved dynamically from the working directory.

Data Parsing — CLI Reference

Usage / 调用方式

# Single file / 单文件
python {skill_base}/core/data_parser.py <file_path> [--summary]

# Multiple files / 多文件
python {skill_base}/core/data_parser.py <file1> <file2> ... [--summary]

# Multiple files with auto-merge / 多文件自动合并
python {skill_base}/core/data_parser.py <file1> <file2> ... [--merge] [--summary]

Merge behavior:

Supported formats

Chart Generation — CLI Reference

Usage / 调用方式

python {skill_base}/core/chart_generator.py \
  <file_path> <chart_type> \
  --title "Chart Title" \
  --x-axis "date" \
  --y-axis "revenue profit" \
  --output-dir "./output"

Parameters

Supported chart types

Report Templates

Users can store custom report templates under the templates_dir directory.

Directory structure

templates/
├── _template_index.json         # Auto-generated metadata index
├── sales_report/
│   ├── meta.json                # Template metadata card
│   ├── template.md              # Template content
│   └── original.docx            # Source file (optional)
└── project_progress/
    ├── meta.json
    └── template.md

meta.json schema

{
  "id": "tmpl_sales_monthly",
  "name": "Monthly Sales Report",
  "description": "For monthly sales summaries: revenue trend, top products, regional breakdown.",
  "scenarios": ["monthly sales report", "sales performance review", "quarterly comparison"],
  "variables": ["period", "revenue", "profit", "order_count", "mom_growth", "yoy_growth"],
  "categories": ["sales", "finance", "business analysis"],
  "format": "markdown",
  "created_time": "2026-03-26T10:30:00",
  "modified_time": "2026-03-26T10:30:00"
}

LLM-driven template matching

Core principle: template matching is performed by the LLM, not by hard-coded algorithms.

Step 1 — Discover templates and collect metadata

template_summary = template_manager.get_all_templates_summary()

Step 2 — LLM analyzes the user task

User task: "Analyze this month's sales data and generate a report."
LLM reasoning:
  - Keywords: sales, data, report, monthly
  - Task type: data analysis, report generation
  - Data characteristics: sales metrics, time series, KPIs

Step 3 — LLM selects the best-matching template

Templates available:
  tmpl_sales_monthly    → Monthly Sales Report
  tmpl_financial_report → Financial Report
  tmpl_project_progress → Project Progress

Best match: tmpl_sales_monthly
Reason: scenario match (sales), variable overlap (revenue, profit…)

Step 4 — Load template and fill with data insights

template_content = template_manager.get_template_content("tmpl_sales_monthly")
filled_report = fill_template_variables(template_content, data_insights)

Fallback behavior (no match)

Template variable syntax (auto-detected)

Template Management

Supported template formats

Operations / 操作指令

Upload / Save a template

• Triggers: upload template, add template, save template

/ 触发词：上传模板、添加模板、保存模板

User: Save this sales report as a template.
AI:   ✅ Template saved: "Sales Report" (Markdown, 8 variables detected)

View template library

• Triggers: my templates, template list, show templates

/ 触发词：我的模板、模板列表、查看模板

User: Show my templates.
AI:   📋 Your templates (3):
      1. Monthly Sales Report (Markdown) — monthly sales analysis
      2. Project Progress (Word) — project tracking
      3. Financial Report (PDF) — financial analysis

Auto-matching (seamless)

User: Analyze this month's sales data.
AI:   🎯 Matched template: "Monthly Sales Report"
      📊 Auto-filling variables: revenue, profit, growth rate
      📄 Generating professional report…

Template management error handling

Key Principles

1. Multi-file first — Users often upload multiple files. Guide proactively; handle batches gracefully.
2. Confirm before executing — Always show a data summary and confirm understanding before recommending analysis direction.
3. LLM chooses chart types — Recommend based on data semantics; never hard-code mapping rules.
4. Template-first report generation — Use a saved template when a good match exists; fall back to free-form only when necessary.
5. Dynamic path resolution — Absolute paths must never be hard-coded; resolve all paths at runtime.
6. Immediate result presentation — Charts via preview_url; Markdown reports via open_result_view.