AgentSkillsCN

report-expert

为 Frappe 报表提供专业指导,涵盖报表类型、结构、创建流程以及最佳实践。当您需要创建标准脚本报表、查询报表,理解报表结构,处理列与筛选器,或排查与报表相关的问题时,请使用此技能。

SKILL.md
--- frontmatter
name: report-expert
description: Expert guidance on Frappe reports including report types, structure, creation workflow, and best practices. Use when creating standard script reports, query reports, understanding report structure, working with columns and filters, or troubleshooting report-related issues.

Frappe Report Expert

This skill provides comprehensive guidance for working with Frappe reports, their structure, creation workflow, and best practices.

Overview

Frappe provides a powerful reporting framework with multiple report types for different use cases:

  • Report Builder: Visual report builder without code (uses DocType fields)
  • Query Report: SQL-based reports with direct database queries
  • Script Report: Python-based reports with full programmatic control (most flexible)
  • Custom Report: Customized version of an existing report

This skill focuses primarily on Script Reports as they are the most commonly created programmatically and offer the most flexibility.

Quick Reference

Report Types Comparison

Report TypeCode RequiredUse CaseFlexibility
Report BuilderNoneSimple reports from DocType fieldsLow
Query ReportSQL onlyDatabase-driven reports, joinsMedium
Script ReportPython + JSComplex logic, calculations, custom dataHigh
Custom ReportNoneSaved customization of existing reportN/A

Standard Script Report Structure

Every Script Report consists of three files:

code
{module}/report/{report_name}/
├── __init__.py (empty)
├── {report_name}.json (metadata)
├── {report_name}.py (execute function)
└── {report_name}.js (filters and client-side logic)

Core Concepts

The Execute Function

The Python file must contain an execute(filters=None) function:

python
def execute(filters=None):
    columns, data = [], []
    # Your logic here
    return columns, data

Return value:

  • Returns a tuple: (columns, data) or extended (columns, data, message, chart, report_summary, skip_total_row)
  • columns: List of column definitions
  • data: List of rows (each row is a list or dict)

Column Format

Columns can be defined as strings or dictionaries:

String format:

python
columns = [
    "Name:Data:150",
    "Amount:Currency:120",
    "Customer:Link/Customer:200"
]

Dictionary format:

python
columns = [
    {
        "label": "Name",
        "fieldname": "name",
        "fieldtype": "Data",
        "width": 150
    }
]

Common fieldtypes:

  • Text: Data, Small Text, Text, Long Text, Text Editor, HTML Editor, Markdown Editor, Code
  • Numeric: Int, Long Int, Float, Currency, Percent
  • Date/Time: Date, Datetime, Time, Duration
  • Relationships: Link (requires options), Dynamic Link
  • Boolean: Check
  • Special: Attach, Attach Image, Signature, Color, Barcode, Rating, Icon, Geolocation, Phone, Autocomplete, JSON, Password, Read Only

See references/column-fieldtypes.md for complete field type reference.

Data Format

Data rows can be lists or dictionaries:

List format (matches column order):

python
data = [
    ["ID-001", 1000, "Customer A"],
    ["ID-002", 2000, "Customer B"],
]

Dictionary format (uses fieldnames):

python
data = [
    {"name": "ID-001", "amount": 1000, "customer": "Customer A"},
    {"name": "ID-002", "amount": 2000, "customer": "Customer B"},
]

Filters

Filters are defined in the JavaScript file:

javascript
frappe.query_reports["Report Name"] = {
    filters: [
        {
            fieldname: "company",
            label: __("Company"),
            fieldtype: "Link",
            options: "Company",
            reqd: 1
        },
        {
            fieldname: "from_date",
            label: __("From Date"),
            fieldtype: "Date",
            default: frappe.datetime.add_months(frappe.datetime.get_today(), -1)
        }
    ]
};

See references/filter-fieldtypes.md for complete filter reference.

Creating a Script Report

Quick Start

  1. Create Report via Desk: Navigate to Report DocType, create new with type "Script Report"
  2. Implement Execute Function: Edit the generated .py file with your logic
  3. Define Filters: Edit the generated .js file with filter definitions
  4. Test: Run bench migrate and navigate to /app/query-report/Your Report

See references/report-creation-workflow.md for detailed step-by-step guide.

Common Use Cases

Simple List Report

python
def execute(filters=None):
    return get_columns(), frappe.get_list(
        "DocType",
        fields=["name", "status", "amount"],
        filters=filters
    )

def get_columns():
    return [
        "Name:Link/DocType:150",
        "Status:Data:100",
        "Amount:Currency:120"
    ]

Report with Calculations

Add calculated fields in Python before returning data.

Grouped/Hierarchical Reports

Use indent field and return tree structure flag.

See references/script-report-examples.md for complete working examples.

Advanced Features

  • Charts: Return chart configuration as 4th element
  • Report Summary: Return summary metrics as 5th element
  • Custom Buttons: Add buttons in JavaScript onload function
  • Tree View: Enable hierarchical display with indent levels
  • Permissions: Use frappe.only_for() or frappe.has_permission()
  • Performance: Cache results, use proper indexing, limit data

See references/advanced-features.md for detailed documentation.

Reference Files

For detailed information on specific topics:

Best Practices

Code Organization:

  • Use helper functions: get_columns(), get_data(), get_conditions()
  • Handle None filters: Check filters.get(key) not filters[key]

Performance:

  • Filter early in SQL WHERE clause
  • Use proper database indexes
  • Add LIMIT for large datasets
  • Cache expensive operations

Security:

  • Validate permissions with frappe.only_for() or frappe.has_permission()
  • Sanitize inputs in SQL queries
  • Use parameterized queries

User Experience:

  • Provide sensible default filter values
  • Use appropriate column widths
  • Add translations with _() or __()
  • Sort data logically

Troubleshooting

Report not showing up:

  • Check if report is disabled
  • Verify user has required role
  • Run bench migrate to sync

Data not displaying correctly:

  • Verify column count matches data row length
  • Check fieldtype matches data type

Filter not working:

  • Check fieldname matches in JS and Python
  • Verify filter value is passed correctly

See documentation for common issues and solutions.