> Use when building Query Reports, Script Reports, or configuring Report Builder, including chart data integration. Prevents report errors from wrong column definitions, missing permissions, and incorrect data formatting. Covers Query Report (SQL-based), Script Report (Python-based), Report Builder, report columns definition, filters, chart_data, report permissions, prepared_report.
npx skills add https://github.com/Impertio-Studio/Frappe_Claude_Skill_Package --skill frappe-syntax-reports
| Type | Code Required | Use Case | Permission |
|------|--------------|----------|------------|
| Report Builder | None | Simple single-DocType listing with filters, group by | Any user |
| Query Report | SQL only | Direct SQL queries, legacy column format | System Manager |
| Script Report (Standard) | Python + JS | Complex logic, charts, summaries, trees | Administrator + Developer Mode |
| Script Report (Custom) | Python in UI | Quick custom reports without app deployment | System Manager |
def execute(filters=None):
columns = [...] # List of dicts
data = [...] # List of dicts or lists
message = "..." # Optional: HTML message above report
chart = {...} # Optional: chart configuration
report_summary = [...] # Optional: summary cards
skip_total_row = False # Optional: suppress auto-total
return columns, data, message, chart, report_summary, skip_total_row
columns = [
{
"fieldname": "customer",
"label": _("Customer"),
"fieldtype": "Link",
"options": "Customer",
"width": 200
},
{
"fieldname": "amount",
"label": _("Amount"),
"fieldtype": "Currency",
"options": "currency", # field in row holding currency code
"width": 120
}
]
SELECT
name as "Sales Order:Link/Sales Order:200",
customer as "Customer:Link/Customer:180",
grand_total as "Total:Currency:120",
transaction_date as "Date:Date:100"
FROM `tabSales Order`
WHERE docstatus = 1
Format: "Label:Fieldtype/Options:Width" — Options only needed for Link, Dynamic Link, Currency.
frappe.query_reports["My Report"] = {
filters: [
{
fieldname: "company",
label: __("Company"),
fieldtype: "Link",
options: "Company",
default: frappe.defaults.get_user_default("company"),
reqd: 1
},
{
fieldname: "from_date",
label: __("From Date"),
fieldtype: "Date",
default: frappe.datetime.add_months(frappe.datetime.get_today(), -1)
},
{
fieldname: "status",
label: __("Status"),
fieldtype: "Select",
options: "\nDraft\nSubmitted\nCancelled"
}
]
};
Need a report?
├─ Simple list/group of one DocType → Report Builder
│ (no code, UI-only, supports Group By with Count/Sum/Avg)
├─ Direct SQL query, no Python logic needed → Query Report
│ (SQL in Report doc, column format in aliases)
├─ Complex logic, calculations, charts → Script Report (Standard)
│ (Python .py + JS .js files, requires Developer Mode)
└─ Quick one-off with Python but no app deploy → Script Report (Custom)
(Python in Report doc UI, System Manager can create)
Script Report returns what?
├─ Just data → return columns, data
├─ Data + chart → return columns, data, None, chart
├─ Data + summary → return columns, data, None, None, report_summary
├─ Data + message → return columns, data, message
└─ Everything → return columns, data, message, chart, report_summary, skip_total_row
| Fieldtype | Options Required | Notes |
|-----------|-----------------|-------|
| Data | No | Plain text |
| Link | DocType name | Clickable link to document |
| Dynamic Link | Fieldname holding DocType | Pair with a column containing DocType |
| Currency | Currency field or code | Fieldname in row that holds currency |
| Float | No | Decimal number |
| Int | No | Integer |
| Percent | No | Shows percentage bar |
| Date | No | Date display |
| Datetime | No | Date + time |
| Check | No | Boolean checkbox |
| Select | No | Dropdown value |
| Text | No | Long text |
| HTML | No | Raw HTML rendering |
| Fieldtype | Options | Behavior |
|-----------|---------|----------|
| Link | DocType name | Autocomplete from DocType |
| Select | Newline-separated values | Dropdown with fixed options |
| Date | — | Date picker |
| DateRange | — | Returns [from_date, to_date] list |
| Check | — | Boolean toggle |
| Dynamic Link | Fieldname of Link filter | Depends on another filter value |
| Data | — | Free text input |
| Int | — | Numeric input |
| MultiSelectList | DocType name | Multiple value selection |
chart = {
"data": {
"labels": ["Jan", "Feb", "Mar", "Apr"],
"datasets": [
{"name": _("Revenue"), "values": [100, 200, 150, 300]},
{"name": _("Expense"), "values": [80, 150, 120, 250]}
]
},
"type": "bar", # bar, line, pie, donut, percentage
"fieldtype": "Currency",
"options": "currency",
"currency": "USD",
"colors": ["#5e64ff", "#ffa00a"] # Optional custom colors
}
report_summary = [
{
"value": total_revenue,
"label": _("Total Revenue"),
"datatype": "Currency",
"currency": "USD",
"indicator": "Green" # Green, Blue, Orange, Red
},
{
"value": total_count,
"label": _("Total Orders"),
"datatype": "Int",
"indicator": "Blue"
}
]
For reports processing large datasets, enable Prepared Report to run asynchronously:
prepared_report = 1 in the Report documentenqueue()| Source Type | Required Fields | How It Works |
|-------------|----------------|--------------|
| Document Type | document_type, function, aggregate_function_based_on | SQL aggregate on DocType |
| Report | report_name, report_field, function | Pulls value from a report column |
| Custom Method | method | Calls a whitelisted Python method |
Custom method signature:
@frappe.whitelist()
def get_total_active_users(filters=None):
return frappe.db.count("User", {"enabled": 1})
| Source | Configuration | Data Format |
|--------|--------------|-------------|
| Report | Set chart_type = "Report", select report | Uses report's chart data |
| Custom | Set chart_type = "Custom", define source | Hook returns {"labels": [...], "datasets": [...]} |
| Group By | Set chart_type = "Group By", pick field | Auto-aggregates by field |
Dashboard Chart Source hook in hooks.py:
dashboard_chart_source = [
"myapp.dashboard_chart_source.get_chart_data"
]
fieldname, label, fieldtype. The legacy string format is ONLY for Query Report SQL aliases.None for columns or data in execute() — ALWAYS return empty lists []._(...) for translatable labels in columns and report_summary.frappe.db.sql with user-supplied filter values directly in f-strings — ALWAYS pass as parameters: frappe.db.sql(query, filters, as_dict=True).Reference DocType on the Report document — it controls user access permissions.width in column definitions — columns without width render poorly.datasets[].values length to labels length in chart data — mismatched lengths cause chart rendering errors.Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.
MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.
UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.
Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
Take impertio-studio/frappe-syntax-reports from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.