data-processor

name	data-processor
description	Transform, filter, and analyse data using sandbox handlers
triggers	["CSV","JSON","TSV","CSV to JSON","JSON to CSV","data pipeline","ETL","tabular data","parse CSV","parse JSON","transform CSV","transform JSON"]
patterns	["data-transformation","two-handler-pipeline"]
antiPatterns	["Don't load entire datasets into handler source code — pass via event parameter","Don't process everything in one giant handler call — chunk large datasets","Don't use string manipulation for structured data — parse properly first"]
allowed-tools	["register_handler","execute_javascript","execute_bash","delete_handler","get_handler_source","edit_handler","list_handlers","reset_sandbox","list_modules","module_info","list_plugins","plugin_info","manage_plugin","list_mcp_servers","mcp_server_info","manage_mcp","apply_profile","configure_sandbox","sandbox_help","register_module","write_output","read_input","read_output","ask_user"]

Data Processing Guidance

Use the event dispatch pattern for multi-step processing:

register_handler('processor', `
  export function handler(event) {
    if (event.action === 'load') { /* parse and store */ }
    if (event.action === 'transform') { /* filter/map/reduce */ }
    if (event.action === 'export') { /* format output */ }
  }
`)

For large datasets:

• Pass data in chunks via event parameter
• Accumulate results in module-level state
• Use ha:shared-state if results need to survive recompilation

For file I/O:

• Use write_output(path, content) for text output (CSV, JSON, Markdown) — no sandbox needed
• Use read_input(path) for reading text input files — no sandbox needed
• Use fs-write plugin in sandbox only for binary output
• Enable file-builder profile for generous limits on large datasets

Available modules:

• ha:str-bytes for string↔bytes conversion
• ha:base64 for encoding/decoding
• ha:markdown for Markdown generation
• ha:xlsx for Excel workbook generation from processed tabular data