Excel Data Import

Import, merge, and transform data from Excel (.xlsx/.csv) files using YAML-driven configuration. Use when the user asks to: (1) import data from Excel/CSV in...

Security Scan

The package's files, documentation, and runtime instructions are coherent with an Excel/CSV import and mapping tool; nothing requested or described is disproportionate to that purpose.

Purpose & Capability

Name, description, SKILL.md, examples, and included Python scripts all align: this is an Excel/CSV import, mapping, validation, and batch-processing utility. No unrelated credentials or binaries are requested.

Instruction Scope

Runtime instructions focus on reading local Excel/CSV files, producing outputs/backups, and running the provided scripts (e.g., python3 scripts/excel_import.py). A few small inconsistencies appear in docs (e.g., 'uv add' in quickstart, reference to excel_import_enhanced.py in auto_header_detection.md) which may be stale/documentation errors but do not change the overall scope. The tool performs file I/O, directory traversal, backups and report generation as expected for its purpose.

Install Mechanism

No install spec; the bundle contains Python scripts and documentation that instruct users to pip-install openpyxl and pyyaml (and optionally python-calamine). No network-download/third-party install URLs are embedded in the skill metadata. The lack of an automatic install step means the user/host environment will install Python deps before execution.

Credentials

The skill declares no required environment variables or credentials. Some reference docs mention optional remote backup (S3) and encrypt-transform examples that would require user-supplied keys if used, but those are optional examples and not mandatory for normal operation.

Persistence & Privilege

Skill is not always-enabled and uses the normal model-invocation defaults. It does file reads/writes within user-specified paths (backups, logs, outputs) but does not request system-wide privileges or modify other skills' configurations.

Assessment

This skill appears to be what it claims: a local Python-based Excel/CSV import and mapping toolkit. Before running it: (1) review the included Python scripts to ensure they only access the files/paths you expect, (2) install dependencies in a controlled environment (virtualenv), (3) run with --dry-run and small test data first, (4) inspect any backup/remote-backup settings before enabling remote uploads (you must provide cloud credentials yourself), and (5) note minor documentation inconsistencies (typo 'uv add', a reference to excel_import_enhanced.py) that suggest verifying the exact script names/paths in the bundle. If you will run this on sensitive data, audit the code for any network calls or unexpected logging of sensitive fields before use.

Configuration-driven data import from Excel and CSV files with field mapping, validation, and batch processing.

Prerequisites

• Python 3.8+
• Required: pip3 install openpyxl pyyaml
• Optional: pip3 install python-calamine (for .xls legacy format)

Quick Start

# import_config.yaml
task_name: "人员信息导入"
source:
  file_path: "data/source.xlsx"
  sheet_name: "Sheet1"
  header_row: 1
  key_field: "身份证号"
target:
  file_path: "output/result.xlsx"
  sheet_name: "人员信息"
  header_row: 2
  data_start_row: 3
field_mappings:
  - source: "姓名"
    target: "员工姓名"
    required: true
  - source: "身份证号"
    target: "身份证号码"
    required: true
    validate: "id_card"
  - source: "部门"
    target: "所属部门"
    default: "待分配"
error_handling:
  backup: true

python3 scripts/excel_import.py import_config.yaml
python3 scripts/excel_import.py import_config.yaml --dry-run   # preview only

Import Modes

Mode	Source Config	Use Case
Single file	source.file_path	One-to-one import
Directory batch	source.type: "directory"	Process all files in a folder
Multi-source	sources: [...]	Merge from multiple files
CSV	.csv file_path	Auto-encoding detection (UTF-8/GBK/GB2312)
Legacy .xls	.xls file_path	Requires python-calamine
Auto header	header_row: "auto"	Detect header in complex sheets

For full parameter docs, see data-mapping-guide.md.

Key Features

• Incremental update: Match by key_field, update existing or append new rows
• Multi-layer merged headers: Auto-detect and expand merged cell values
• Validation rollback: Failed rows are skipped entirely (no partial writes)
• Source deduplication: Duplicate keys across files are merged
• Auto-create target: Template generated from field_mappings if missing

Built-in Transforms & Validators

Transforms: strip, upper, lower, title, int, float, date

Validators: required, not_empty, id_card, phone, email, numeric, range, regex, length

For advanced usage, see advanced-features.md.

CLI Options

Option	Description
--dry-run	Preview mode, no file writes
--verbose	Detailed per-record output
--no-backup	Skip target file backup

Reference Documents

• Data Mapping Guide: data-mapping-guide.md — field mapping, transforms, validators
• Advanced Features: advanced-features.md — multi-source, batch, auto-header
• Auto Header Detection: auto_header_detection.md — complex header detection
• Quickstart: quickstart.md — step-by-step tutorial
• Workflow: workflow.md — detailed execution flow
• Best Practices: best_practices.md — usage recommendations
• Error Handling: error-handling.md — error codes and recovery
• Troubleshooting: troubleshooting.md — common issues

Workflow

1. Read user's import requirements and source/target file info
2. Create or adjust YAML config file
3. Run python3 scripts/excel_import.py <config.yaml> with --dry-run first
4. Review output, fix issues, then run without --dry-run
5. Check the JSON report alongside the output file