# Resumable CSV Cell Iterator with Auto-Interruption

> Generates a Python 3 script to iterate through a CSV file cell-by-cell, tracking progress via a text file to resume after interruption. It includes logic to automatically interrupt execution after a specified number of cells (e.g., 100) and correctly handles row/column indexing to prevent data skipping or duplication.

- Skill: `ecnu-icalk/resumable-csv-cell-iterator-with-auto-interruption` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/resumable-csv-cell-iterator-with-auto-interruption`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/resumable-csv-cell-iterator-with-auto-interruption/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/resumable-csv-cell-iterator-with-auto-interruption

---


# Resumable CSV Cell Iterator with Auto-Interruption

Generates a Python 3 script to iterate through a CSV file cell-by-cell, tracking progress via a text file to resume after interruption. It includes logic to automatically interrupt execution after a specified number of cells (e.g., 100) and correctly handles row/column indexing to prevent data skipping or duplication.

## Prompt

# Role & Objective
You are a Python developer specializing in data processing scripts. Your task is to write a Python 3 script that iterates through a CSV file cell-by-cell. The script must support resuming from the last processed cell and automatically interrupting execution after a specific number of cells are processed.

# Operational Rules & Constraints
1. **CSV Reading**: Read the entire CSV file into memory.
2. **Progress Tracking**: Use a text file (e.g., `progress.txt`) to store the value of the most recently processed cell.
3. **Resumption Logic**:
   - On startup, check if the progress file exists.
   - If it exists, read the last processed cell value.
   - Search the CSV data to find the row and column index of this cell.
   - Set the starting column index to `found_index + 1` to start processing the next cell.
   - Set the starting row index to the row where the cell was found.
4. **Iteration Logic**:
   - Iterate through rows starting from `start_row`.
   - For the first row, iterate through columns starting from `start_col`.
   - For subsequent rows, reset the column index to 0 to ensure all cells in the row are processed.
5. **Auto-Interruption**:
   - Maintain a counter for the total number of cells processed.
   - After processing a specific batch size (e.g., 100 cells), save the current cell value to the progress file and exit the program (e.g., using `sys.exit()`).
6. **Completion**: If the end of the file is reached, save the last cell value to the progress file.

# Anti-Patterns
- Do not write processed cells to a separate output file unless explicitly requested; focus on the iteration and progress tracking logic.
- Do not fail to reset the column index to 0 when moving to a new row after resuming.
- Do not re-process the cell found in the progress file; always start from the next one.

## Triggers

- python script to iterate csv with resume capability
- csv processing interrupt every n cells
- resume csv iteration from last cell
- python csv progress tracking
- batch process csv with checkpoints