Awesome-omni-skill annotating-csv

Annotates CSV rows using OpenAI by applying a prompt to each row and adding a new column with AI-generated results. Use when the user wants to classify, extract, summarize, or add AI-generated annotations to CSV data. Triggers on mentions of CSV annotation, row-by-row AI processing, bulk classification, or adding AI columns to spreadsheet data.

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/ai-agents/annotating-csv" ~/.claude/skills/diegosouzapw-awesome-omni-skill-annotating-csv && rm -rf "$T"

manifest: skills/ai-agents/annotating-csv/SKILL.md

safety · automated scan (medium risk)

This is a pattern-based risk scan, not a security review. Our crawler flagged:

pip install
references .env files

Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.

source content

CSV Annotation with OpenAI

Adds AI-generated annotations to CSV files by processing each row with a prompt.

Quick Start

uv run annotate-csv.py "Classify sentiment as positive/negative/neutral" reviews.csv

Output:

reviews_annotated.csv

with new

annotation

column.

Dependencies install automatically on first run.

Core Usage

uv run annotate-csv.py <prompt> <csv_file> [options]

Option	Description	Default
`-o, --output`	Output file path	`<input>_annotated.csv`
`-c, --column`	Annotation column name	`annotation`
`--context`	Columns to use ( `all` or comma-separated)	`all`
`--model`	OpenAI model	`gpt-5-mini`
`--parallelism`	Parallel workers	`10`
`--id-column`	Column for progress display	-

Prompt Sources

Prompts can be inline or from a file:

# Inline prompt
python annotate-csv.py "Extract the main topic" data.csv

# From file (for complex prompts)
python annotate-csv.py prompt.txt data.csv

Common Patterns

Classification:

python annotate-csv.py "Classify sentiment: positive, negative, or neutral" feedback.csv -c sentiment

Extraction:

python annotate-csv.py "Extract the product name mentioned" reviews.csv -c product --context "review_text"

Summarization:

python annotate-csv.py "Summarize in one sentence" articles.csv -c summary

See EXAMPLES.md for more patterns and prompt templates.

Environment Setup

Requires

OPENAI_API_KEY

.env

file:

OPENAI_API_KEY=sk-...

Run with uv (recommended):

uv run annotate-csv.py "Your prompt" data.csv

Or install dependencies first:

uv pip install -r requirements.txt
python annotate-csv.py "Your prompt" data.csv

Workflow

Preview data: Check CSV structure and columns
Choose context: Decide which columns the AI needs to see
Write prompt: Be specific about expected output format
Test on subset: Try on a few rows first if large dataset
Run full annotation: Process the complete file
Validate results: Spot-check annotation quality

Writing Effective Prompts

Be explicit about output format (single word, phrase, sentence)
List valid categories for classification tasks
Specify what to do with ambiguous cases
Keep prompts focused on one task

See PROMPTS.md for prompt-writing guidance.

Troubleshooting

Issue	Solution
API key error	Check `.env` file has `OPENAI_API_KEY`
Column not found	Verify column names match CSV exactly
Rate limits	Reduce `--parallelism` to 5 or lower
Empty annotations	Check context columns have data