Extract text, tables, and images from PDFs. Use when: extracting data from reports; converting PDF tables to CSV; pulling images from presentations; processing research papers; batch converting PDFs to text
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Extract text, tables, and images from PDF files using pdfplumber - turn static PDFs into usable data.
| Claude Does | You Decide |
|---|---|
| Structures analysis frameworks | Metric definitions |
| Identifies patterns in data | Business interpretation |
| Creates visualization templates | Dashboard design |
| Suggests optimization areas | Action priorities |
| Calculates statistical measures | Decision thresholds |
pip install pdfplumber pypdf click pandas
# For image extraction:
pip install Pillow
python scripts/main.py text document.pdf
python scripts/main.py text document.pdf --pages 1-5
python scripts/main.py tables report.pdf --output tables.csv
python scripts/main.py tables financial.pdf --page 3
python scripts/main.py images presentation.pdf --output ./images/
python scripts/main.py merge doc1.pdf doc2.pdf --output combined.pdf
python scripts/main.py info document.pdf
python scripts/main.py tables annual-report.pdf --output financials.csv
# Output: financials.csv with all tables found
# Also creates individual CSVs: table_page3_1.csv, table_page5_1.csv
python scripts/main.py batch ./pdfs/ --output ./text/
# Converts all PDFs in folder to .txt files
python scripts/main.py text whitepaper.pdf --pages 1,5-10,15
# Extracts only pages 1, 5-10, and 15
category: automation
subcategory: document-processing
dependencies: [pdfplumber, pypdf, pandas]
difficulty: beginner
time_saved: 4+ hours/week
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Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
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Assess customer account health using product usage, support sentiment, payment status, and relationship signals
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Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
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Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
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Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
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