ベストプラクティス

## Extract PDF text

Use pdfplumber for text extraction:
```python
import pdfplumber

with pdfplumber.open("file.pdf") as pdf:
    text = pdf.pages[0].extract_text()
```

## Extract PDF text

PDF (Portable Document Format) files are a common file format that contains
text, images, and other content. To extract text from a PDF, you'll need to
use a library. There are many libraries available for PDF processing, but we
recommend pdfplumber because it's easy to use and handles most cases well.
First, you'll need to install it using pip. Then you can use the code below...

## Code review process

1. Analyze the code structure and organization
2. Check for potential bugs or edge cases
3. Suggest improvements for readability and maintainability
4. Verify adherence to project conventions

## Generate report

Use this template and customize as needed:
```python
def generate_report(data, format="markdown", include_charts=True):
    # Process data
    # Generate output in specified format
    # Optionally include visualizations
```

## Database migration

Run exactly this script:
```bash
python scripts/migrate.py --verify --backup
```

Do not modify the command or add additional flags.

description: Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.

description: Analyze Excel spreadsheets, create pivot tables, generate charts. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.

description: Generate descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.

description: Helps with documents

description: Processes data

description: Does stuff with files

pdf/
|-- SKILL.md              # Main instructions (loaded when triggered)
|-- FORMS.md              # Form-filling guide (loaded as needed)
|-- reference.md          # API reference (loaded as needed)
|-- examples.md           # Usage examples (loaded as needed)
`-- scripts/
  |-- analyze_form.py   # Utility script (executed, not loaded)
  |-- fill_form.py      # Form filling script
  `-- validate.py       # Validation script

---
name: PDF Processing
description: Extracts text and tables from PDF files, fills forms, and merges documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
---

# PDF Processing

## Quick start

Extract text with pdfplumber:
```python
import pdfplumber
with pdfplumber.open("file.pdf") as pdf:
    text = pdf.pages[0].extract_text()
```

## Advanced features

**Form filling**: See [FORMS.md](FORMS.md) for complete guide
**API reference**: See [REFERENCE.md](REFERENCE.md) for all methods
**Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns

bigquery-skill/
|-- SKILL.md (overview and navigation)
`-- reference/
  |-- finance.md (revenue, billing metrics)
  |-- sales.md (opportunities, pipeline)
  |-- product.md (API usage, features)
  `-- marketing.md (campaigns, attribution)

# BigQuery Data Analysis

## Available datasets

**Finance**: Revenue, ARR, billing -> See [reference/finance.md](reference/finance.md)
**Sales**: Opportunities, pipeline, accounts -> See [reference/sales.md](reference/sales.md)
**Product**: API usage, features, adoption -> See [reference/product.md](reference/product.md)
**Marketing**: Campaigns, attribution, email -> See [reference/marketing.md](reference/marketing.md)

## Quick search

Find specific metrics using grep:
```bash
grep -i "revenue" reference/finance.md
grep -i "pipeline" reference/sales.md
grep -i "api usage" reference/product.md
```

# DOCX Processing

## Creating documents

Use docx-js for new documents. See [DOCX-JS.md](DOCX-JS.md).

## Editing documents

For simple edits, modify the XML directly.

**For tracked changes**: See [REDLINING.md](REDLINING.md)
**For OOXML details**: See [OOXML.md](OOXML.md)

# SKILL.md
See [advanced.md](advanced.md)...

# advanced.md
See [details.md](details.md)...

# details.md
Here's the actual information...

# SKILL.md

**Basic usage**: [instructions in SKILL.md]
**Advanced features**: See [advanced.md](advanced.md)
**API reference**: See [reference.md](reference.md)
**Examples**: See [examples.md](examples.md)

# API Reference

## Contents
- Authentication and setup
- Core methods (create, read, update, delete)
- Advanced features (batch operations, webhooks)
- Error handling patterns
- Code examples

## Authentication and setup
...

## Core methods
...

## Research synthesis workflow

Copy this checklist and track your progress:
```
Research Progress:
- [ ] Step 1: Read all source documents
- [ ] Step 2: Identify key themes
- [ ] Step 3: Cross-reference claims
- [ ] Step 4: Create structured summary
- [ ] Step 5: Verify citations
```

**Step 1: Read all source documents**

Review each document in the `sources/` directory. Note the main arguments and supporting evidence.

**Step 2: Identify key themes**

Look for patterns across sources. What themes appear repeatedly? Where do sources agree or disagree?

**Step 3: Cross-reference claims**

For each major claim, verify it appears in the source material. Note which source supports each point.

**Step 4: Create structured summary**

Organize findings by theme. Include:
- Main claim
- Supporting evidence from sources
- Conflicting viewpoints (if any)

**Step 5: Verify citations**

Check that every claim references the correct source document. If citations are incomplete, return to Step 3.

## PDF form filling workflow

Copy this checklist and check off items as you complete them:
```
Task Progress:
- [ ] Step 1: Analyze the form (run analyze_form.py)
- [ ] Step 2: Create field mapping (edit fields.json)
- [ ] Step 3: Validate mapping (run validate_fields.py)
- [ ] Step 4: Fill the form (run fill_form.py)
- [ ] Step 5: Verify output (run verify_output.py)
```

**Step 1: Analyze the form**

Run: `python scripts/analyze_form.py input.pdf`

This extracts form fields and their locations, saving to `fields.json`.

**Step 2: Create field mapping**

Edit `fields.json` to add values for each field.

**Step 3: Validate mapping**

Run: `python scripts/validate_fields.py fields.json`

Fix any validation errors before continuing.

**Step 4: Fill the form**

Run: `python scripts/fill_form.py input.pdf fields.json output.pdf`

**Step 5: Verify output**

Run: `python scripts/verify_output.py output.pdf`

If verification fails, return to Step 2.

## Content review process

1. Draft your content following the guidelines in STYLE_GUIDE.md
2. Review against the checklist:
 - Check terminology consistency
 - Verify examples follow the standard format
 - Confirm all required sections are present
3. If issues found:
 - Note each issue with specific section reference
 - Revise the content
 - Review the checklist again
4. Only proceed when all requirements are met
5. Finalize and save the document

## Document editing process

1. Make your edits to `word/document.xml`
2. **Validate immediately**: `python ooxml/scripts/validate.py unpacked_dir/`
3. If validation fails:
 - Review the error message carefully
 - Fix the issues in the XML
 - Run validation again
4. **Only proceed when validation passes**
5. Rebuild: `python ooxml/scripts/pack.py unpacked_dir/ output.docx`
6. Test the output document

If you're doing this before August 2025, use the old API.
After August 2025, use the new API.

## Current method

Use the v2 API endpoint: `api.example.com/v2/messages`

## Old patterns

<details>
<summary>Legacy v1 API (deprecated 2025-08)</summary>

The v1 API used: `api.example.com/v1/messages`

This endpoint is no longer supported.
</details>

## Report structure

ALWAYS use this exact template structure:
```markdown
# [Analysis Title]

## Executive summary
[One-paragraph overview of key findings]

## Key findings
- Finding 1 with supporting data
- Finding 2 with supporting data
- Finding 3 with supporting data

## Recommendations
1. Specific actionable recommendation
2. Specific actionable recommendation
```

## Report structure

Here is a sensible default format, but use your best judgment based on the analysis:
```markdown
# [Analysis Title]

## Executive summary
[Overview]

## Key findings
[Adapt sections based on what you discover]

## Recommendations
[Tailor to the specific context]
```

Adjust sections as needed for the specific analysis type.

## Commit message format

Generate commit messages following these examples:

**Example 1:**
Input: Added user authentication with JWT tokens
Output:
```
feat(auth): implement JWT-based authentication

Add login endpoint and token validation middleware
```

**Example 2:**
Input: Fixed bug where dates displayed incorrectly in reports
Output:
```
fix(reports): correct date formatting in timezone conversion

Use UTC timestamps consistently across report generation
```

**Example 3:**
Input: Updated dependencies and refactored error handling
Output:
```
chore: update dependencies and refactor error handling

- Upgrade lodash to 4.17.21
- Standardize error response format across endpoints
```

Follow this style: type(scope): brief description, then detailed explanation.

## Document modification workflow

1. Determine the modification type:

 **Creating new content?** -> Follow "Creation workflow" below
 **Editing existing content?** -> Follow "Editing workflow" below

2. Creation workflow:
 - Use docx-js library
 - Build document from scratch
 - Export to .docx format

3. Editing workflow:
 - Unpack existing document
 - Modify XML directly
 - Validate after each change
 - Repack when complete

{
"skills": ["pdf-processing"],
"query": "Extract all text from this PDF file and save it to output.txt",
"files": ["test-files/document.pdf"],
"expected_behavior": [
  "Successfully reads the PDF file using an appropriate PDF processing library or command-line tool",
  "Extracts text content from all pages in the document without missing any pages",
  "Saves the extracted text to a file named output.txt in a clear, readable format"
]
}

**Bad example: Too many choices** (confusing):
"You can use pypdf, or pdfplumber, or PyMuPDF, or pdf2image, or..."

**Good example: Provide a default** (with escape hatch):
"Use pdfplumber for text extraction:
```python
import pdfplumber
```

For scanned PDFs requiring OCR, use pdf2image with pytesseract instead."

def process_file(path):
  """Process a file, creating it if it doesn't exist."""
  try:
      with open(path) as f:
          return f.read()
  except FileNotFoundError:
      # Create file with default content instead of failing
      print(f"File {path} not found, creating default")
      with open(path, 'w') as f:
          f.write('')
      return ''
  except PermissionError:
      # Provide alternative instead of failing
      print(f"Cannot access {path}, using default")
      return ''

def process_file(path):
  # Just fail and let Claude figure it out
  return open(path).read()

# HTTP requests typically complete within 30 seconds
# Longer timeout accounts for slow connections
REQUEST_TIMEOUT = 30

# Three retries balances reliability vs speed
# Most intermittent failures resolve by the second retry
MAX_RETRIES = 3

TIMEOUT = 47  # Why 47?
RETRIES = 5   # Why 5?

## Utility scripts

**analyze_form.py**: Extract all form fields from PDF
```bash
python scripts/analyze_form.py input.pdf > fields.json
```

Output format:
```json
{
  "field_name": {"type": "text", "x": 100, "y": 200},
  "signature": {"type": "sig", "x": 150, "y": 500}
}
```

**validate_boxes.py**: Check for overlapping bounding boxes
```bash
python scripts/validate_boxes.py fields.json
# Returns: "OK" or lists conflicts
```

**fill_form.py**: Apply field values to PDF
```bash
python scripts/fill_form.py input.pdf fields.json output.pdf
```

## Form layout analysis

1. Convert PDF to images:
```bash
   python scripts/pdf_to_images.py form.pdf
```

2. Analyze each page image to identify form fields
3. Claude can see field locations and types visually

bigquery-skill/
|-- SKILL.md (overview, points to reference files)
`-- reference/
  |-- finance.md (revenue metrics)
  |-- sales.md (pipeline data)
  `-- product.md (usage analytics)

Use the BigQuery:bigquery_schema tool to retrieve table schemas.
Use the GitHub:create_issue tool to create issues.

**Bad example: Assumes installation**:
"Use the pdf library to process the file."

**Good example: Explicit about dependencies**:
"Install required package: `pip install pypdf`

Then use it:
```python
from pypdf import PdfReader
reader = PdfReader("file.pdf")
```