---
name: doc-pipeline-40aed30c6de0
description: Chain document operations into reusable pipelines
author: claude-office-skills
version: "1.0"
tags: ['pipeline', 'workflow', 'chain', 'automation']
models: [claude-sonnet-4, claude-opus-4]
tools: [computer, code_execution, file_operations]
library:
  name: Custom
  url: https://github.com/claude-office-skills/skills
  stars: N/A
---

# Doc Pipeline Skill

## Overview

This skill enables building document processing pipelines - chain multiple operations (extract, transform, convert) into reusable workflows with data flowing between stages.

## How to Use

1. Describe what you want to accomplish
2. Provide any required input data or files
3. I'll execute the appropriate operations

**Example prompts:**
- "PDF → Extract Text → Translate → Generate DOCX"
- "Image → OCR → Summarize → Create Report"
- "Excel → Analyze → Generate Charts → Create PPT"
- "Multiple inputs → Merge → Format → Output"

## Domain Knowledge


### Pipeline Architecture

```
Stage 1      Stage 2      Stage 3      Stage 4
┌──────┐    ┌──────┐    ┌──────┐    ┌──────┐
│Extract│ → │Transform│ → │ AI   │ → │Output│
│ PDF  │    │  Data  │    │Analyze│   │ DOCX │
└──────┘    └──────┘    └──────┘    └──────┘
     │           │           │           │
     └───────────┴───────────┴───────────┘
                 Data Flow
```

### Pipeline DSL (Domain Specific Language)

```yaml
# pipeline.yaml
name: contract-review-pipeline
description: Extract, analyze, and report on contracts

stages:
  - name: extract
    operation: pdf-extraction
    input: $input_file
    output: $extracted_text
    
  - name: analyze
    operation: ai-analyze
    input: $extracted_text
    prompt: "Review this contract for risks..."
    output: $analysis
    
  - name: report
    operation: docx-generation
    input: $analysis
    template: templates/review_report.docx
    output: $output_file
```

### Python Implementation

```python
from typing import Callable, Any
from dataclasses import dataclass

@dataclass
class Stage:
    name: str
    operation: Callable
    
class Pipeline:
    def __init__(self, name: str):
        self.name = name
        self.stages: list[Stage] = []
    
    def add_stage(self, name: str, operation: Callable):
        self.stages.append(Stage(name, operation))
        return self  # Fluent API
    
    def run(self, input_data: Any) -> Any:
        data = input_data
        for stage in self.stages:
            print(f"Running stage: {stage.name}")
            data = stage.operation(data)
        return data

# Example usage
pipeline = Pipeline("contract-review")
pipeline.add_stage("extract", extract_pdf_text)
pipeline.add_stage("analyze", analyze_with_ai)
pipeline.add_stage("generate", create_docx_report)

result = pipeline.run("/path/to/contract.pdf")
```

### Advanced: Conditional Pipelines

```python
class ConditionalPipeline(Pipeline):
    def add_conditional_stage(self, name: str, condition: Callable, 
                               if_true: Callable, if_false: Callable):
        def conditional_op(data):
            if condition(data):
                return if_true(data)
            return if_false(data)
        return self.add_stage(name, conditional_op)

# Usage
pipeline.add_conditional_stage(
    "ocr_if_needed",
    condition=lambda d: d.get("has_images"),
    if_true=run_ocr,
    if_false=lambda d: d
)
```


## Best Practices

1. **Keep stages focused (single responsibility)**
2. **Use intermediate outputs for debugging**
3. **Implement stage-level error handling**
4. **Make pipelines configurable via YAML/JSON**

## Installation

```bash
# Install required dependencies
pip install python-docx openpyxl python-pptx reportlab jinja2
```

## Resources

- [Custom Repository](https://github.com/claude-office-skills/skills)
- [Claude Office Skills Hub](https://github.com/claude-office-skills/skills)


---
Source attribution: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/lijie420461340/doc-pipeline/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 clawdbot

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/lijie420461340/doc-pipeline/SKILL.md
Attribution basis: Source repository record

Fields of work: Content & Media, Data & Analytics, Marketing, Product Management, Project & Program Management
Roles: Content Marketer, Data Analyst, Product Manager, Project Manager, Technical Writer
Specialties: API
Tools: Jinja2, Openpyxl, Python Docx, Python Pptx, Reportlab
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 0e0223170cceb61408841eef3237393ebfa2b62ffd8dbcad02f81137b5885a97