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

• Describe what you want to accomplish

• Provide any required input data or files

• 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)

# 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

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

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

• Keep stages focused (single responsibility)

• Use intermediate outputs for debugging

• Implement stage-level error handling

• Make pipelines configurable via YAML/JSON

Installation

# Install required dependencies

pip install python-docx openpyxl python-pptx reportlab jinja2

Resources

• Custom Repository

• Claude Office Skills Hub