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