Extraction Pipeline Patterns
Master format detection, extraction, and fallback orchestration for 75+ file formats.
Evidence shows a detailed security policy (SECURITY.md) outlining mitigations for malicious inputs (zip bombs, path traversal, entity expansion, etc.), and no signs of overreach or covert behavior. However, code was not executed, and least privilege, user confirmation, rollback details cannot be verified, hence deduction.
Evidence includes real CI workflows (benchmarks.yaml, ci-docker.yaml) and committed test suites (e2e/node/tests/*.test.ts), suggesting key paths have test coverage. But static review did not execute, and error handling quality, failure feedback are only inferred from docs, hence deduction.
Skill description is clear, specifying applicable scenarios (document extraction) and non-fit boundaries (e.g., unsupported formats). Supports multiple languages and deployment modes, environment fit is good. But trigger conditions (semantic triggers) are only inferred from description without concrete examples, hence deduction.
Documentation is well-structured, with clear installation steps, examples, capability lists, and limitation notes. License is explicit (MIT), but versioning (e.g., changelog) details are thin, and maintenance responsibility is not clearly stated in the skill doc, hence deduction.
README and skill doc describe capabilities in detail, but static review cannot verify output immediacy or actual task completion efficiency. Although benchmarks are mentioned, no concrete results are provided, hence deduction.
Evidence includes real CI workflows and test files, but no third-party independent verification or cross-source corroboration is provided. Static review cannot execute tests, hence deduction.
- Static review, code not executed; functional correctness not verified.
- Publisher not verified; identity unknown, assess trust yourself.
- Some features (e.g., OCR, embeddings) depend on external models or services, may be unavailable in certain network environments.
- Skill doc does not explicitly mention sensitive data handling flow; use with caution.
What does this skill do, and when should you use it?
This skill, drawn from the Xberg document-intelligence framework, focuses on architecting robust document-extraction pipelines. It details the core pipeline: format detection via magic bytes and extension validation, routing to format-specific extractors, and fallback strategies for password-protected PDFs, OCR, and nested archives. It provides Rust pseudocode for MIME detection and extraction dispatch, identifies key modules (e.g., mime.rs, pipeline.rs), and outlines feature-flag management. It serves as a practical reference for developers designing or implementing similar document-processing pipelines. Xberg itself is MIT-licensed and a mature multi-language library.
The skill describes document extraction pipeline architecture and patterns: 1) detects MIME type via magic bytes and validates extension to prevent spoofing; 2) routes documents to format-specific extractors (PDF, DOCX, images, HTML, email, archives, etc.); 3) enables fallback strategies including password attempts, OCR fallback, recursive archive extraction, and corrupted-file recovery; 4) enforces a mandatory post-processing pipeline with validators, quality processing, chunking, and custom hooks; 5) suggests feature-flag-driven conditional compilation.
- Backend developers building a multi-format document extraction service in Rust or Python, avoiding from-scratch implementation.
- Data engineers integrating PDF, Office, and image extraction into RAG pipelines with reliable fallback.
- Cloud-native developers using Xberg's Docker images for a document-processing API.
- Automation engineers handling tricky inputs like password-protected PDFs or nested archives.
What are this skill's strengths and limitations?
- Clear architectural blueprint: format detection → extraction → fallback → post-processing.
- Explicit fallback strategies for password-protected PDFs, OCR, and corrupted files.
- Provides reproducible Rust code snippets and module pointers.
- Emphasizes security measures like format-alignment validation to prevent spoofing.
- Covers a wide range of formats (75+) and feature-flag management.
- Some implementation details are specific to Xberg's codebase and may not be directly reusable.
- Rust code examples may pose a learning curve for non-Rust developers.
- Skill lacks configuration or deployment steps beyond what is shown in installation examples.
- No dedicated test suite or operational guidance included.
How do you install this skill?
The skill resides at .ai-rulez/skills/extraction-pipeline-patterns/SKILL.md within the Xberg repository. To use the full Xberg library, install via package manager (e.g., pip install xberg for Python, npm install @xberg-io/xberg for Node.js). No standalone installation steps are provided for this skill.
How do you use this skill?
Read the skill's SKILL.md to understand the pipeline architecture and key patterns. To use Xberg in your project, follow the quick-start examples in the repo's README (e.g., using the Rust crate, configuring ExtractionConfig, and calling extract). Consult the Xberg documentation for detailed usage before adopting these patterns.