Ocr Paddleocr

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps

• Backend

• Frontend

• AI-RAG

• Security Critical

Overview

PaddleOCR is a powerful, open-source OCR toolkit that supports multi-language text recognition, table recognition, and document layout analysis. This skill covers implementation patterns for various document processing scenarios.

Why This Matters

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Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions

• Maintain consistency across codebase

• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution

• Iterate based on feedback and requirements

• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:

• <e.g., env vars, request payload, file paths, schema>

• Entry Conditions:

• <Pre-requisites: e.g., Repo initialized, DB running, specific branch checked out>

• Outputs:

• <e.g., artifacts (PR diff, docs, tests, dashboard JSON)>

• Artifacts Required (Deliverables):

• <e.g., Code Diff, Unit Tests, Migration Script, API Docs>

• Acceptance Evidence:

• <e.g., Test Report (screenshot/log), Benchmark Result, Security Scan Report>

• Success Criteria:

• <e.g., p95 < 300ms, coverage ≥ 80%>

Skill Composition

• Depends on: None

• Compatible with: None

• Conflicts with: None

• Related Skills: None

Quick Start / Implementation Example

• Review requirements and constraints

• Set up development environment

• Implement core functionality following patterns

• Write tests for critical paths

• Run tests and fix issues

• Document any deviations or decisions

Example implementation following best practices

def example_function(): # Your implementation here pass

Assumptions / Constraints / Non-goals

• Assumptions:

• Development environment is properly configured

• Required dependencies are available

• Team has basic understanding of domain

• Constraints:

• Must follow existing codebase conventions

• Time and resource limitations

• Compatibility requirements

• Non-goals:

• This skill does not cover edge cases outside scope

• Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:

• Python 3.8+

• Node.js 16+

• Modern browsers (Chrome, Firefox, Safari, Edge)

• Required AI Tools:

• Code editor (VS Code recommended)

• Testing framework appropriate for language

• Version control (Git)

• Dependencies:

• Language-specific package manager

• Build tools

• Testing libraries

• Environment Setup:

• .env.example keys: API_KEY , DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Type Focus Area Required Scenarios / Mocks

Unit Core Logic Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage

Integration DB / API All external API calls or database connections must be mocked during unit tests

E2E User Journey Critical user flows to test

Performance Latency / Load Benchmark requirements

Security Vuln / Auth SAST/DAST or dependency audit

Frontend UX / A11y Accessibility checklist (WCAG), Performance Budget (Lighthouse score)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII

• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager

• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms

• Memory Management: Use streams/pagination for large data

• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection

• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id

• Metrics: Track error_rate , latency , queue_depth

• Error Handling: Standardized error codes, no bare except

• Observability Artifacts:

• Log Fields: timestamp, level, message, request_id

• Metrics: request_count, error_count, response_time

• Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.

• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.

• Self-Review: Check against Guardrails & Anti-patterns before finalizing.

• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met

• Lint/Typecheck passed

• Logging/Metrics/Trace implemented

• Security checks passed

• Documentation/Changelog updated

• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries

• ⚠️ Watch out for: Common symptoms and quick fixes

• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples

• Official documentation and best practices

• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0

• Changelog:

• 2026-02-22: Initial version with complete template structure