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    nex-claude-api-integration

    Production-ready Claude API integration with dual-model routing, token tracking, and resilient retry logic.

    Updated Jun 2026
    Security scanned

    $9

    · or 45 credits

    30-day refund guarantee

    Secure checkout via Stripe

    Included in download

    • Route high-volume classification to cheap models and generation to strong ones
    • Implement a 'validate-then-retry' loop to fix malformed model outputs
    • terminal, env_vars automation included
    • Instant install

    Sample input

    I need to call Claude in my Python app to score 10,000 leads and write emails for the good ones. I need it to be cost-effective and handle API timeouts.

    Sample output

    I've generated a `claude-integration/` package. It uses a cheap model for `score_prospect()` and a strong model for `generate_email()`. All calls are wrapped in a typed error taxonomy with token tracking. The factory handles model routing so your business logic stays clean.

    About This Skill

    Production-Grade Claude Integration for Python

    Stop writing fragile, one-off API calls. This skill builds a robust, production-ready integration layer for the Anthropic Claude API using professional software architecture patterns. It moves beyond simple SDK scripts to create a resilient provider abstraction that handles the complexities of running LLMs at scale.

    Features

    • Dual-Model Strategy: Routes high-volume tasks (like scoring or classification) to cheaper models while reserving premium models for complex generation, optimizing your API spend automatically.
    • Provider Abstraction: Implementation follows an AIProvider interface, allowing you to swap models or providers in the future without refactoring your entire backend.
    • Robust Error Taxonomy: Includes specific exception handling for timeouts, transient API errors, and invalid outputs, mapping directly to smart retry policies.
    • Built-in Token Accounting: Every call returns precise input and output token counts, enabling real-time cost tracking and budgeting.
    • Resilient Generation Loop: Features a "validate-then-retry" logic that attempts to salvage failed generations with stricter prompting before failing.

    Supported Tools

    Standardized for Python backends using the Anthropic Messages API. The output is structured as a clean, modular package (base.py, factory.py, anthropic_provider.py) ready for integration into FastAPI, Flask, or Django applications.

    Use Cases

    • Route high-volume classification to cheap models and generation to strong ones
    • Implement a 'validate-then-retry' loop to fix malformed model outputs
    • Track token usage and costs across different application features
    • Abstract the LLM layer to allow swapping providers without code rewrites
    • Handle Anthropic API timeouts and transient errors with a robust retry policy

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    Security Scanned

    Passed automated security review

    Permissions

    Terminal / Shell
    Environment Variables

    Allowed Hosts

    keepachangelog.com
    semver.org
    nex-ai.be

    File Scopes

    assets/**
    references/**

    Frequently Asked Questions