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    Nex Claude Api Integration

    5

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

    $9

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    nex-claude-api-integration

    nex-claude-api-integration

    Example session with this skill installed

    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.

    • Read your context and instructions
    • Compiled the nex-claude-api-integration

    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.

    Connects securely to your tools. The creator never sees your data.

    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.

    Built by Nex AI. More skills and info at nex-ai.be and slopsome.com.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Agents & Orchestration.

    • Requires manual Anthropic API key setup.
    • Focuses on Python-only backends.
    • Does not include frontend UI components.

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    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    4 uses

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    Security scan passed 3 months ago

    Permissions

    Terminal / Shell
    Environment Variables
    Listed5 months ago
    Updated3 months ago

    What's inside

    Frequently Asked Questions

    This skill provides a structured, architecture-ready Python implementation for Claude, replacing manual scripts with a reusable 'AIProvider' interface that handles model routing, token counting, and error recovery out-of-the-box.

    The skill is optimized for Python-based frameworks like FastAPI, Flask, and Django, and it is designed to fit into any backend architecture that requires reliable LLM communication.

    The integration features a dual-model strategy that automatically routes high-volume, simple tasks to Claude's faster, cheaper models while reserving the premium models for complex reasoning.

    You will receive a modular package of production-ready Python files—including a factory pattern, base provider classes, and the specific Anthropic provider implementation—ready to drop into your codebase.

    Yes, the code uses a standardized provider interface, making it straightforward to extend the logic to other LLMs while maintaining a consistent structure across your application.

    It includes a 'validate-then-retry' loop that catch transient API errors or malformed outputs, attempting to correct and re-run the request before returning a clean exception to your system.

    New in Agents & Orchestration