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    AI Provider Migration & Compatibility Readiness Review

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    AI Provider Migration & Compatibility Readiness Review is a bounded ToolForge Labs workflow.

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    AI Provider Migration & Compatibility Readiness Review

    AI Provider Migration & Compatibility Readiness Review

    Example session with this skill installed

    We plan to move a support assistant from one AI provider to another. It uses tools, JSON outputs, streaming, safety rules, retrieval context, and provider-specific retry settings. Review migration readiness and identify blockers before cutover.

    • Read your context and instructions
    • Compiled the ai provider migration

    The likely issue is a contract mismatch around AI provider migrations, model identifiers, authentication, tool calling, structured outputs, streaming, token/context limits, safety settings, latency, fallback behavior, evaluation, observability, rollout, and rollback. I would first define the invariant: An AI provider migration is ready only when required application behavior, safety boundaries, tool/schema contracts, observability, cost/latency constraints, and rollback evidence are mapped and tested against the replacement provider. Then I would trace the smallest owning boundary, keep VERIFIED facts separate from LIKELY/UNKNOWN state, apply or propose the narrowest repair, and add deterministic normal, boundary, failure, retry/cancellation, and recovery tests. The workflow preserves authorization, privacy, security policy, public interfaces, user-visible behavior, and documented ownership. With repository/workspace access, findings can map to exact paths and focused local validation; with partial evidence, the result remains an explicit diagnosis/evidence map with no invented execution claims.

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

    About this skill

    AI Provider Migration & Compatibility Readiness Review is a bounded ToolForge Labs workflow. Diagnose and repair AI provider migrations, model identifiers, authentication, tool calling, structured outputs, streaming, token/context limits, safety settings, latency, fallback behavior, evaluation, observability, rollout, and rollback. Works from repositories/workspaces or partial evidence. Produces bounded fixes and deterministic validation while preserving authorization, security, public interfaces, user-visible behavior, and documented ownership and avoiding invented execution claims. It works from a full repository/workspace or partial evidence such as snippets, logs, configuration, traces, documents, tickets, and architecture notes. The skill separates verified facts from assumptions, avoids invented execution claims, and produces concrete repair/review steps with focused deterministic validation or traceable evidence.

    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 repository access or specific snippets/logs to be effective. Does not perform live production infrastructure changes or secret rotation directly.

    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

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    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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    Permissions

    Terminal / Shell
    Read Files
    Write Files

    Use a clean branch or controlled review workspace. Supply repository/workspace files, logs, traces, configuration, documents, or evidence needed for the task. Review patches/findings before merge or operational action; keep production credentials and destructive production actions out of the workflow.

    Listed1 month ago
    Updated9 days ago

    What's inside

    Frequently Asked Questions

    This skill performs a comprehensive audit and repair of your AI integration, fixing issues related to provider migrations (e.g., OpenAI to Anthropic), tool-calling syntax, structured output schemas, and token limit management. It ensures your agent remains stable and secure while switching models or optimizing latency and fallback behaviors.

    The skill is designed for versatility, working directly from full code repositories, workspaces, or even partial evidence like logs, configuration files, and architecture notes. It supports major providers including OpenAI and Anthropic, specifically addressing their unique authentication and streaming requirements.

    Every review produces concrete, bounded repair steps and deterministic validation to ensure changes don't break public interfaces or existing security protocols. The skill strictly avoids 'invented execution claims,' meaning it only provides fixes based on verified facts and traceable evidence within your environment.

    Unlike generic code linters, this skill specializes in LLM-ops, diagnosing subtle issues like safety setting mismatches, latency bottlenecks during rollout, and the specific nuances of tool-calling and structured outputs across different providers. It provides a roadmap for both migration and rollback, ensuring your transition is resilient.

    You receive a complete ToolForge Labs workflow that includes diagnostic modules for LLM parameters, migration templates for model identifiers, and a validation framework for testing your agent’s new configuration. It also includes documentation on how to implement observability and fallback logic based on the review's findings.

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