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    LLM API Integration Debugger

    1

    Technical debugger for LLM API integrations, SDK mismatches, and response parsing errors.

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    LLM API Integration Debugger

    LLM API Integration Debugger

    Example session with this skill installed

    My OpenAI call fails with 'TypeError: 'ChatCompletion' object is not subscriptable'. I'm using 'print(response["choices"][0]["message"]["content"])'. How do I fix this?

    • Read your context and instructions
    • Compiled the llm api integration
    • Generated the document

    The SDK is returning a Pydantic object, but you are attempting to access it as a Python dictionary.

    LLM Integration Debug Report

    ComponentStatusFinding
    ProviderOpenAIv1.0.0+ SDK detected based on response type.
    ErrorTypeErrorSubscript access [] is not supported for this object.
    Root CauseSyntax MismatchThe library transitioned from dict-like responses to object-oriented attributes.

    Corrected Implementation

    
# Problematic Pattern
# print(response["choices"][0]["message"]["content"])

# Corrected Pattern (OpenAI SDK v1+)
print(response.choices[0].message.content)

# Alternative (convert to dict if preferred)
response_dict = response.model_dump()
print(response_dict["choices"][0]["message"]["content"])

    Next steps

    1. Update the accessor syntax to use dot notation for all ChatCompletion attributes.
    2. Verify the SDK version in your requirements.txt matches the implementation.
    3. Run a test call to ensure response.choices[0].message.content returns the expected string.

    llm-api-integration-debugger.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

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

    What you get

    Identify root causes for failed LLM API requests and 4xx/5xx errors.Fix response parsing logic for streaming and structured outputs.Correct malformed tool-calling and multimodal request payloads.Resolve environment-specific authentication and header issues.

    About this skill

    The problem

    LLM API integrations often fail with cryptic errors, mismatched SDK versions, or incorrect response parsing. Developers waste hours tracing whether a 401 is a malformed header or a deployment secret issue.

    What it does

    • Analyzes code, payloads, and error logs to identify the exact point of failure in the request lifecycle.
    • Diagnoses authentication, serialization, streaming, and provider-specific parameter mismatches.
    • Maps HTTP status codes to specific integration fixes rather than generic causes.
    • Detects response parsing errors where code expects a different JSON structure than the provider returns.

    Frameworks & tools

    Supports all major LLM providers including OpenAI, Anthropic, and Google Gemini. Works with Python, JavaScript/TypeScript, and raw HTTP clients like cURL or Axios.

    Why this beats prompting it yourself

    General-purpose models often hallucinate parameters or recommend outdated SDK syntax. This skill follows a strict INSPECT to VALIDATE workflow, ensuring diagnoses are backed by technical evidence rather than guesswork.

    Use cases

    • Fixing 'object is not subscriptable' errors in SDK response parsing.
    • Debugging streaming chunks that break application logic.
    • Resolving authentication failures that only appear in CI/CD or production environments.
    • Correcting malformed tool-calling and function-calling payloads.

    Known limitations

    Does not execute live API calls. Requires provided code snippets or error logs to function.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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      Download the ZIP

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      Unzip into your skills folder

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    3. 3

      Ask your agent to use it

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    Security scanned

    Verified clean 6 days ago

    • Passed all security checks, Safe to install

    Listed6 days ago

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