Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    ai workflow failure finder

    2

    A systematic diagnostic framework for debugging AI pipelines, prompt drift, RAG failures, and agent orchestrations.

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    ai workflow failure finder

    ai workflow failure finder

    Example session with this skill installed

    My RAG system is returning the wrong answers even though the data is in the database. Help me debug why it is failing.

    • Read your context and instructions
    • Compiled the ai workflow failure

    The issue is likely a 'Hallucination Loop' or retrieval mismatch.

    1. Check similarity thresholds for your embeddings.
    2. Verify if the model is ignoring retrieved context.
    3. Add grounding instructions: 'Only use information from the provided context.'
    4. Implement a fact-checking layer.

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

    What you get

    Debug silent hallucinations in production LLM responsesFix state loss and circular dependencies in multi-agent handoffsOptimize RAG retrieval accuracy and context window usageImplement robust error handling for API rate limits and timeoutsPerform regression testing on prompt changes to prevent behavioral drift

    About this skill

    The problem

    AI workflows fail in non-obvious ways. Silent hallucinations, context drift, and non-deterministic behavior make standard debugging tools ineffective for LLM pipelines.

    What it does

    • Classifies failures into specific categories like context cliffs, instruction collisions, and silent schema mismatches.
    • Provides a triage protocol to isolate API issues from prompt logic or resource constraints.
    • Diagnoses RAG-specific failure modes including retrieval noise and context window saturation.
    • Offers structured code patterns for implementing circuit breakers, exponential backoff, and response validators.
    • Outlines production incident response timelines for rapid recovery.

    Frameworks & tools

    Works with any LLM provider (OpenAI, Anthropic) and orchestration layers like LangChain or LangSmith. Includes code snippets for Python, Bash, and JSON schema validation.

    Why this beats prompting it yourself

    General-purpose LLMs often suggest generic debugging steps like "check your logs." This skill provides a specialized diagnostic framework built for AI-specific edge cases like temperature variance and token truncation that standard software debugging misses.

    Use cases

    • Identify why a multi-agent system is losing state during handoffs.
    • Debug why a RAG pipeline is returning irrelevant documents.
    • Resolve persistent 429 rate limit errors with jitter-based backoff strategies.
    • Determine the optimal temperature for deterministic structured data output.

    Known limitations

    Focuses on logical and architectural failures. Does not fix underlying provider-side outages or model-level bias issues.

    How to install

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

    ~30 seconds
    1. 1

      Download the ZIP

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    2. 2

      Unzip into your skills folder

      Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.

    3. 3

      Ask your agent to use it

      Restart the agent if it was already running. It picks the skill up automatically - no config needed.

    Skills folder by agent

    Click the path to copy it. Create the folder if it does not exist yet.

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    Fresh listing

    Recently published to Agensi

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    Trust & safety

    Security scanned

    Verified clean 1 month ago

    • Passed all security checks, Safe to install

    Listed1 month ago

    What's inside

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