Works with the AI tools you already use
ai workflow failure finder
by tamim boy
A systematic diagnostic framework for debugging AI pipelines, prompt drift, RAG failures, and agent orchestrations.
Secure checkout via Stripe
See it in action
You say
My RAG system is returning the wrong answers even though the data is in the database. Help me debug why it is failing.
Your agent does
The issue is likely a 'Hallucination Loop' or retrieval mismatch.
- Check similarity thresholds for your embeddings.
- Verify if the model is ignoring retrieved context.
- Add grounding instructions: 'Only use information from the provided context.'
- Implement a fact-checking layer.
What you get
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
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
Fresh listing
Recently published to Agensi
30-day refund
Not a fit? Get your money back
Trust & safety
Security scanned
Verified clean today
- 30-day refund guarantee
- One-time purchase, yours forever
- Secure checkout via Stripe
Frequently Asked Questions
Popular in Workflow Automation
it support desk
Diagnose and resolve 1st/2nd line IT issues with structured triage, health scripts, and escalation workflows.
agent-financial-research-workflow
Teaches AI coding agents (Claude Code, Cursor, Codex CLI) to perform structured financial research using free APIs and systematic methodology. Covers SEC filing analysis, financial metrics extraction,
skill miner
Analyzes your agent conversation history to find and automate your most frequent recurring tasks.
PPTX Toolkit — Create, Edit, and Extract PowerPoint Presentations
Everything needed to work with PowerPoint presentations programmatically