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Found 8 Skills
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when debugging Foundation Models issues — context exceeded, guardrail violations, slow generation, availability problems, unsupported language, or unexpected output. Systematic diagnostics with production crisis defense.
AI Debugging Collaboration Solution. Convert console.log into HTTP requests to collect logs. After the user completes operations, AI can automatically view and analyze the logs without the need for screenshots or copying console content. Supports Claude Code, OpenCode, Cursor.
Avoid common mistakes and debug issues in PydanticAI agents. Use when encountering errors, unexpected behavior, or when reviewing agent implementations.
Debug AI traces, find exceptions, analyze sessions, and manage prompts via Langfuse MCP. Also handles MCP setup and configuration.
Analyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on "analyze this trace", "what went wrong with this trace", "debug trace", "investigate trace", "why did this trace fail", "root cause this trace".
Create effective debugging prompts—include error messages, stack traces, expected vs actual behavior, logs, and attempted solutions
AI-powered enterprise debugging orchestrator with Context7 integration, intelligent error pattern recognition, automated root cause analysis, predictive fix suggestions, and multi-process debugging coordination across 25+ languages and distributed systems