What problem does it solve? When Beam.ai agent tasks fail, stop, or require input, finding the root cause means manually digging through task logs and Langfuse traces. This Skill automates that investigation by fetching issue tasks, analyzing trace reasoning, and producing structured debug reports. ## Core Features & Use Cases - Issue Task Detection: Lists tasks with statuses like FAILED, ERROR, STOPPED, TIMEOUT, or USER_INPUT_REQUIRED across BID and Prod workspaces over configurable lookback periods. - Langfuse Trace Analysis: Extracts reasoning from key spans (ParameterSelection, ExecuteGPT_Tool, NodeSelection:EdgeEvaluation, TaskSuccessCriteriaCheck) to identify root causes, with direct session and trace URLs. - Smart Brevity Debug Reports: Saves markdown reports with headline, takeaway, details, root cause, and fix suggestions to per-agent debug folders. - Use Case: An agent's tasks keep stopping with 'condition failed'. Run the script with the agent ID to see that a checklist evaluation failed because a subfolder parameter was null, then fix the input data. ## Quick Start Ask the AI to debug issue tasks for a specific Beam agent ID, optionally specifying a task ID, workspace, or lookback period in days.
