Identify agent failures that happen before any work starts by checking iteration counts, tool use, artifacts, and logs. Classify these incidents as pre-execution failures and guide investigation toward environment setup, initialization, and resource limits.
Identify and classify agent failures occurring before execution starts
Review the instructions the agent will receive before you install.
This skill provides a workflow for identifying and classifying agent failures that occur prior to any task execution.
Flag a task execution as a Zero-Iteration Failure if all of the following conditions are met:
When a Zero-Iteration Failure is detected, do not attempt standard agent-level debugging (e.g., analyzing reasoning chains or tool parameters). Instead, investigate system-level causes:
failure_mode: pre_execution rather than failure_mode: agent_logic.Status: Failed
Iterations: 0
Tools Called: []
Error: "Session initialization failed: Missing environment variable"
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Prepare audio stems, the separate parts of a track, for mixing by matching their durations with silence, loops, or crossfades. Verify each generated and processed file before moving to the next production step.
Produce audio in separate stems, meaning individual instrument tracks, and verify each output as you go. Align track lengths with silence, loops, or crossfades, then apply effects, mix a master, and package the files.
Build serverless endpoints that relay external API requests for a dashboard while keeping API keys on the server. Includes Vercel examples for input checks, caching, cross-origin access, and consistent JSON responses.
Produce a master track and separate audio parts through a structured Python workflow. Apply filters and reverb, export WAV files with consistent settings, package them in a ZIP archive, and check that the deliverables meet the requested specifications.
Keep document creation moving when web searches and page retrieval repeatedly fail. The skill preserves the original task, uses the model’s existing knowledge, and clearly identifies information that could not be checked against external sources.
When Python execution repeatedly fails in execute_code_sandbox, use a file-based alternative: save the script, run it through the shell, and capture results or errors. The guidance covers paths, imports, timeouts, structured output, and optional cleanup.