dyad%3asession-debug（成对%3session 调试）

Session Debug

Analyze session debugging data to identify errors and issues that may have caused a user-reported problem.

Arguments

• $ARGUMENTS: Two space-separated arguments expected:

1. URL to a JSON file containing session debugging data (starts with http:// or https://) 2. GitHub issue number or URL

Instructions

1. Parse and validate the arguments: Split $ARGUMENTS on whitespace to get exactly two arguments: Validation: If fewer than two arguments are provided, inform the user: "Usage: /dyad:session-debug " "Example: /dyad:session-debug https://example.com/session.json 123" Then stop execution.

- First argument: session data URL (must start with http:// or https://) - Second argument: GitHub issue identifier (number like 123 or full URL like https://github.com/owner/repo/issues/123)

1. Fetch the GitHub issue: gh issue view <issue-number> --json title,body,comments,labels Understand:

- What problem the user is reporting - Steps to reproduce (if provided) - Expected vs actual behavior - Any error messages the user mentioned

1. Fetch the session debugging data: Use WebFetch to retrieve the JSON session data from the provided URL.
2. Analyze the session data: Look for suspicious entries including:

- Errors: Any error messages, stack traces, or exception logs - Warnings: Warning-level log entries that may indicate problems - Failed requests: HTTP errors, timeout failures, connection issues - Unexpected states: Null values where data was expected, empty responses - Timing anomalies: Unusually long operations, timeouts - User actions before failure: What the user did leading up to the issue

1. Correlate with the reported issue: For each suspicious entry found, assess:

- Does the timing match when the user reported the issue occurring? - Does the error message relate to the feature/area the user mentioned? - Could this error cause the symptoms the user described?

1. Rank the findings: Create a ranked list of potential causes, ordered by likelihood: ## Most Likely Causes ### 1. [Error/Issue Name] - **Evidence**: What was found in the session data - **Timestamp**: When it occurred - **Correlation**: How it relates to the reported issue - **Confidence**: High/Medium/Low ### 2. [Error/Issue Name]...
2. Provide recommendations: For each high-confidence finding, suggest:

- Where in the codebase to investigate - Potential root causes - Suggested fixes if apparent

1. Summarize:

- Total errors/warnings found - Top 3 most likely causes - Recommended next steps for investigation