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:

• URL to a JSON file containing session debugging data (starts with http:// or https:// )

• GitHub issue number or URL

Instructions

Parse and validate the arguments:

Split $ARGUMENTS on whitespace to get exactly two arguments:

• 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 )

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.

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

Fetch the session debugging data:

Use WebFetch to retrieve the JSON session data from the provided URL.

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

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?

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]

...

Provide recommendations:

For each high-confidence finding, suggest:

• Where in the codebase to investigate

• Potential root causes

• Suggested fixes if apparent

Summarize:

• Total errors/warnings found

• Top 3 most likely causes

• Recommended next steps for investigation