GLM-Tools-Skills-Agents/skills/community/dyad/session-debug/SKILL.md

name, description

name	description
dyad:session-debug	Analyze session debugging data to identify errors and issues that may have caused a user-reported problem.

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:

   • 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.

2. 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

3. Fetch the session debugging data:

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

4. 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

5. 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?

6. 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]
   ...
   

7. Provide recommendations:

   For each high-confidence finding, suggest:

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

8. Summarize:

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