b60638f0a3
Community Skills (32): - jat: jat-start, jat-verify, jat-complete - pi-mono: codex-cli, codex-5.3-prompting, interactive-shell - picoclaw: github, weather, tmux, summarize, skill-creator - dyad: 18 skills (swarm-to-plan, multi-pr-review, fix-issue, lint, etc.) - dexter: dcf valuation skill Agents (23): - pi-mono subagents: scout, planner, reviewer, worker - toad: 19 agent configs (Claude, Codex, Gemini, Copilot, OpenCode, etc.) System Prompts (91): - Anthropic: 15 Claude prompts (opus-4.6, code, cowork, etc.) - OpenAI: 49 GPT prompts (gpt-5 series, o3, o4-mini, tools) - Google: 13 Gemini prompts (2.5-pro, 3-pro, workspace, cli) - xAI: 5 Grok prompts - Other: 9 misc prompts (Notion, Raycast, Warp, Kagi, etc.) Hooks (9): - JAT hooks for session management, signal tracking, activity logging Prompts (6): - pi-mono templates for PR review, issue analysis, changelog audit Sources analyzed: jat, ralph-desktop, toad, pi-mono, cmux, pi-interactive-shell, craft-agents-oss, dexter, picoclaw, dyad, system_prompts_leaks, Prometheus, zed, clawdbot, OS-Copilot, and more
92 lines
2.9 KiB
Markdown
92 lines
2.9 KiB
Markdown
---
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name: dyad:session-debug
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description: Analyze session debugging data to identify errors and issues that may have caused a user-reported problem.
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---
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# Session Debug
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Analyze session debugging data to identify errors and issues that may have caused a user-reported problem.
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## Arguments
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- `$ARGUMENTS`: Two space-separated arguments expected:
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1. URL to a JSON file containing session debugging data (starts with `http://` or `https://`)
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2. GitHub issue number or URL
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## Instructions
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1. **Parse and validate the arguments:**
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Split `$ARGUMENTS` on whitespace to get exactly two arguments:
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- First argument: session data URL (must start with `http://` or `https://`)
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- Second argument: GitHub issue identifier (number like `123` or full URL like `https://github.com/owner/repo/issues/123`)
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**Validation:** If fewer than two arguments are provided, inform the user:
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> "Usage: /dyad:session-debug <session-data-url> <issue-number-or-url>"
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> "Example: /dyad:session-debug https://example.com/session.json 123"
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Then stop execution.
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2. **Fetch the GitHub issue:**
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```
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gh issue view <issue-number> --json title,body,comments,labels
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```
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Understand:
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- What problem the user is reporting
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- Steps to reproduce (if provided)
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- Expected vs actual behavior
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- Any error messages the user mentioned
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3. **Fetch the session debugging data:**
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Use `WebFetch` to retrieve the JSON session data from the provided URL.
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4. **Analyze the session data:**
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Look for suspicious entries including:
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- **Errors**: Any error messages, stack traces, or exception logs
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- **Warnings**: Warning-level log entries that may indicate problems
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- **Failed requests**: HTTP errors, timeout failures, connection issues
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- **Unexpected states**: Null values where data was expected, empty responses
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- **Timing anomalies**: Unusually long operations, timeouts
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- **User actions before failure**: What the user did leading up to the issue
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5. **Correlate with the reported issue:**
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For each suspicious entry found, assess:
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- Does the timing match when the user reported the issue occurring?
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- Does the error message relate to the feature/area the user mentioned?
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- Could this error cause the symptoms the user described?
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6. **Rank the findings:**
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Create a ranked list of potential causes, ordered by likelihood:
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```
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## Most Likely Causes
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### 1. [Error/Issue Name]
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- **Evidence**: What was found in the session data
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- **Timestamp**: When it occurred
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- **Correlation**: How it relates to the reported issue
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- **Confidence**: High/Medium/Low
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### 2. [Error/Issue Name]
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...
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```
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7. **Provide recommendations:**
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For each high-confidence finding, suggest:
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- Where in the codebase to investigate
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- Potential root causes
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- Suggested fixes if apparent
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8. **Summarize:**
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- Total errors/warnings found
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- Top 3 most likely causes
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- Recommended next steps for investigation
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