fix-logs

[IMPORTANT] Use TaskCreate to break ALL work into small tasks BEFORE starting — including tasks for each file read. This prevents context loss from long files. For simple tasks, AI MUST ask user whether to skip.

Prerequisites: MUST READ .claude/skills/shared/understand-code-first-protocol.md AND .claude/skills/shared/evidence-based-reasoning-protocol.md before executing.

• docs/project-reference/domain-entities-reference.md — Domain entity catalog, relationships, cross-service sync (read when task involves business entities/models)
• .claude/skills/shared/estimation-framework.md — Story points and complexity (MUST provide story_points and complexity estimate in fix summary)

Skill Variant: Variant of /fix — log-based troubleshooting and error analysis.

Quick Summary

Goal: Analyze application logs to diagnose and fix runtime errors or unexpected behavior.

Workflow:

1. Collect — Gather relevant log output (error messages, stack traces, timestamps)
2. Trace — Map log entries to source code locations
3. Fix — Apply fix based on traced execution path

Key Rules:

• Debug Mindset: every claim needs file:line evidence
• Focus on log patterns: stack traces, error codes, timing anomalies
• Cross-reference logs with source code to find actual root cause

IMPORTANT: Analyze the skills catalog and activate the skills that are needed for the task during the process.

Debug Mindset (NON-NEGOTIABLE)

Be skeptical. Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence percentages (Idea should be more than 80%).

• Do NOT assume the first hypothesis is correct — verify with actual code traces
• Every root cause claim must include file:line evidence
• If you cannot prove a root cause with a code trace, state "hypothesis, not confirmed"
• Question assumptions: "Is this really the cause?" → trace the actual execution path
• Challenge completeness: "Are there other contributing factors?" → check related code paths
• No "should fix it" without proof — verify the fix addresses the traced root cause

⚠️ MANDATORY: Confidence & Evidence Gate

MANDATORY IMPORTANT MUST declare Confidence: X% with evidence list + file:line proof for EVERY claim. 95%+ recommend freely | 80-94% with caveats | 60-79% list unknowns | <60% STOP — gather more evidence.

Mission

$ARGUMENTS

Workflow

1. Check if ./logs.txt exists:
   • If missing, set up permanent log piping in project's script config (package.json, Makefile, pyproject.toml, etc.):
     • Bash/Unix: append 2>&1 | tee logs.txt
     • PowerShell: append *>&1 | Tee-Object logs.txt
   • Run the command to generate logs
2. Use debugger subagent to analyze ./logs.txt and find root causes:
   • Use Grep with head_limit: 30 to read only last 30 lines (avoid loading entire file)
   • If insufficient context, increase head_limit as needed
   • External Memory: Write log analysis to .ai/workspace/analysis/{issue-name}.analysis.md. Re-read before fixing.
3. Use scout subagent to analyze the codebase and find the exact location of the issues, then report back to main agent.
4. Use planner subagent to create an implementation plan based on the reports, then report back to main agent.
5. Start implementing the fix based the reports and solutions.
6. Use tester agent to test the fix and make sure it works, then report back to main agent.
7. Use code-reviewer subagent to quickly review the code changes and make sure it meets requirements, then report back to main agent.
8. If there are issues or failed tests, repeat from step 3.
9. After finishing, respond back to user with a summary of the changes and explain everything briefly, guide user to get started and suggest the next steps.

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IMPORTANT Task Planning Notes (MUST FOLLOW)

• Always plan and break work into many small todo tasks
• Always add a final review todo task to verify work quality and identify fixes/enhancements
• After fixing, MUST run /prove-fix — build code proof traces per change with confidence scores. Never skip.