[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)

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:

• Collect — Gather relevant log output (error messages, stack traces, timestamps)

• Trace — Map log entries to source code locations

• 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

• 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

• 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.

• Use scout subagent to analyze the codebase and find the exact location of the issues, then report back to main agent.

• Use planner subagent to create an implementation plan based on the reports, then report back to main agent.

• Start implementing the fix based the reports and solutions.

• Use tester agent to test the fix and make sure it works, then report back to main agent.

• Use code-reviewer subagent to quickly review the code changes and make sure it meets requirements, then report back to main agent.

• If there are issues or failed tests, repeat from step 3.

• 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.

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.