fix-fast
[IMPORTANT] Use
TaskCreateto 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 providestory_pointsandcomplexityestimate in fix summary)
Skill Variant: Variant of
/fix— quick fixes with minimal investigation.
Quick Summary
Goal: Rapidly fix small, well-understood issues with minimal investigation overhead.
Workflow:
- Identify — Quick root cause analysis from error message
- Fix — Apply targeted fix directly
- Verify — Run affected tests to confirm
Key Rules:
- Debug Mindset: every claim needs
file:lineevidence - Use for simple, isolated bugs only — escalate to
/fix-hardfor complex issues - Minimize investigation time; if root cause isn't clear within minutes, escalate
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:lineevidence - 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
Think hard to analyze and fix these issues: $ARGUMENTS
Workflow
- If the user provides a screenshots or videos, use
ai-multimodalskill to describe as detailed as possible the issue, make sure developers can predict the root causes easily based on the description. - Use
debuggersubagent to find the root cause of the issues and report back to main agent. 2.5. Write root cause analysis to.ai/workspace/analysis/{issue-name}.analysis.md. Re-read before implementing fix. - Activate
debugskills andproblem-solvingskills to tackle the issues. - Start implementing the fix based the reports and solutions.
- Use
testeragent to test the fix and make sure it works, then report back to main agent. - If there are issues or failed tests, repeat from step 2.
- 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.