/smart-fix
Intelligent issue resolution with multi-agent debugging, root cause analysis, and verified fix implementation
$ npx -y skills add wshobson/agents --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/smart-fix
Context preview
What this command does when you run it.
Intelligent issue resolution with multi-agent debugging, root cause analysis, and verified fix implementation
Command definition
smart-fix.mddescription: "Intelligent issue resolution with multi-agent debugging, root cause analysis, and verified fix implementation"
argument-hint: "<issue description> [--verification minimal|standard|comprehensive] [--prevention none|immediate|comprehensive]"
Intelligent Issue Resolution Orchestrator
CRITICAL BEHAVIORAL RULES
You MUST follow these rules exactly. Violating any of them is a failure.
1. **Execute steps in order.** Do NOT skip ahead, reorder, or merge steps. 2. **Write output files.** Each step MUST produce its output file in `.smart-fix/` before the next step begins. Read from prior step files — do NOT rely on context window memory. 3. **Stop at checkpoints.** When you reach a `PHASE CHECKPOINT`, you MUST stop and wait for explicit user approval before continuing. Use the AskUserQuestion tool with clear options. 4. **Halt on failure.** If any step fails (agent error, test failure, missing dependency), STOP immediately. Present the error and ask the user how to proceed. Do NOT silently continue. 5. **Use only local agents.** All `subagent_type` references use agents bundled with this plugin or `general-purpose`. No cross-plugin dependencies. 6. **Never enter plan mode autonomously.** Do NOT use EnterPlanMode. This command IS the plan — execute it.
Pre-flight Checks
Before starting, perform these checks:
1. Check for existing session
Check if `.smart-fix/state.json` exists:
- If it exists and `status` is `"in_progress"`: Read it, display the current step, and ask the user:
Found an in-progress smart-fix session:
Issue: [issue from state]
Current step: [step from state]
1. Resume from where we left off
2. Start fresh (archives existing session)
- If it exists and `status` is `"complete"`: Ask whether to archive and start fresh.
2. Initialize state
Create `.smart-fix/` directory and `state.json`:
{
"issue": "$ARGUMENTS",
"status": "in_progress",
"verification_level": "standard",
"prevention_focus": "immediate",
"current_step": 1,
"current_phase": 1,
"completed_steps": [],
"files_created": [],
"started_at": "ISO_TIMESTAMP",
"last_updated": "ISO_TIMESTAMP"
}Parse `$ARGUMENTS` for `--verification` and `--prevention` flags. Use defaults if not specified.
3. Parse issue description
Extract the issue description from `$ARGUMENTS` (everything before the flags). This is referenced as `$ISSUE` in prompts below.
---
Phase 1: Issue Analysis (Steps 1-2)
Step 1: Error Detection and Context Gathering
Use the Task tool to launch the error detective agent:
Task:
subagent_type: "incident-response-error-detective"
description: "Analyze error context for: $ISSUE"
prompt: |
Analyze error traces, logs, and observability data for: $ISSUE
Deliverables:
1. Error signature analysis: exception type, message patterns, frequency, first occurrence
2. Stack trace deep dive: failure location, call chain, involved components
3. Reproduction steps: minimal test case, environment requirements, data fixtures needed
4. Observability context:
- Sentry/DataDog error groups and trends
- Distributed traces showing request flow (OpenTelemetry/Jaeger)
- Structured logs (JSON logs with correlation IDs)
- APM metrics: latency spikes, error rates, resource usage
5. User impact assessment: affected user segments, error rate, business metrics impact
6. Timeline analysis: when did it start, correlation with deployments/config changes
7. Related symptoms: similar errors, cascading failures, upstream/downstream impacts
Modern debugging techniques to employ:
- AI-assisted log analysis (pattern detection, anomaly identification)
- Distributed trace correlation across microservices
- Production-safe debugging (no code changes, use observability data)
- Error fingerprinting for deduplication and tracking
Provide structured output with: ERROR_SIGNATURE, FREQUENCY, FIRST_SEEN, STACK_TRACE,
REPRODUCTION, OBSERVABILITY_LINKS, USER_IMPACT, TIMELINE, RELATED_ISSUES.Save the agent's output to `.smart-fix/01-error-analysis.md`.
Update `state.json`: set `current_step` to 2, add step 1 to `completed_steps`.
Step 2: Root Cause Identification
Read `.smart-fix/01-error-analysis.md` to load error context.
Use the Task tool to launch the debugger agent:
Task:
subagent_type: "incident-response-debugger"
description: "Identify root cause for: $ISSUE"
prompt: |
Perform root cause investigation using error-detective output:
Context from Error-Detective:
[Insert full contents of .smart-fix/01-error-analysis.md]
Deliverables:
1. Root cause hypothesis with supporting evidence
2. Code-level analysis: variable states, control flow, timing issues
3. Git bisect analysis: identify introducing commit (automate with git bisect run)
4. Dependency analysis: version conflicts, API changes, configuration drift
5. State inspection: database state, cache state, external API responses
6. Failure mechanism: why does the code fail under these specific conditions
7. Fix strategy options with tradeoffs (quick fix vs proper fix)
Context needed for next phase:
- Exact file paths and line numbers requiring changes
- Data structures or API contracts affected
- Dependencies that may need updates
- Test scenarios to verify the fix
- Performance characteristics to maintain
Provide structured output with: ROOT_CAUSE, INTRODUCING_COMMIT, AFFECTED_FILES,
FAILURE_MECHANISM, DEPENDENCIES, FIX_STRATEGY, TESTING_REQUIREMENTS.Save the agent's output to `.smart-fix/02-root-cause.md`.
Update `state.json`: set `current_step` to "checkpoint-1", add step 2 to `completed_steps`.
---
PHASE CHECKPOINT 1 — User Approval Required
You MUST stop here and present findings for review.
Display a summary from `.smart-fix/01-error-analysis.md` and `.smart-fix/02-root-cause.md`
Read more
description: "Intelligent issue resolution with multi-agent debugging, root cause analysis, and verified fix implementation" argument-hint: "<issue description> [--verification minimal|standard|comprehensive] [--prevention none|immediate|comprehensive]"
Intelligent Issue Resolution Orchestrator
CRITICAL BEHAVIORAL RULES
You MUST follow these rules exactly. Violating any of them is a failure.
1. **Execute steps in order.** Do NOT skip ahead, reorder, or merge steps. 2. **Write output files.** Each step MUST produce its output file in `.smart-fix/` before the next step begins. Read from prior step files — do NOT rely on context window memory. 3. **Stop at checkpoints.** When you reach a `PHASE CHECKPOINT`, you MUST stop and wait for explicit user approval before continuing. Use the AskUserQuestion tool with clear options. 4. **Halt on failure.** If any step fails (agent error, test failure, missing dependency), STOP immediately. Present the error and ask the user how to proceed. Do NOT silently continue. 5. **Use only local agents.** All `subagent_type` references use agents bundled with this plugin or `general-purpose`. No cross-plugin dependencies. 6. **Never enter plan mode autonomously.** Do NOT use EnterPlanMode. This command IS the plan — execute it.
Pre-flight Checks
Before starting, perform these checks:
1. Check for existing session
Check if `.smart-fix/state.json` exists:
- If it exists and `status` is `"in_progress"`: Read it, display the current step, and ask the user:
Found an in-progress smart-fix session: Issue: [issue from state] Current step: [step from state] 1. Resume from where we left off 2. Start fresh (archives existing session)
- If it exists and `status` is `"complete"`: Ask whether to archive and start fresh.
2. Initialize state
Create `.smart-fix/` directory and `state.json`:
{
"issue": "$ARGUMENTS",
"status": "in_progress",
"verification_level": "standard",
"prevention_focus": "immediate",
"current_step": 1,
"current_phase": 1,
"completed_steps": [],
"files_created": [],
"started_at": "ISO_TIMESTAMP",
"last_updated": "ISO_TIMESTAMP"
}Parse `$ARGUMENTS` for `--verification` and `--prevention` flags. Use defaults if not specified.
3. Parse issue description
Extract the issue description from `$ARGUMENTS` (everything before the flags). This is referenced as `$ISSUE` in prompts below.
---
Phase 1: Issue Analysis (Steps 1-2)
Step 1: Error Detection and Context Gathering
Use the Task tool to launch the error detective agent:
Task:
subagent_type: "incident-response-error-detective"
description: "Analyze error context for: $ISSUE"
prompt: |
Analyze error traces, logs, and observability data for: $ISSUE
Deliverables:
1. Error signature analysis: exception type, message patterns, frequency, first occurrence
2. Stack trace deep dive: failure location, call chain, involved components
3. Reproduction steps: minimal test case, environment requirements, data fixtures needed
4. Observability context:
- Sentry/DataDog error groups and trends
- Distributed traces showing request flow (OpenTelemetry/Jaeger)
- Structured logs (JSON logs with correlation IDs)
- APM metrics: latency spikes, error rates, resource usage
5. User impact assessment: affected user segments, error rate, business metrics impact
6. Timeline analysis: when did it start, correlation with deployments/config changes
7. Related symptoms: similar errors, cascading failures, upstream/downstream impacts
Modern debugging techniques to employ:
- AI-assisted log analysis (pattern detection, anomaly identification)
- Distributed trace correlation across microservices
- Production-safe debugging (no code changes, use observability data)
- Error fingerprinting for deduplication and tracking
Provide structured output with: ERROR_SIGNATURE, FREQUENCY, FIRST_SEEN, STACK_TRACE,
REPRODUCTION, OBSERVABILITY_LINKS, USER_IMPACT, TIMELINE, RELATED_ISSUES.Save the agent's output to `.smart-fix/01-error-analysis.md`.
Update `state.json`: set `current_step` to 2, add step 1 to `completed_steps`.
Step 2: Root Cause Identification
Read `.smart-fix/01-error-analysis.md` to load error context.
Use the Task tool to launch the debugger agent:
Task:
subagent_type: "incident-response-debugger"
description: "Identify root cause for: $ISSUE"
prompt: |
Perform root cause investigation using error-detective output:
Context from Error-Detective:
[Insert full contents of .smart-fix/01-error-analysis.md]
Deliverables:
1. Root cause hypothesis with supporting evidence
2. Code-level analysis: variable states, control flow, timing issues
3. Git bisect analysis: identify introducing commit (automate with git bisect run)
4. Dependency analysis: version conflicts, API changes, configuration drift
5. State inspection: database state, cache state, external API responses
6. Failure mechanism: why does the code fail under these specific conditions
7. Fix strategy options with tradeoffs (quick fix vs proper fix)
Context needed for next phase:
- Exact file paths and line numbers requiring changes
- Data structures or API contracts affected
- Dependencies that may need updates
- Test scenarios to verify the fix
- Performance characteristics to maintain
Provide structured output with: ROOT_CAUSE, INTRODUCING_COMMIT, AFFECTED_FILES,
FAILURE_MECHANISM, DEPENDENCIES, FIX_STRATEGY, TESTING_REQUIREMENTS.Save the agent's output to `.smart-fix/02-root-cause.md`.
Update `state.json`: set `current_step` to "checkpoint-1", add step 2 to `completed_steps`.
---
PHASE CHECKPOINT 1 — User Approval Required
You MUST stop here and present findings for review.
Display a summary from `.smart-fix/01-error-analysis.md` and `.smart-fix/02-root-cause.md`
Production-ready agentic workflow building blocks: 94 plugins, 203 agents, 175 skills, 109 commands — built for Claude Code and consumed natively by OpenAI Codex CLI, Cursor, OpenCode, Gemini CLI, and GitHub Copilot from a single Markdown source.
Repo: wshobson/agents
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