bug-fix

Bugfix - Automated Bug-Fixing Workflow

Coordinates specialist agents through a bug-fixing cycle: clarify bug -> reproduce with failing test -> diagnose root cause -> implement fix -> verify -> review -> document.

Workflow

1. Clarify the bug

Gather bug details from user:

• What are the symptoms? (error messages, unexpected behavior, crashes)
• What is the expected behavior vs actual behavior?
• How do you reproduce it? (steps, inputs, configuration)
• When did it start? Any recent changes that might be related?
• Environment specifics? (OS, versions, configuration)

Ask clarifying questions if needed:

• Ambiguous symptoms
• Missing reproduction steps
• Unclear expected behavior

Loop back to user until the bug is clearly understood. You need enough information to write a test that demonstrates the failure.

2. Write Failing Test(s)

Detect project language/framework:

• Check for language-specific files (go.mod, package.json, Cargo.toml, Dockerfile, Makefile, etc.)
• Determine which specialist agent to use

Spawn appropriate SME agent:

• If Golang project: spawn swe-sme-golang agent
• If GraphQL schema/resolvers: spawn swe-sme-graphql agent
• If Dockerfile changes: spawn swe-sme-docker agent
• If Makefile changes: spawn swe-sme-makefile agent
• If Ansible playbooks/roles: spawn swe-sme-ansible agent
• If Zig project: spawn swe-sme-zig agent
• If TypeScript project: spawn swe-sme-typescript agent
• If JavaScript project: spawn swe-sme-javascript agent
• If HTML/markup changes: spawn swe-sme-html agent
• If CSS/styling changes: spawn swe-sme-css agent
• Otherwise: write tests directly with general best practices

SME agent task:

• Write test(s) that reproduce the reported bug
• Tests should encode the expected behavior (so they fail against the current buggy behavior)
• Run the tests and verify they actually fail
• If the tests pass (bug cannot be reproduced): report back with findings — the bug may be environmental, already fixed, or misunderstood

This is the contract. When these tests pass, the bug is fixed.

If reproduction fails:

• Return to step 1 for more information from the user
• Report what was tried and why reproduction failed
• Max 2 reproduction attempts before escalating to user with findings

3. Diagnosis

Spawn swe-bug-investigator agent:

• Pass: bug description, failing test(s) from step 2, reproduction results
• Agent performs root-cause analysis:
  • Traces execution paths through the code
  • Git archaeology at agent's discretion (shallow for obvious bugs, deep for systemic ones)
  • Identifies the root cause with supporting evidence
  • Identifies related failure modes that may share the same root cause
• Agent produces a structured diagnosis report

Diagnosis report includes:

• Root cause (immediate and underlying)
• Evidence (code paths, git history, test results)
• Recommended fix approach
• Related failure modes (specific, actionable — not vague possibilities)
• Confidence level

4. Implement the Fix

Re-invoke the same SME agent from step 2:

• Pass the diagnosis report as guidance
• SME implements the fix following the diagnostician's recommended approach
• SME writes additional tests for related failure modes identified in step 3
• SME verifies: the originally-failing test(s) from step 2 now pass
• SME runs the full test suite to check for regressions

Implementation agent responsibilities:

• Fix the bug as narrowly and precisely as possible
• Don't refactor unrelated code (that's a separate workflow)
• Write tests for each related failure mode the diagnostician identified
• Ensure all new and existing tests pass

5. Quality Assurance - Verify Fix (CRITICAL GATE)

Spawn qa-engineer agent:

• Pass: original bug description, reproduction steps, and acceptance criteria (the bug should no longer occur)
• The agent infers its mode from context: presence of acceptance criteria triggers acceptance verification
• Agent performs practical verification first — actually reproducing the originally-reported scenario and confirming it's fixed
• This means: executing CLI commands, spawning subagents to test MCP tools, making API calls, etc.
• Only AFTER practical verification confirms the fix works does the agent run the full test suite
• Agent also checks the related failure modes identified by the diagnostician

Practical verification by feature type:

• CLI tools: Run commands in subshell (skip destructive operations)
• MCP servers/Claude skills: Spawn subagent to actually use the feature
• API integrations: Make actual calls (with caution for dangerous operations)
• Libraries: Quick sanity checks, then unit tests

This is a CRITICAL GATE:

• The originally-reported bug must be demonstrably fixed (practical test) AND tests must pass to proceed
• If practical verification fails: return to step 4 (implementation) immediately
• If verification passes but tests fail: debug tests, not implementation
• Track iteration count (max 3 attempts before escalating to user)

Expected output: Practical verification results, pass/fail determination with specific findings.

6. Code Review (Conditional)

Only proceed to code review if fix verification passed. Don't review broken code.

Conditionally invoke specialized reviewers based on code changes and complexity. All reviewers provide feedback; implementation agent responds to all feedback in step 7.

6a. Security Review (Conditional - Has Authority)

If security-sensitive code changed (auth, crypto, input validation, data access):

Spawn sec-blue-teamer agent:

• Evaluates defensive security posture of changed code
• Checks control consistency, defense-in-depth, and configuration
• Identifies systemic gaps that enable vulnerability classes
• Has authority to demand changes - security issues should block or require explicit user approval

Output: Defense evaluation with severity levels (critical/high/low)

6b. Refactoring Review (Conditional - Advisory)

If non-trivial implementation (>50 lines changed, multiple files, or complex logic):

Spawn swe-code-reviewer agent:

• Reviews implementation for code quality issues
• Identifies refactoring opportunities (DRY violations, dead code, complexity, etc.)
• Provides structured recommendations organized by priority
• Agent will report "No refactoring needed" if code is already clean

Output: Refactoring recommendations (advisory only)

6c. Performance Review (Conditional - Advisory)

If performance-critical code changed (hot paths, loops, database queries, API endpoints):

Spawn swe-perf-reviewer agent:

• Reviews code for performance bottlenecks
• Identifies missing benchmarks and profiling gaps
• Provides optimization recommendations

Output: Performance metrics and recommendations (advisory only)

7. Implement Review Feedback

Aggregate all review feedback from step 6 and provide to implementation agent.

If specialist was used in steps 2/4 (swe-sme-golang, swe-sme-graphql, etc.):

• Re-invoke the same specialist agent
• Provide all review feedback (security, refactoring, performance)
• Specialist reviews all feedback and uses own discretion to implement
• Security findings: Must address or get explicit user approval to skip
• Other feedback: Advisory only - specialist has final authority
• Specialist may decline recommendations that conflict with language idioms
• Specialist implements accepted changes and commits

If no specialist (general implementation):

• Re-invoke the same agent that did implementation in step 4
• Provide all review feedback
• Agent addresses security issues and implements accepted recommendations
• Agent commits changes

Note: Security issues have authority to block; other feedback is advisory. Implementation agent makes final decisions on advisory feedback.

8. SME Peer Review (Conditional)

Condition: Non-trivial changes (>50 lines changed, multiple files, or complex logic).

Skip if:

• Changes are trivial (single file, <50 lines, simple logic)
• No language-specific SME was used in step 2 (general implementation)

If applicable, spawn a fresh instance of the same SME type used in step 2:

• Fresh context window - reviewer sees only current code state via git diff
• Reviews with "would I accept this PR" lens
• Focuses on: minor oversights, idiomatic patterns, naming, small cleanups
• Has authority to make small fixes directly

Scope constraints (critical):

• Small cleanups and nits only
• No architectural changes
• No re-implementing features
• No changing approaches or patterns chosen by original SME
• If something seems wrong at a deeper level, flag it rather than fix it

Expected output: Small fixes committed, or "no changes needed" if code is clean.

9. Quality Assurance - Coverage & Quality Verification

Spawn qa-engineer agent:

• The agent infers its mode from context: absence of acceptance criteria triggers general assessment
• Agent performs general QA assessment of all changed code (from steps 4, 7, and 8)
• Assess test coverage for new/modified code
• Identify gaps and write missing tests
• Run linters/formatters and fix issues
• Evaluate test quality (assertions, edge cases)
• Verify implementations from steps 7 and 8 didn't break tests - catches issues from refactoring, security fixes, or peer review cleanups

This is SUPPLEMENTARY:

• Issues found here don't block the workflow by default
• Agent documents issues and proposes fixes
• Minor issues: proceed to documentation
• Significant issues (tests broken, major bugs): return to step 7 (review feedback) or step 4 (implementation)

Expected output: Coverage metrics, tests added/modified, quality issues found, confirmation that all changes still pass tests.

Feedback Loops

Reproduction failure (step 2):

• Return to step 1 (clarification)
• Report what was tried and why reproduction failed
• Max 2 reproduction attempts before escalating to user

Fix verification failure (step 5):

• Return to step 4 (implementation)
• Track iteration count (max 3 attempts before escalating to user)
• Report specific failures (which tests still fail, what practical verification showed)

Security review findings (step 6a):

• Critical/High severity: Must address in step 7 or get explicit user approval to skip
• Medium/Low severity: Advisory - implementation agent decides in step 7

Code review feedback (steps 6b-6c):

• All refactoring and performance feedback is advisory
• Implementation agent in step 7 uses discretion to implement

Peer review issues (step 8):

• If peer review changes break tests: revert peer review changes and proceed (nits aren't worth breaking functionality)
• If peer review flags deeper issues: escalate to user for decision

Coverage & quality issues (step 9):

• Tests broken by changes from step 7: Return to step 7 to fix or revert
• Tests broken by changes from step 8: Revert step 8 changes (peer review nits shouldn't break tests)
• Major bugs found: Return to step 4 (implementation)
• Minor issues (low coverage in non-critical code, style nitpicks): document and proceed
• User can approve proceeding despite issues if acceptable

10. Documentation

Spawn doc-maintainer agent:

• Review git diff to identify which files/modules changed
• Determine which documentation is affected:
  • README.md if user-facing functionality changed
  • CLAUDE.md if architecture/integration points changed
  • API docs if public interfaces changed
  • Internal docs (doc/, adr/) if implementation details relevant to architecture changed
• Focus review on documentation related to changed code
• Verify code examples in affected docs still work
• Check for broken links in modified documentation

Agent should be targeted, not comprehensive:

• Only review documentation relevant to the changes made
• Skip full codebase documentation scan
• Report "no documentation changes needed" if changes are purely internal with no doc impact

11. Final verification

Run comprehensive checks:

• Execute full test suite (must pass)
• Run all linters and formatters
• Verify no new warnings introduced
• Check git status (document any uncommitted changes)

Present summary to user:

• Bug that was fixed (original report)
• Root cause identified (from diagnosis)
• Files changed
• Tests added/modified (regression tests for the bug + related failure modes)
• Any remaining TODOs or follow-ups

If verification fails:

• Determine root cause (implementation bug vs. refactoring issue vs. test issue)
• Return to appropriate step (step 4 for implementation bugs, step 7 for refactoring issues)
• Report specific failures to user

12. Workflow Completion (Optional)

After presenting summary from step 11, ask user if they want to complete the workflow.

User may also explicitly request workflow completion at any point after step 11 succeeds.

If user confirms, perform housekeeping tasks:

12a. Commit Changes

If uncommitted changes exist:

• Review git status to see what changed
• Create commit with descriptive message
• If ticket number is known (from branch name, prior context, or user input):
  • Reference ticket in commit message (e.g., "Fixes #123" or "Closes: #123")
• Use heredoc format for multi-line commit messages

Example commit message format:

Fix off-by-one error in pagination boundary check

Root cause: boundary comparison used < instead of <= when calculating
the last page, causing the final item to be excluded from results.
Also added regression tests for adjacent edge cases identified during
diagnosis.

Fixes #456

12b. Update/Close Issue Tracker Ticket (Conditional)

Detect issue tracker:

• Check for platform integration (CLI, MCP server, or API)
• Skip if not available

Determine ticket number:

• Try to extract from current branch name (e.g., fix/456-pagination-bug -> #456)
• Check commit messages for ticket references
• Ask user for ticket number if not found and they indicated a ticket exists

If ticket number identified:

• Ask user: "Do you want to update and/or close ticket #456?"
  • Options: "Update only", "Update and close", "Skip"

If user chooses to update:

• Post comment with summary:
  • Root cause identified
  • Fix applied
  • Tests added (regression + related failure modes)
  • Files changed
• Use available integration to comment on issue

If user chooses to close:

• Close the ticket with final comment
• Use available integration to close issue

12c. Sync with Main Branch (Conditional)

Check branch state:

• Identify main branch name (master or main)
• Check if current branch is not main
• Check if there are new commits on main that current branch doesn't have

If current branch is behind main:

• Ask user: "Your branch is behind main. Would you like to rebase on main?"
• If user confirms:
  • Perform: git fetch origin main && git rebase origin/main
  • If conflicts arise, report to user and halt (don't auto-resolve)
  • If rebase succeeds, report success

Skip if:

• Current branch is main/master
• Current branch is up to date with main
• No main/master branch exists

Present final status:

• Changes committed
• Ticket updated/closed
• Branch rebased on main
• Ready to push or move to next task

Agent Coordination

Sequential execution:

• Agents run one at a time (no parallel spawning)
• Each agent completes before next begins
• Use Task tool to spawn agents

State management:

• Track current workflow step
• Track reproduction attempt count (for step 2 failures)
• Track QA iteration count (for fix verification failures)
• Accumulate summary information
• Preserve original bug report for passing to QA verification
• Preserve diagnosis report for passing to implementation agent

Agent self-skipping:

• Agents should skip work if not applicable
• Report "nothing to do" rather than making unnecessary changes

SME continuity:

• The same SME type is used across steps 2 (failing test), 4 (fix), 7 (review feedback), and 8 (peer review)
• Steps 2 and 4 should ideally be the same instance or carry forward context
• Step 8 (peer review) must be a fresh instance for independent perspective

Diagnosis pass-through:

• The diagnostician's report from step 3 is passed to the SME in step 4
• The SME should acknowledge the diagnosis and follow its recommended approach
• The SME should write tests for each related failure mode the diagnostician identified

Iteration Limits

Maximum iterations:

• Reproduction loop (step 2): 2 attempts, then escalate to user
• Fix verification loop (step 5): 3 attempts, then escalate to user
• Overall workflow: 12 total agent spawns (safety limit)

Escalation to user:

• Present current state
• Explain what's blocking progress
• Request guidance