Bug Killer

Bug Killer -- Hypothesis-Driven Debugging Workflow

Execute a systematic debugging workflow that enforces investigation before fixes. Every bug gets a hypothesis journal, evidence gathering, and root cause confirmation before any code changes.

Phase Overview

1. Triage and Reproduction -- Understand, reproduce, route to quick or deep track
2. Investigation -- Gather evidence with language-specific techniques
3. Root Cause Analysis -- Confirm root cause through hypothesis testing
4. Fix and Verify -- Fix with proof, regression test, quality check
5. Wrap-up and Report -- Document trail, capture learnings

Nested Agents

• bug-investigator (agents/bug-investigator.md) -- Executes diagnostic investigation tasks to test debugging hypotheses. Runs tests, traces execution, checks git history, and reports evidence. Used in Phase 3 (deep track).
• code-explorer (cross-reference: ../../core-tools/skills/deep-analysis/agents/code-explorer.md from the core-tools package) -- Read-only exploration agent for investigating codebase areas related to the bug. Used in Phase 2 (deep track).

Phase 1: Triage and Reproduction

Goal: Understand the bug, reproduce it, and decide the investigation track.

1.1 Parse Context

Extract from $ARGUMENTS and conversation context:

• Bug description: What's failing? Error messages, symptoms
• Reproduction steps: How to trigger the bug (test command, user action, etc.)
• Environment: Language, framework, test runner, relevant config
• Prior attempts: Has the user already tried fixes? What didn't work?
• Deep flag: If --deep is present, skip triage and go directly to deep track (jump to Phase 2 deep track)

1.2 Reproduce the Bug

Attempt to reproduce before investigating:

1. If a failing test was mentioned, run it:

   # Run the specific test to confirm the failure
   <test-runner> <test-file>::<test-name>
   

2. If an error was described, find and trigger it
3. If neither, search for related test files and run them

Capture the exact error output -- this is your primary evidence.

If the bug cannot be reproduced:

• Prompt the user for more context
• Check if it's environment-specific or intermittent
• Note "not yet reproduced" in the hypothesis journal

1.3 Form Initial Hypothesis

Based on the error message and context, form your first hypothesis:

### H1: [Title]
- Hypothesis: [What you think is causing the bug]
- Evidence for: [What supports this -- error message, stack trace, etc.]
- Evidence against: [Anything that contradicts it -- if none yet, say "None yet"]
- Test plan: [Specific steps to confirm or reject]
- Status: Pending

1.4 Route to Track

Quick-fix signals (ALL must be true):

• Clear, specific error message pointing to exact location
• Localized to 1-2 files (not spread across the codebase)
• Obvious fix visible from reading the error location
• No concurrency, timing, or state management involved

Deep-track signals (ANY one triggers deep track):

• Bug spans 3+ files or modules
• Root cause unclear from the error message alone
• Intermittent or environment-dependent failure
• Involves concurrency, timing, shared state, or async behavior
• User already tried fixes that didn't work
• Generic error message (e.g., "null reference" without clear origin)
• Stack trace points to library/framework code rather than application code

Present your assessment. Prompt the user to choose:

• Summarize the bug and your initial hypothesis
• Recommend quick or deep track with justification
• Options: "Quick track (Recommended)" / "Deep track" or "Deep track (Recommended)" / "Quick track" -- depending on your assessment
• Let the user override your recommendation

Track escalation rule: If during quick track execution, 2 hypotheses are rejected, automatically escalate to deep track. Preserve all hypothesis journal entries when escalating.

Phase 2: Investigation

Goal: Gather evidence systematically, guided by language-specific techniques.

2.1 Load Language Reference

Detect the primary language of the bug's context and load the appropriate reference:

Always also consult the General Debugging Reference as a supplement when using a language-specific reference.

2.2 Quick Track Investigation

For quick-track bugs, investigate directly:

1. Read the error location -- the file and function where the error occurs
2. Read the immediate callers -- 1-2 files up the call chain
3. Check recent changes -- git log --oneline -5 -- <file> for the affected files
4. Update hypothesis -- does the evidence support H1? Add evidence for/against

Proceed to Phase 3 (quick track).

2.3 Deep Track Investigation

For deep-track bugs, use parallel exploration agents:

1. Plan exploration areas -- identify 2-3 focus areas based on the bug:

   • Focus 1: The error site and immediate code path
   • Focus 2: Data flow and state management leading to the error
   • Focus 3: Related subsystems, configuration, or external dependencies

2. Launch exploration agents:

   This step uses the code-explorer agent from the deep-analysis skill in the core-tools package (see ../../core-tools/skills/deep-analysis/agents/code-explorer.md).

   Spawn 2-3 exploration agents with distinct focus areas:

   Prompt for each agent:
   Bug context: [description of the bug and error]
   Focus area: [specific area for this agent]
   
   Investigate this focus area in relation to the bug:
   - Find all relevant files
   - Trace the execution/data path
   - Identify where behavior diverges from expected
   - Note any suspicious patterns, recent changes, or known issues
   - Report structured findings
   

   Launch agents in parallel for independent focus areas.

3. Synthesize exploration results:

   • Collect findings from all agents
   • Identify convergence (multiple agents pointing to same area)
   • Update hypothesis journal with new evidence
   • Form additional hypotheses if evidence warrants (aim for 2-3 total)

Proceed to Phase 3 (deep track).

Phase 3: Root Cause Analysis

Goal: Confirm the root cause through systematic hypothesis testing.

3.1 Quick Track Root Cause

For quick-track bugs:

1. Verify the hypothesis:

   • Read the specific code identified in Phase 2
   • Trace the logic step-by-step
   • Confirm that the hypothesized cause produces the observed error

2. If confirmed (Status -> Confirmed):

   • Update H1 with confirming evidence
   • Proceed to Phase 4

3. If rejected (Status -> Rejected):

   • Update H1 with evidence against and reason for rejection
   • Form a new hypothesis (H2) based on what you learned
   • Investigate H2 following Phase 2 quick track steps
   • If H2 is also rejected, escalate to deep track
   • Preserve all journal entries, continue with Phase 2 deep track

3.2 Deep Track Root Cause

For deep-track bugs:

1. Prepare hypotheses for testing:

   • You should have 2-3 hypotheses from Phase 2
   • Each needs a concrete test plan (how to confirm or reject)

2. Launch investigator agents:

   Delegate to 1-3 investigator agents (see agents/bug-investigator.md for instructions) to test hypotheses in parallel:

   Prompt for each agent:
   Bug context: [description of the bug and error]
   
   Hypothesis to test: [specific hypothesis]
   Test plan:
   1. [Step 1 -- e.g., run this specific test with these arguments]
   2. [Step 2 -- e.g., check git blame for this function]
   3. [Step 3 -- e.g., trace the data from input to error site]
   
   Report your findings with verdict (confirmed/rejected/inconclusive),
   evidence, and recommendations.
   

   Launch agents in parallel when they test independent hypotheses.

3. Evaluate results:

   • Update hypothesis journal with each agent's findings

   • If one hypothesis is confirmed, proceed to Phase 4

   • If all are rejected/inconclusive, apply 5 Whys technique:

     Take the strongest "inconclusive" finding and ask "why?" iteratively:

     Observed: [what actually happens]
     Why? -> [first-level cause]
     Why? -> [second-level cause]
     Why? -> [root cause]
     

   • Form new hypotheses from 5 Whys analysis and repeat investigation

4. If stuck after 2 rounds of investigation:

   • Present all findings to the user
   • Share the hypothesis journal
   • Prompt the user to choose: "Continue investigating", "Try a different angle", "Provide more context"

Phase 4: Fix and Verify

Goal: Fix the root cause and prove the fix works.

4.1 Design the Fix

Before writing any code:

1. Explain the root cause -- state clearly what's wrong and why
2. Explain the fix -- describe what will change and WHY it addresses the root cause
3. Identify affected files -- list every file that needs modification
4. Consider side effects -- could this fix break other behavior?

4.2 Implement the Fix

1. Read all files that will be modified before making changes
2. Apply the fix -- minimal, focused changes
3. Match existing patterns -- follow the codebase's conventions

4.3 Run Tests

1. Run the originally failing test -- it should now pass:

   <test-runner> <test-file>::<test-name>
   

2. Run related tests -- tests in the same file and nearby test files:

   <test-runner> <test-directory>
   

3. If tests fail:

   • Determine if the failure is related to the fix or pre-existing
   • If related, revise the fix (do NOT revert to a different approach without updating the hypothesis journal)
   • If pre-existing, note it but don't let it block the fix

4.4 Write Regression Test

Write a test that would have caught this bug:

1. The test should fail WITHOUT the fix (verifying it tests the right thing)
2. The test should pass WITH the fix
3. The test should be minimal -- test the specific behavior that was broken
4. Place it in the appropriate test file following project conventions

4.5 Deep Track: Quality Check and Related Issues

Deep track only -- skip on quick track.

1. Load code-quality skill: Refer to the code-quality skill and review the fix against its principles.

2. Check for related issues:

   • Search for the same pattern elsewhere in the codebase
   • If the same bug exists in other locations, report them to the user
   • Prompt the user to choose: "Fix all related instances now?" / "Fix only the reported bug" / "Create tasks for related fixes"

Phase 5: Wrap-up and Report

Goal: Document the investigation trail and capture learnings.

5.1 Bug Fix Summary

Present to the user:

## Bug Fix Summary

### Bug
[One-line description of the bug]

### Root Cause
[What was actually wrong and why]

### Fix Applied
[What was changed, with file:line references]

### Tests
- [Originally failing test]: Now passing
- [Regression test added]: [test name and location]
- [Related tests]: All passing

### Track
[Quick / Deep] [Escalated from quick: Yes/No]

5.2 Hypothesis Journal Recap

Present the complete hypothesis journal showing the investigation trail:

### Investigation Trail

#### H1: [Title]
- Status: Confirmed / Rejected
- [Key evidence summary]

#### H2: [Title] (if applicable)
- Status: Confirmed / Rejected
- [Key evidence summary]

[... additional hypotheses ...]

5.3 Project Learnings

Refer to the project-learnings skill to evaluate whether this bug reveals project-specific knowledge worth capturing.

Follow its workflow to evaluate the finding. Common debugging discoveries that qualify:

• Surprising API behavior specific to this project
• Undocumented conventions that caused the bug
• Architectural constraints that aren't obvious from the code

5.4 Deep Track: Future Recommendations

Deep track only:

If the investigation revealed broader concerns, present recommendations:

• Architecture improvements to prevent similar bugs
• Missing test coverage areas
• Documentation gaps
• Monitoring or alerting suggestions

5.5 Next Steps

Prompt the user to choose:

• "Commit the fix" -- proceed to commit workflow
• "Review the changes" -- show a diff of all modifications
• "Run full test suite" -- run the complete test suite to verify no regressions
• "Done" -- wrap up the session

Hypothesis Journal

The hypothesis journal is the core artifact of this workflow. Maintain it throughout all phases.

Format

## Hypothesis Journal -- [Bug Title]

### H1: [Descriptive Title]
- **Hypothesis:** [What's causing the bug -- be specific]
- **Evidence for:** [Supporting observations with file:line references]
- **Evidence against:** [Contradicting observations]
- **Test plan:** [Concrete steps to confirm or reject]
- **Status:** Pending / Confirmed / Rejected
- **Notes:** [Additional context, timestamps, agent findings]

### H2: [Descriptive Title]
[Same format]

Rules

• Minimum hypotheses: 1 on quick track, 2-3 on deep track
• Never delete entries -- rejected hypotheses are valuable context
• Update incrementally -- add evidence as you find it, don't wait
• Be specific -- "the data is wrong" is not a hypothesis; "processOrder receives dollars but expects cents" is

Track Reference

Error Recovery

If any phase fails:

1. Explain what went wrong and what you've learned so far
2. Present the hypothesis journal as-is
3. Prompt the user to choose:
   • "Retry this phase"
   • "Skip to fix" (if you have enough evidence)
   • "Provide more context"
   • "Abort"

General Debugging Reference

Language-agnostic debugging strategies, systematic investigation methods, and common bug categories.

Systematic Debugging Methods

Binary Search for Bugs

Narrow the problem space by half at each step:

1. Identify the full code path from input to incorrect output
2. Place a diagnostic check at the midpoint
3. Determine which half contains the bug
4. Repeat in the failing half until the exact location is found

Works for: data transformation pipelines, middleware chains, multi-step processes.

Git Bisect

Automate binary search through commit history:

git bisect start
git bisect bad                    # current commit is broken
git bisect good <known-good-sha> # this commit was working

# Automated: let a test command drive the search
git bisect run <test-command>     # returns 0 = good, non-0 = bad

git bisect reset                  # when done

Best for: regressions where you know "it used to work."

Delta Debugging

Minimize the input that triggers the bug:

1. Start with the full failing input
2. Remove half the input -- does it still fail?
3. If yes, keep the smaller input and repeat
4. If no, restore and try removing the other half
5. Continue until you find the minimal failing case

Works for: large inputs, complex configurations, test case reduction.

Rubber Duck Debugging

Explain the problem out loud (or in writing), step by step:

1. State what the code is supposed to do
2. Walk through the actual execution, line by line
3. At each step, explain what the state should be vs. what it is
4. The discrepancy often reveals itself during the explanation

5 Whys

Drill past symptoms to root causes:

Bug: Users see a 500 error on checkout
Why? -> The payment API call throws a timeout
Why? -> The request takes >30 seconds
Why? -> The order total calculation is O(n^2)
Why? -> It recalculates item prices for each item pair
Why? -> The discount logic compares every item against every other item
Root cause: Quadratic discount calculation algorithm

Stop when the answer is something you can directly fix.

Reading Stack Traces

Universal Patterns

What Stack Traces Cannot Tell You

• Why the wrong value got there (you need to trace backwards)
• When the state became corrupted (may have happened much earlier)
• Where in async code the real problem is (async gaps in traces)
• Whether a caught-and-rethrown error lost its original context

Investigation Strategy

1. Read the error message first -- it often contains the key clue
2. Find your code in the trace (skip framework/library frames)
3. Read the immediate caller -- what arguments were passed?
4. Check the state at that point -- are variables what you expect?
5. Trace backwards from the error to where the data originated

Bug Categories

Off-by-One Errors

Symptoms: Missing first/last element, array index out of bounds, fencepost errors.

Check for:

• < vs <= in loop conditions
• 0-based vs 1-based indexing confusion
• Inclusive vs exclusive range boundaries
• Empty collection edge case (length 0)

Null/Undefined/None Errors

Symptoms: Null reference exceptions, "undefined is not a function," AttributeError.

Check for:

• Uninitialized variables
• Missing return values (functions that implicitly return null/undefined)
• Optional fields accessed without guards
• API responses with unexpected null fields
• Database queries returning no results

Race Conditions

Symptoms: Intermittent failures, works in debugger but fails normally, order-dependent results.

Check for:

• Shared mutable state accessed concurrently
• Missing locks/synchronization
• Read-then-write without atomicity
• Callback ordering assumptions
• File system operations assuming sequential access

Resource Leaks

Symptoms: Slow degradation, eventual crashes, "too many open files," memory growth.

Check for:

• File handles not closed (missing close() / with / using)
• Database connections not returned to pool
• Event listeners added but never removed
• Timers/intervals not cleared
• Temporary files not cleaned up

State Corruption

Symptoms: Inconsistent data, works sometimes but not always, cascade of errors after a specific action.

Check for:

• Mutation of shared objects
• Missing deep copies (aliased references)
• Partial updates (crash between related writes)
• Cache invalidation issues
• Global/singleton state modified by multiple code paths

Diagnostic Logging Strategy

Targeted Logging

Log at decision points and data boundaries:

[ENTRY] function_name called with: key_arg=value
[BRANCH] taking path X because condition=value
[DATA] received from external: summary_of_data
[EXIT] function_name returning: summary_of_result

Logging Anti-Patterns

Effective Diagnostic Pattern

1. Before the suspected area: Log inputs and state
2. At decision points: Log which branch was taken and why
3. After the suspected area: Log outputs and state
4. Compare: Are the inputs/outputs what you expect at each point?

Investigation Checklist

Before proposing a fix, verify you can answer:

Understanding the Bug

• Can you reproduce the bug reliably?
• What is the expected behavior vs actual behavior?
• When did this start happening? (regression or latent bug?)
• Does it happen always, sometimes, or in specific conditions?

Root Cause Identification

• Have you identified the specific line(s) causing the issue?
• Do you understand WHY those lines produce the wrong result?
• Is this the root cause, or a symptom of a deeper issue?
• Could this same root cause affect other code paths?

Fix Validation

• Does the fix address the root cause, not just the symptom?
• Could the fix introduce new bugs? (side effects, changed behavior)
• Are there existing tests that should have caught this?
• Does the fix handle all edge cases for this code path?

Broader Impact

• Are there similar patterns elsewhere that might have the same bug?
• Does the fix require updates to documentation or configuration?
• Could this be a regression? If so, what change introduced it?
• Is a new test needed to prevent this from recurring?