Agent skill

bugs

Bug hunting with Codex CLI Use when: (1) /bugs is invoked, (2) task relates to bugs functionality.

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npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/bugs

SKILL.md

/bugs (v2.37)

Deep bug analysis using Codex gpt-5.2-codex with the bug-hunter skill and TLDR context optimization.

Pre-Bugs: TLDR Context Preparation (v2.37)

AUTOMATIC - Before bug hunting, gather context with 95% token savings:

bash
# Get function signatures and call flow
tldr context "$TARGET_FILE" . > /tmp/bugs-context.md

# Get dependency graph for tracking bug propagation
tldr deps "$TARGET_FILE" . > /tmp/bugs-deps.md

# Get codebase structure for understanding module relationships
tldr structure . > /tmp/bugs-structure.md

# Semantic search for error handling patterns
tldr semantic "try catch error exception throw" .

Overview

The /bugs command performs comprehensive static analysis using TLDR-compressed context to identify potential bugs, logic errors, race conditions, edge cases, and other code issues that could cause runtime failures or unexpected behavior. It uses Codex GPT-5.2 model with specialized bug-hunting capabilities to analyze code paths, detect anti-patterns, and suggest fixes.

Unlike traditional linters, Codex bug hunting performs deep semantic analysis:

  • Context-aware: Understands code intent and business logic
  • Multi-file analysis: Traces bugs across module boundaries
  • Pattern recognition: Identifies common bug patterns and anti-patterns
  • Fix suggestions: Provides actionable remediation steps

When to Use

Use /bugs when:

  • Investigating mysterious test failures or production issues
  • Auditing newly merged code for potential issues
  • Debugging complex interactions between modules
  • Preparing critical code paths for production deployment
  • Reviewing legacy code for modernization
  • Searching for edge cases before stress testing
  • Performing pre-merge quality checks (complexity >= 7)

Analysis Methodology

Codex bug hunting follows a systematic approach:

  1. Static Analysis: Parse AST and control flow graphs
  2. Pattern Matching: Compare against known bug patterns database
  3. Semantic Understanding: Analyze code intent and data flow
  4. Edge Case Detection: Identify boundary conditions and error paths
  5. Severity Assessment: Classify bugs by impact and probability
  6. Fix Generation: Propose concrete remediation steps

Bug Categories

Category Examples Severity
Logic Errors Off-by-one, incorrect conditions, wrong operators HIGH
Race Conditions Unprotected shared state, TOCTOU bugs HIGH
Memory Issues Leaks, use-after-free, buffer overflows CRITICAL
Type Errors Implicit conversions, type coercion bugs MEDIUM
Error Handling Uncaught exceptions, missing null checks HIGH
Edge Cases Empty arrays, boundary values, overflow MEDIUM
Async Issues Unhandled promises, callback hell, deadlocks HIGH
Security Bugs Injection, XSS, CSRF (see /security for full audit) CRITICAL

CLI Execution

bash
# Bug hunt on specific file
ralph bugs src/auth/login.ts

# Bug hunt on directory
ralph bugs src/components/

# Bug hunt on entire codebase
ralph bugs .

# Background execution with logging
ralph bugs src/ > bugs-report.json 2>&1 &

Task Tool Invocation (TLDR-Enhanced)

Use the Task tool to invoke Codex bug hunting with TLDR context:

yaml
Task:
  subagent_type: "debugger"
  model: "sonnet"
  run_in_background: true
  description: "Codex bug hunting analysis"
  prompt: |
    # Context (95% token savings via tldr)
    Structure: $(tldr structure .)
    File Context: $(tldr context $ARGUMENTS .)
    Dependencies: $(tldr deps $ARGUMENTS .)

    Execute Codex bug hunting via CLI:
    cd /Users/alfredolopez/Documents/GitHub/multi-agent-ralph-loop && \
    codex exec --yolo --enable-skills -m gpt-5.2-codex \
    "Use bug-hunter skill. Find bugs in: $ARGUMENTS

    Output JSON: {
      bugs: [
        {
          severity: 'CRITICAL|HIGH|MEDIUM|LOW',
          type: 'logic|race|memory|type|error-handling|edge-case|async|security',
          file: 'path/to/file.ts',
          line: 42,
          description: 'Clear bug description',
          fix: 'Concrete remediation steps'
        }
      ],
      summary: {
        total: 5,
        high: 2,
        medium: 2,
        low: 1,
        approved: false
      }
    }"

    Apply Ralph Loop: iterate until all HIGH+ bugs are resolved or approved.

Direct Codex Execution

For immediate results without Task orchestration:

bash
codex exec --yolo --enable-skills -m gpt-5.2-codex \
  "Use bug-hunter skill. Find bugs in: src/

  Focus on:
  - Race conditions in async code
  - Uncaught promise rejections
  - Type coercion issues
  - Edge case handling

  Output JSON with severity, type, file, line, description, fix"

Output Format

The bug hunting analysis returns structured JSON:

json
{
  "bugs": [
    {
      "severity": "HIGH",
      "type": "race",
      "file": "src/auth/session.ts",
      "line": 87,
      "description": "Race condition: session.user accessed before async initialization completes",
      "fix": "Add await before accessing session.user, or use Promise.all() to ensure initialization"
    },
    {
      "severity": "MEDIUM",
      "type": "edge-case",
      "file": "src/utils/parser.ts",
      "line": 23,
      "description": "Empty array not handled: arr[0] will throw if arr is empty",
      "fix": "Add guard: if (arr.length === 0) return null; before accessing arr[0]"
    }
  ],
  "summary": {
    "total": 2,
    "high": 1,
    "medium": 1,
    "low": 0,
    "approved": false
  }
}

Severity Levels

Severity Meaning Action
CRITICAL Production-breaking, security issues MUST FIX before merge
HIGH Likely to cause failures, data corruption SHOULD FIX before merge
MEDIUM Edge cases, potential issues under load Review and decide
LOW Code smells, minor improvements Optional fix

Integration

The /bugs command integrates with other Ralph workflows:

With @debugger Agent

yaml
Task:
  subagent_type: "debugger"
  model: "opus"  # Opus for deep analysis
  description: "Full debugging workflow"
  prompt: |
    1. Run /bugs on $TARGET
    2. Analyze top 5 HIGH severity bugs
    3. Trace execution paths to root cause
    4. Propose fixes with test cases
    5. Validate fixes pass quality gates

With /adversarial

When a bug fix needs a clarified spec:

bash
# Step 1: Bug hunting
ralph bugs src/payment/

# Step 2: Draft a short spec for the fix
ralph adversarial "Draft: Fix payment retry logic with idempotency"

With /unit-tests

Generate tests that specifically target discovered bugs:

yaml
Task:
  subagent_type: "test-architect"
  model: "sonnet"
  prompt: |
    Read bugs-report.json
    For each HIGH/CRITICAL bug:
    - Write failing test that reproduces bug
    - Verify test fails before fix
    - Apply fix from bug report
    - Verify test passes after fix

    Use TDD pattern: RED → FIX → GREEN

Related Commands

Command Purpose When to Use
/security Security-focused audit (CWE checks) Before production deploy
/unit-tests Generate test coverage After bug fixes
/refactor Improve code structure After identifying patterns
/adversarial Adversarial spec refinement Critical code paths
/full-review Comprehensive analysis (6 agents) Major features/releases

Ralph Loop Integration

The /bugs command follows the Ralph Loop pattern with these hooks:

┌─────────────────────────────────────────────────────────┐
│ RALPH LOOP: Bug Hunting                                 │
├─────────────────────────────────────────────────────────┤
│                                                         │
│ 1. EXECUTE   → codex exec bug-hunter                    │
│ 2. VALIDATE  → Check severity counts                    │
│ 3. ITERATE   → Fix HIGH+ bugs                           │
│ 4. VERIFY    → Re-run until summary.approved = true     │
│                                                         │
│ Quality Gate: No HIGH+ bugs OR all explicitly approved  │
│ Max Iterations: 15 (Codex GPT-5.2)                      │
│                                                         │
└─────────────────────────────────────────────────────────┘

Approval Criteria

The bug hunting loop continues until:

  • Zero HIGH+ bugs detected, OR
  • All HIGH+ bugs explicitly approved by user with justification
  • Quality gates pass (no new bugs introduced by fixes)

Example Workflow

Full bug hunting and remediation workflow:

bash
# 1. Initial bug scan
ralph bugs src/

# 2. Review report
cat ~/.ralph/tmp/codex_bugs.json | jq '.summary'

# 3. Fix HIGH severity bugs
# (manual or via /refactor)

# 4. Verify fixes
ralph bugs src/  # Should show reduced bug count

# 5. Generate regression tests
ralph unit-tests src/

# 6. Run quality gates
ralph gates

# 7. Final approval (if LOW bugs remain)
# Add to bugs-report.json: "approved": true, "justification": "Low risk edge cases"

Best Practices

  1. Run before merge: Always scan critical paths before PR approval
  2. Prioritize HIGH+: Focus on CRITICAL and HIGH severity first
  3. Fix root causes: Don't just patch symptoms
  4. Add tests: Every fixed bug needs a regression test
  5. Track patterns: If same bug type appears multiple times, refactor pattern
  6. Combine with /security: Bug hunting finds logic errors, security finds vulnerabilities
  7. Use Opus for critical: Switch to --model opus for payment/auth/crypto code

Cost Optimization

Model Cost Speed When to Use
GPT-5.2-Codex ~15% Fast Default for bug hunting
Opus 100% Slow Critical code paths
Sonnet 60% Medium Task orchestration only

Recommended: Codex GPT-5.2 for bug hunting (optimized for code analysis)

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