debug-ops
Systematic debugging methodology, language-specific debuggers, and common scenario playbooks. Use for: debug, debugging, bug, crash, hang, memory leak, race condition, deadlock, bisect, reproduce, root cause, breakpoint, profiling, performance issue, segfault, stack trace, core dump.
Best use case
debug-ops is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Systematic debugging methodology, language-specific debuggers, and common scenario playbooks. Use for: debug, debugging, bug, crash, hang, memory leak, race condition, deadlock, bisect, reproduce, root cause, breakpoint, profiling, performance issue, segfault, stack trace, core dump.
Teams using debug-ops should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/debug-ops/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How debug-ops Compares
| Feature / Agent | debug-ops | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Systematic debugging methodology, language-specific debuggers, and common scenario playbooks. Use for: debug, debugging, bug, crash, hang, memory leak, race condition, deadlock, bisect, reproduce, root cause, breakpoint, profiling, performance issue, segfault, stack trace, core dump.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Debug Operations
Systematic debugging methodology with language-specific tooling and common scenario playbooks.
## Bug Classification Decision Tree
```
Bug Report / Symptom
│
├─ Crash
│ ├─ Segfault / Access Violation
│ │ └─ Check: null pointer, buffer overflow, use-after-free, stack overflow
│ ├─ Panic / Fatal Error
│ │ └─ Check: assertion failure, unrecoverable state, out-of-memory
│ └─ Unhandled Exception
│ └─ Check: missing error handler, unexpected input type, network failure
│
├─ Hang
│ ├─ Deadlock
│ │ └─ Check: lock ordering, mutex contention, channel blocking
│ ├─ Infinite Loop
│ │ └─ Check: loop termination condition, counter overflow, recursive call
│ └─ Blocked I/O
│ └─ Check: network timeout, DNS resolution, disk full, file lock
│
├─ Wrong Output
│ ├─ Logic Error
│ │ └─ Check: operator precedence, boundary conditions, boolean logic
│ ├─ Data Corruption
│ │ └─ Check: concurrent mutation, encoding mismatch, truncation
│ └─ Off-by-One
│ └─ Check: loop bounds, array indexing, fence-post errors
│
├─ Performance
│ ├─ Slow Queries
│ │ └─ Check: missing index, N+1 queries, full table scan, lock wait
│ ├─ Memory Bloat
│ │ └─ Check: cache without eviction, leaked references, large allocations
│ └─ CPU Spikes
│ └─ Check: hot loops, regex backtracking, excessive GC, busy-wait
│
└─ Intermittent
├─ Race Condition
│ └─ Check: shared mutable state, read-modify-write, check-then-act
├─ Timing-Dependent
│ └─ Check: timeout values, clock skew, event ordering assumptions
└─ Environment-Specific
└─ Check: OS differences, locale, timezone, file system case sensitivity
```
## Systematic Debugging Workflow
Six-step process from symptom to prevention:
### Step 1: Reproduce
Confirm the bug exists and create a reliable reproduction. A bug you cannot reproduce is a bug you cannot confidently fix. Capture exact inputs, environment, and sequence of operations.
### Step 2: Isolate
Narrow the fault to the smallest possible scope. Use binary search (git bisect, commenting out code halves), stubs, feature flags, and environment isolation to eliminate innocent code.
### Step 3: Identify
Find the root cause, not just the proximate trigger. Use the 5 Whys technique, trace execution, inspect state at key points. Distinguish between the symptom and the underlying defect.
### Step 4: Fix
Apply the minimal correct change that addresses the root cause. Avoid shotgun debugging (changing multiple things at once). Understand why the fix works, not just that it works.
### Step 5: Verify
Confirm the fix resolves the original issue without introducing regressions. Re-run the original reproduction case. Run the full test suite. Test edge cases related to the fix.
### Step 6: Prevent
Add a regression test. Update documentation or runbooks if applicable. Consider whether the same class of bug could exist elsewhere. Share findings with the team.
## Reproduction Checklist
```
[ ] Minimal reproduction steps documented (numbered, unambiguous)
[ ] Environment captured (OS, runtime version, dependencies, config)
[ ] Exact inputs recorded (request payload, CLI args, file contents)
[ ] Timing sensitivity assessed (does it fail only under load? after delay?)
[ ] Single-threaded reproduction attempted (eliminates concurrency noise)
[ ] Reproduction automated as script or test case
[ ] Confirmed reproduction is deterministic (fails N/N attempts)
[ ] Identified whether reproduction requires specific data/state
```
## Isolation Techniques Quick Reference
| Technique | Method | Best For |
|-----------|--------|----------|
| **Binary search (git)** | `git bisect start BAD GOOD` then `git bisect run ./test.sh` | Finding which commit introduced the bug |
| **Binary search (code)** | Comment out half the code, test, repeat | Narrowing fault location in unfamiliar code |
| **Stubs/Mocks** | Replace dependencies with known-good fakes | Isolating from external services |
| **Feature flags** | Toggle features off one by one | Finding which feature causes the issue |
| **Environment isolation** | Docker container, fresh VM, clean install | Eliminating environment contamination |
| **Network interception** | mitmproxy, Charles Proxy, mock server | Isolating client vs server issues |
| **Input reduction** | Remove input fields/data until bug disappears | Finding minimal trigger |
| **Dependency pinning** | Lock all deps, update one at a time | Finding breaking dependency update |
## Root Cause Analysis Template
### 5 Whys Example
```
Problem: API returns 500 error on user login
1. Why? → The database query throws a timeout exception
2. Why? → The users table scan takes >30 seconds
3. Why? → There is no index on the email column
4. Why? → The migration that adds the index was never run in production
5. Why? → The deployment script skips migrations when the --fast flag is used
Root cause: Deployment script's --fast flag bypasses migrations
Fix: Remove --fast flag behavior that skips migrations, add migration check to health endpoint
Prevention: CI check that verifies all migrations are applied after deployment
```
### Fault Tree Basics
```
[System Failure]
/ \
[Hardware] [Software]
/ \ / \
[Disk] [Memory] [Config] [Code Bug]
| |
[Missing [Race in
env var] worker pool]
```
Work from the top (observed failure) down to leaves (root causes). Each branch is an AND/OR gate -- AND means all children must be true, OR means any one child suffices.
## Language-Specific Debugger Quick Reference
| Language | Tool | Launch Command | Key Commands |
|----------|------|----------------|--------------|
| **Node.js** | Chrome DevTools | `node --inspect-brk app.js` | Open `chrome://inspect`, set breakpoints in Sources |
| **Node.js** | ndb | `npx ndb app.js` | Enhanced DevTools with blackboxing |
| **Python** | pdb | `python -m pdb script.py` | `n` next, `s` step, `c` continue, `p expr` print, `bt` backtrace |
| **Python** | debugpy | `python -m debugpy --listen 5678 --wait-for-client script.py` | VS Code "Attach" launch config |
| **Python** | breakpoint() | Insert `breakpoint()` in code | Drops into pdb at that line (Python 3.7+) |
| **Go** | Delve | `dlv debug ./cmd/server` | `b main.go:42` break, `c` continue, `n` next, `p var` print |
| **Go** | Delve (test) | `dlv test ./pkg/...` | Debug test functions directly |
| **Go** | Delve (attach) | `dlv attach PID` | Debug running process |
| **Rust** | rust-gdb | `rust-gdb target/debug/myapp` | `b main`, `r`, `n`, `p variable`, `bt` |
| **Rust** | rust-lldb | `rust-lldb target/debug/myapp` | `b s main`, `r`, `n`, `p variable`, `bt` |
| **Rust** | CodeLLDB | VS Code extension | GUI breakpoints, variable inspection |
| **Browser** | DevTools | F12 or Ctrl+Shift+I | Elements, Console, Network, Sources, Performance, Memory |
### Quick Debug Snippets
```javascript
// Node.js: drop into debugger at this point
debugger;
// Node.js: conditional breakpoint
if (user.id === 'problem-user') debugger;
```
```python
# Python: drop into debugger at this point
breakpoint()
# Python: conditional breakpoint
if user_id == 'problem-user':
breakpoint()
```
```go
// Go: print goroutine stacks (send SIGQUIT or SIGABRT)
// kill -QUIT <pid>
// Or in code:
import "runtime/debug"
debug.PrintStack()
```
```rust
// Rust: enable full backtraces
// RUST_BACKTRACE=1 cargo run
// RUST_BACKTRACE=full cargo run
```
## Log-Based Debugging Patterns
### Strategic Logging
Place logs at decision points, not just error paths:
```
[ENTRY] function_name(args_summary) -- entering the function
[STATE] key_variable=value -- state at critical decision point
[BRANCH] taking path X because Y -- which branch and why
[EXIT] function_name -> result_summary -- leaving the function
[ERROR] operation failed: detail -- error with context
```
### Correlation IDs
Trace a single request across services:
```bash
# Generate at entry point, propagate through all calls
X-Request-ID: 550e8400-e29b-41d4-a716-446655440000
# Search across all service logs
rg "550e8400-e29b-41d4-a716-446655440000" /var/log/services/
```
### Timeline Reconstruction
```bash
# Merge and sort logs from multiple sources by timestamp
sort -t' ' -k1,2 service-a.log service-b.log service-c.log > timeline.log
# Find gaps in activity (potential hang/block)
awk '{print $1, $2}' timeline.log | uniq -c | sort -rn | head -20
```
### Structured Log Queries
```bash
# jq queries on JSON logs
# Find all errors for a specific user
jq 'select(.level == "error" and .user_id == "u123")' app.log
# Get timing distribution for slow requests
jq 'select(.duration_ms > 1000) | .duration_ms' app.log | sort -n
# Count errors by type
jq -r 'select(.level == "error") | .error_type' app.log | sort | uniq -c | sort -rn
```
## Common Gotchas
| Gotcha | Why It Hurts | Fix |
|--------|-------------|-----|
| Fixing symptoms, not root cause | Bug resurfaces in a different form | Use 5 Whys to dig deeper |
| Debugging in production without safety net | Risk of data loss or extended outage | Use read-only queries, feature flags, canary deploys |
| Heisenbug (disappears under observation) | Adding logging/breakpoints changes timing | Use non-invasive tools: `strace`, sampling profiler, `rr` |
| Assumption bias ("it can't be X") | Skipping the actual cause because you trust it | Test every assumption explicitly, even "obvious" ones |
| Missing reproduction case | Cannot verify fix, cannot prevent regression | Invest time upfront in reliable reproduction |
| Over-relying on print/log debugging | Slow iteration, pollutes code, misses concurrency bugs | Use proper debugger, profiler, or tracing tool |
| Not checking recent changes | The answer is often in the last few commits | `git log --oneline -20`, `git diff HEAD~5` |
| Ignoring warning messages | Warnings often predict the error that follows | Treat warnings as errors during debugging |
| Debugging wrong version/branch | Wasting time on already-fixed or different code | Verify `git branch`, `git log -1`, runtime version |
| Not reading the full stack trace | Root cause is often in the middle, not the top | Read bottom-up: find your code in the trace first |
| Changing multiple things at once | Cannot tell which change fixed (or broke) it | One change per test cycle |
| Not capturing the "before" state | Cannot diff against working baseline | Snapshot config, deps, data before debugging |
## Reference Files
| File | Contents | Lines |
|------|----------|-------|
| `references/systematic-methods.md` | Scientific method, binary search, delta debugging, differential debugging, time-travel debugging, team debugging | ~600 |
| `references/tool-specific.md` | Browser DevTools, Node.js, Python, Go, Rust, database, network, Docker debugging tools | ~650 |
| `references/common-scenarios.md` | Memory leaks, deadlocks, race conditions, performance regressions, API debugging, deployment issues | ~550 |
## See Also
- **testing-ops** -- Write tests to prevent bugs from recurring
- **security-ops** -- Security-specific debugging (auth failures, injection, CSRF)
- **monitoring-ops** -- Production observability, alerting, dashboards
- **code-stats** -- Measure code complexity and identify bug-prone areas
- **container-orchestration** -- Docker and Kubernetes debugging context
- **git-ops** -- Git bisect workflow and history investigation