debugging-and-error-recovery

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# Debugging and Error Recovery

## Overview

Systematic debugging with structured triage. When something breaks, stop adding features, preserve evidence, and follow a structured process to find and fix the root cause. Guessing wastes time. The triage checklist works for test failures, build errors, runtime bugs, and production incidents.

## When to Use

- Tests fail after a code change
- The build breaks
- Runtime behavior doesn't match expectations
- A bug report arrives
- An error appears in logs or console
- Something worked before and stopped working

## The Stop-the-Line Rule

When anything unexpected happens:

```
1. STOP adding features or making changes
2. PRESERVE evidence (error output, logs, repro steps)
3. DIAGNOSE using the triage checklist
4. FIX the root cause
5. GUARD against recurrence
6. RESUME only after verification passes
```

**Don't push past a failing test or broken build to work on the next feature.** Errors compound. A bug in Step 3 that goes unfixed makes Steps 4-10 wrong.

## The Triage Checklist

Work through these steps in order. Do not skip steps.

### Step 1: Reproduce

Make the failure happen reliably. If you can't reproduce it, you can't fix it with confidence.

```
Can you reproduce the failure?
├── YES → Proceed to Step 2
└── NO
    ├── Gather more context (logs, environment details)
    ├── Try reproducing in a minimal environment
    └── If truly non-reproducible, document conditions and monitor
```

**When a bug is non-reproducible:**

```
Cannot reproduce on demand:
├── Timing-dependent?
│   ├── Add timestamps to logs around the suspected area
│   ├── Try with artificial delays (setTimeout, asyncio.sleep, time.sleep) to widen race windows
│   └── Run under load or concurrency to increase collision probability
├── Environment-dependent?
│   ├── Compare Node/Python/browser versions, OS, environment variables
│   ├── Check for differences in data (empty vs populated database)
│   └── Try reproducing in CI where the environment is clean
├── State-dependent?
│   ├── Check for leaked state between tests or requests
│   ├── Look for global variables, singletons, or shared caches
│   └── Run the failing scenario in isolation vs after other operations
└── Truly random?
    ├── Add defensive logging at the suspected location (console.error / logging.exception)
    ├── Set up an alert for the specific error signature
    └── Document the conditions observed and revisit when it recurs
```

For **JavaScript / TypeScript** test failures:

```bash
# Run a single failing test by name
npm test -- --grep "test name"
npx jest -t "test name"

# Verbose output
npm test -- --verbose

# Run in isolation (rules out test pollution)
npx jest --testPathPattern="specific-file" --runInBand
```

For **Python** test failures (pytest):

```bash
# Run a specific failing test by node id
pytest tests/tasks/test_task_service.py::test_complete_task_sets_completed_at

# Filter by keyword
pytest -k "complete_task and not legacy"

# Verbose, show locals on failure, stop on first failure
pytest -vv --showlocals -x

# Drop into the debugger at the point of failure
pytest --pdb

# Reproduce a flake by replaying the same random seed (with pytest-randomly)
pytest -p randomly --randomly-seed=12345
```

For **Python** runtime issues, drop a breakpoint and step through:

```bash
python -m pdb script.py
# Inside the program, use breakpoint() (Python 3.7+) at the suspected line
```

```python
# Inline breakpoint — modern equivalent of `import pdb; pdb.set_trace()`
def complete_task(task_id: str) -> Task:
    breakpoint()  # debugger stops here
    ...
```

### Step 2: Localize

Narrow down WHERE the failure happens:

From jankneumann/agentic-coding-tools, MIT.

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