Systematic Debugging

Systematic Debugging

Overview

Random fixes waste time and create new bugs. Quick patches mask underlying issues.

Core principle: ALWAYS find root cause before attempting fixes. Symptom fixes are failure.

Violating the letter of this process is violating the spirit of debugging.

The Iron Law

NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST

If you haven't completed Phase 1, you cannot propose fixes.

When to Use

Use for ANY technical issue:

• Test failures
• Bugs in production
• Unexpected behavior
• Performance problems
• Build failures
• Integration issues
• Local reverse-proxy / strangler routing problems (client → nginx-strangler →

multiple backends)

Use this ESPECIALLY when:

• Under time pressure (emergencies make guessing tempting)
• "Just one quick fix" seems obvious
• You've already tried multiple fixes
• Previous fix didn't work
• You don't fully understand the issue

Don't skip when:

• Issue seems simple (simple bugs have root causes too)
• You're in a hurry (rushing guarantees rework)
• Someone wants it fixed NOW (systematic is faster than thrashing)

The Four Phases

You MUST complete each phase before proceeding to the next.

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Phase 1: Root Cause Investigation

BEFORE attempting ANY fix:

1. Read Error Messages Carefully

• Don't skip past errors or warnings
• They often contain the exact solution
• Read stack traces completely
• Note line numbers, file paths, error codes

Action: Use read_file on the relevant source files. Use search_files to find the error string in the codebase.

1b. Plan before changing anything

For live infrastructure failures, write a short diagnostic plan before making changes.

• Name the exact failing component.
• Identify the minimum comparison set (usually one healthy node or one working

workload).

• Decide what evidence will confirm or reject the hypothesis.
• Only then proceed to a fix.

This prevents cluster debugging from turning into broad manifest thrashing.

2. Reproduce Consistently

• Can you trigger it reliably?
• What are the exact steps?
• Does it happen every time?
• If not reproducible → gather more data, don't guess

Action: Use the terminal tool to run the failing test or trigger the bug:

# Run specific failing test
pytest tests/test_module.py::test_name -v

# Run with verbose output
pytest tests/test_module.py -v --tb=long

3. Check Recent Changes

• What changed that could cause this?
• Git diff, recent commits
• New dependencies, config changes

Action:

# Recent commits
git log --oneline -10

# Uncommitted changes
git diff

# Changes in specific file
git log -p --follow src/problematic_file.py | head -100

4. Gather Evidence in Multi-Component Systems

WHEN system has multiple components (API → service → database, CI → build → deploy):

BEFORE proposing fixes, add diagnostic instrumentation:

For EACH component boundary:

• Log what data enters the component
• Log what data exits the component
• Verify environment/config propagation
• Check state at each layer

Run once to gather evidence showing WHERE it breaks. THEN analyze evidence to identify the failing component. THEN investigate that specific component.

5. Trace Data Flow

WHEN error is deep in the call stack:

• Where does the bad value originate?
• What called this function with the bad value?
• Keep tracing upstream until you find the source
• Fix at the source, not at the symptom

Action: Use search_files to trace references:

# Find where the function is called
search_files("function_name(", path="src/", file_glob="*.py")

# Find where the variable is set
search_files("variable_name\\s*=", path="src/", file_glob="*.py")

Phase 1 Completion Checklist

• [ ] Error messages fully read and understood
• [ ] Issue reproduced consistently
• [ ] Recent changes identified and reviewed
• [ ] Evidence gathered (logs, state, data flow)
• [ ] Problem isolated to specific component/code
• [ ] Root cause hypothesis formed

STOP: Do not proceed to Phase 2 until you understand WHY it's happening.

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Phase 2: Pattern Analysis

Find the pattern before fixing:

1. Find Working Examples

• Locate similar working code in the same codebase
• What works that's similar to what's broken?

Action: Use search_files to find comparable patterns:

search_files("similar_pattern", path="src/", file_glob="*.py")

2. Compare Against References

• If implementing a pattern, read the reference implementation COMPLETELY
• Don't skim — read every line
• Understand the pattern fully before applying

3. Identify Differences

• What's different between working and broken?
• List every difference, however small
• Don't assume "that can't matter"

4. Understand Dependencies

• What other components does this need?
• What settings, config, environment?
• What assumptions does it make?

---

Phase 3: Hypothesis and Testing

Scientific method:

1. Form a Single Hypothesis

• State clearly: "I think X is the root cause because Y"
• Write it down
• Be specific, not vague

2. Test Minimally

• Make the SMALLEST possible change to test the hypothesis
• One variable at a time
• Don't fix multiple things at once

3. Verify Before Continuing

• Did it work? → Phase 4
• Didn't work? → Form NEW hypothesis
• DON'T add more fixes on top

4. When You Don't Know

• Say "I don't understand X"
• Don't pretend to know
• Ask the user for help
• Research more

---

Phase 4: Implementation

Fix the root cause, not the symptom:

1. Create Failing Test Case

• Simplest possible reproduction
• Automated test if possible
• MUST have before fixing
• Use the test-driven-development skill

2. Implement Single Fix

• Address the root cause identified
• ONE change at a time
• No "while I'm here" improvements
• No bundled refactoring

3. Verify Fix

# Run the specific regression test
pytest tests/test_module.py::test_regression -v

# Run full suite — no regressions
pytest tests/ -q

4. If Fix Doesn't Work — The Rule of Three

• STOP.
• Count: How many fixes have you tried?
• If prisma generate`, suspect a shared Prisma client output path rather

than a code regression. See references/prisma-workspace-race.md for the reproduction and the root cause pattern.

Kubernetes / RKE2 Node Debugging

When a cluster add-on fails with artifact pulls, registry timeouts, DNS errors, or pod startup loops on an RKE2 node, do not jump straight to CNI blame. First separate host reachability from pod reachability, then read the RKE2 journal for the earliest fatal error, then inspect node networking state and kubelet/containerd logs.

Useful split:

• host DNS / egress works, but pod DNS fails: inspect CNI, CoreDNS,

NetworkPolicy, and pod-level DNS routing

• add-on pull timeouts plus

lookup github.com on []:53: server misbehaving: suspect pod DNS or CNI, not the registry itself

• rke2-canal/flannel crashes on one IP family while the node is otherwise

Ready: verify dual-stack CIDRs, node IPs, and CNI backend compatibility before resetting

Key pitfall: a visible Flux or Helm artifact timeout can be downstream of a node bootstrap error such as an IP-family mismatch (cluster-cidr vs node-ip) or a partially reconciled CNI setup. If the host can reach the registry but workloads cannot, investigate NetworkPolicy / pod egress / in-pod DNS before planning a cluster reset.

See references/rke2-flux-node-debugging.md for the command sequence and heuristics.

Real-World Impact

From debugging sessions:

• Systematic approach: 15-30 minutes to fix
• Random fixes approach: 2-3 hours of thrashing
• First-time fix rate: 95% vs 40%
• New bugs introduced: Near zero vs common

No shortcuts. No guessing. Systematic always wins.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: shikanime-labs
• Source: shikanime-labs/skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.