debugging

name	debugging
description	Produces a Debug Report documenting root cause, fix applied, and lessons learned through systematic hypothesis testing. Use when: 'debug this error', 'why is this failing', 'investigate the issue', 'find the root cause', 'fix this bug systematically'.

Debugging & Troubleshooting Skills

Version: 1.0 Created: 2026-02-02 Author: Cipher Purpose: Systematic debugging and troubleshooting for code, systems, and workflows

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Overview

This skill encodes best practices for debugging and troubleshooting—isolating problems, identifying root causes, and implementing fixes methodically. It provides patterns for reading logs, reproducing issues, testing hypotheses, and verifying solutions.

Philosophy: Debugging is systematic investigation, not random guessing. One change at a time, test, observe, learn.

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When to Use This Skill

• Code behaves unexpectedly (errors, crashes, wrong output)
• Performance degradation (slow queries, long build times)
• Data inconsistency (database state out of sync)
• Integration failure (API returns errors, sync breaks)
• Feature not working as expected (UI bug, logic error)

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Core Principles

1. Debugging is Systematic, Not Random

Don't: Change 5 things at once, guess without evidence, apply fixes blindly, skip testing after fixing.

Do: Formulate hypothesis ("X causes Y"), test hypothesis, fix based on confirmed cause, re-test to verify.

2. Observe, Don't Assume

Observe: Error messages (exact text, stack traces), logs (timestamps, context, sequence), system state, user behavior.

Don't assume: "It's probably a caching issue" / "The user must have done X wrong" / "This library is broken" (without evidence).

3. Isolate Variables

When you have multiple factors, test them independently:

Factor	Result
Database query	FAIL — Timeout (4s)
Network request	PASS — Works
Frontend render	PASS — Works

Conclusion: Database query is bottleneck. Optimize query or increase timeout.

4. Reproduce Consistently

Before debugging: identify exact steps to reproduce, run 3+ times, note if intermittent, identify patterns if so.

Why: You can't fix what you can't reproduce.

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Debugging Workflow

Step 1: Gather Information (5-10 minutes)

Collect: Error messages (exact text, stack traces, error codes), logs, context (what changed recently?), expected vs. actual behavior, environment (OS, versions, dependencies).

Template:

## Debug: [Issue Title]

**Error Message:** [Exact error text]
**Stack Trace:** [Copy relevant portion]
**Logs:** [Key log lines with timestamps]
**Context:** What triggered the issue / Recent changes / Environment (Node, DB, OS)
**Expected Behavior:** [What should happen]
**Actual Behavior:** [What actually happened]

Step 2: Formulate Hypothesis (2-5 minutes)

Ask: What could cause this? What's most likely? What evidence supports it?

Examples:

• "Database query timeout caused by missing index"
• "404 error caused by wrong file path in env var"
• "Sync fails because file encoding is wrong"

Prioritize: Most likely (70%) → Second most likely (30%) → Edge cases (10%)

Step 3: Test Hypothesis (5-15 minutes)

1. Verify directly — Add logging to confirm cause; run minimal reproduction case
2. Isolate variables — Remove dependencies, simplify data, change environment
3. Confirm fix — Apply targeted fix, re-run reproduction steps, verify resolved

Document results:

Hypothesis: [Description]
Test Method: [How tested]
Result: Confirmed / Rejected
Evidence: [What proved/disproved hypothesis]

Step 4: Implement Fix

If confirmed: Implement minimal fix, add error handling, add logging for future detection.
If rejected: Move to next hypothesis, re-gather information, consider broader systemic causes.

Step 5: Verify & Document (5-10 minutes)

Verify: Original reproduction steps no longer fail, edge cases work, no regressions.

Document:

## Fix: [Issue Title]

**Root Cause:** [What actually caused issue]
**Solution:** [What was fixed]
**Code Changes:** [File, function, or module changed]
**Testing:**
- Original issue: Resolved
- Edge cases: Tested
- Regression check: No regressions
**Lessons Learned:** [What to do differently in future]

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Common Debugging Patterns

Pattern 1: Read Logs Strategically

Don't read entire log files. Search for error codes or keywords, extract lines around error timestamp (±5 min), look for recurring patterns.

grep -i "error" app.log | tail -20
grep "2026-02-02T14:00" app.log -A 5 -B 5
grep -A 10 "Error:" app.log

Pattern 2: Use Logging to Verify

Point	Log What	Why
Before database query	Query string, parameters	See what's being executed
After database query	Rows returned, time taken	Performance check
Before API call	Request payload	Verify what's sent
After API call	Response status, body	Verify what's received
Before file write	File path, content	Verify what's written

When in doubt, add a log. You can always remove later.

Pattern 3: Binary Search for Root Cause

Narrow down variables systematically. For a slow query: test with 10 rows (rules out table size), then run EXPLAIN (reveals missing index), then add index and confirm fix. One variable at a time.

Pattern 4: Reproduce in Isolated Environment

When issue only happens in production: create minimal reproduction in dev, simplify data, mock external dependencies, test against same environment versions. Goal: isolate whether issue is data, code, or environment.

Pattern 5: Use Version Control for Time Travel

git log --oneline -10
git log --oneline --since="2026-02-02T13:00"
git bisect start
git bisect bad HEAD
git bisect good <last-working-commit>

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Troubleshooting Categories

1. Code Errors (Runtime, Compile, Type)

Causes: Null/undefined access, async timing issues, type mismatches, module not found
Approach: Read error carefully, check stack trace for file/line, search error code, isolate the function

2. Performance Issues

Causes: N+1 queries, slow renders, memory leaks, network latency
Approach: Profile the slow operation, identify bottleneck (database/CPU/I/O/network), optimize the bottleneck, measure before/after

3. Data Inconsistency

Causes: Database out of sync, cache invalidation, race conditions, migration not applied
Approach: Compare DB state vs. expected, check last sync timestamp, verify integrity, re-run sync or migration

4. Integration Failures

Causes: API contract mismatch, auth failures, network connectivity, service downtime
Approach: Verify request format, check credentials, test API directly (curl/Postman), check service status

5. Environment-Specific Issues

Causes: Missing/wrong env vars, path differences (Win/Mac/Linux), dependency version conflicts, file permissions
Approach: Compare working vs. broken env, check env vars, verify dependency versions, check paths and permissions

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Quality Checklist

Before considering debugging complete:

• Root cause identified (not just symptom fixed)
• Hypothesis tested with evidence
• Fix is minimal (don't over-engineer)
• Original issue reproduced before fix
• Fix resolves issue (not just hides it)
• Edge cases tested
• No regressions (other features still work)
• Root cause, solution, and lessons learned documented

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Common Pitfalls to Avoid

• Shotgun debugging — Change everything — one change, test, observe
• Ignoring error messages — "Probably network" — read exact error, search it
• Fixing without testing — Apply change, assume it works — reproduce, fix, re-test
• Assuming environment — "Works on my machine" — verify in actual environment
• Blaming external deps — "Library is broken" — verify your usage first

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Output

• A Debug Report markdown file saved to the project's working directory or docs/debug/
• Named: [date]_[issue-slug]_debug.md
• Sections: Error Message, Stack Trace, Root Cause, Solution, Code Changes, Test Results, Lessons Learned

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Examples

Scenario 1: "Debug this error: TypeError: Cannot read property 'id' of undefined" → Debug Report with hypothesis (null user object), test method (added logging), root cause confirmed, fix applied (null check), regression test documented

Scenario 2: "Why is this database query taking 8 seconds?" → Debug Report using binary search on query complexity, EXPLAIN output showing missing index, index added, before/after timing documented

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Edge Cases

• Error is intermittent (not reproducible): Add logging around the suspected area, capture the next occurrence, treat the log as the reproduction case
• Issue only happens in production: Create a minimal reproduction in dev using production-equivalent data and environment vars; document the environment delta
• Multiple simultaneous errors: Prioritize by severity — fix the error that is blocking other fixes first; do not debug all at once

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Anti-Patterns

• Changing multiple things at once before testing — makes it impossible to identify which change resolved the issue
• Searching for the error message online before reading the stack trace — the stack trace almost always locates the cause; search is for when the trace is unhelpful
• Marking a bug as fixed when tests pass but root cause was not confirmed — symptom masking produces the same bug later under different conditions
• Writing a fix that hides the error (empty catch, fallback default) instead of addressing the cause

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Related Skills:

• workspace-navigation — When debugging collaborative workspaces
• repo-context-sync — When debugging git integration issues
• web-research — When searching error codes or similar issues online
• research-modes — Deep investigation techniques

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Last Updated: 2026-04-08 Maintained By: Cipher Status: Active