debugging-and-error-recovery
Helps your agent systematically investigate the root cause of problems. It helps fix failures in tests, builds, or unexpected behaviors.
What it does
This guide helps you find and fix computer program errors in an organized way. It teaches you to stop, investigate the root cause, and then fix it, avoiding guesswork.
When to use it
- ✓When a test fails after a change.
- ✓When the program stops working unexpectedly.
- ✓When you don't know why something went wrong.
Example of what to ask your AI
“My code is giving a strange error I can't figure out, can you help me investigate?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It suggests following steps like reproducing the error, finding where it happens, simplifying the problem, and then fixing the real cause. Finally, it asks to add a test to prevent the error from returning.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill debugging-and-error-recoveryOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/debugging-and-error-recovery/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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 4-6 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, sleep) to widen race windows
│ └── Run under load or concurrency to increase collision probability
├── Environment-dependent?
│ ├── Compare Node/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
├── Set up an alert for the specific error signature
└── Document the conditions observed and revisit when it recurs
For test failures:
# Run the specific failing test
npm test -- --grep "test name"
# Run with verbose output
npm test -- --verbose
# Run in isolation (rules out test pollution)
npm test -- --testPathPattern="specific-file" --runInBand
Step 2: Localize
Narrow down WHERE the failure happens:
Which layer is failing?
├── UI/Frontend → Check console, DOM, network tab
├── API/Backend → Check server logs, request/response
├── Database → Check queries, schema, data integrity
├── Build tooling → Check config, dependencies, environment
├── External service → Check connectivity, API changes, rate limits
└── Test itself → Check if the test is correct (false negative)
Use bisection for regression bugs:
# Find which commit introduced the bug
git bisect start
git bisect bad # Current commit is broken
git bisect good <known-good-sha> # This commit worked
# Git will checkout midpoint commits; run your test at each
git bisect run npm test -- --grep "failing test"
Step 3: Reduce
Create the minimal failing case:
- Remove unrelated code/config until only the bug remains
- Simplify the input to the smallest example that triggers the failure
- Strip the test to the bare minimum that reproduces the issue
A minimal reproduction makes the root cause obvious and prevents fixing symptoms instead of causes.
Step 4: Fix the Root Cause
Fix the underlying issue, not the symptom:
Symptom: "The user list shows duplicate entries"
Symptom fix (bad):
→ Deduplicate in the UI component: [...new Set(users)]
Root cause fix (good):
→ The API endpoint has a JOIN that produces duplicates
→ Fix the query, add a DISTINCT, or fix the data model
Ask: "Why does this happen?" until you reach the actual cause, not just where it manifests.
Step 5: Guard Against Recurrence
Write a test that catches this specific failure:
// The bug: task titles with special characters broke the search
it('finds tasks with special characters in title', async () => {
…
FAQ
What does 'preserve evidence' mean?
It means saving important information about the error, like error messages, logs, or the exact steps that led to the problem, before making any other changes.
What is 'bisection'?
It's a technique to find which specific change caused a problem by comparing old and new versions of the code until you pinpoint where the error appeared.
Why shouldn't I just fix the symptom?
Fixing the symptom is like plugging a leak without addressing the cause. The problem might return or cause other errors. It's better to find and fix the root cause for a permanent solution.
Details
Original description by the author
Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2739
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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