Troubleshooting AI Agent Skills: A Debugging Story

February 18, 2026

Categories: tutorial, debugging, openclaw, tooling

This post was co-created with Clawsistant, my OpenClaw AI agent. Yes, an AI can debug AI agent issues too.

Debugging AI agents is... different. Sometimes the bug is obvious (a typo). Sometimes it's hidden in documentation that's months out of date. And sometimes, you need to trace through multiple layers of skill loading to find the problem.

Here's the story of two debugging adventures I had recently — and what I learned from them.

Bug #1: The Binary Search That Wasn't Working

It started with a simple request: "Can you check this binary search implementation?"

The code looked reasonable at first glance. But when tested, it returned wrong results. Time to debug.

The Problem

Here's the buggy code (simplified):

def binary_search(arr, target):
    low = 0
    high = len(arr) - 1
    
    while low <= high:
        mid = (low + high) // 2
        if arr[mid] = target:  # BUG: assignment, not comparison!
            return mid
        elif arr[mid] < target:
            low = mid
        else:
            high = mid
    
    return -1

Spot it? The line if arr[mid] = target: uses a single = (assignment) instead of == (comparison). This is a classic mistake — and Python doesn't catch it because it's syntactically valid.

The Fix

def binary_search(arr, target):
    low = 0
    high = len(arr) - 1
    
    while low <= high:
        mid = (low + high) // 2
        if arr[mid] == target:  # Fixed!
            return mid
        elif arr[mid] < target:
            low = mid + 1  # Fixed! was just 'mid'
        else:
            high = mid - 1  # Fixed! was just 'mid'
    
    return -1

The Test Suite

To verify the fix, I created a comprehensive test suite:

import unittest

class TestBinarySearch(unittest.TestCase):
    def test_basic(self):
        self.assertEqual(binary_search([1,2,3,4,5], 3), 2)
    
    def test_not_found(self):
        self.assertEqual(binary_search([1,2,3], 4), -1)
    
    def test_first_element(self):
        self.assertEqual(binary_search([1,2,3,4,5], 1), 0)
    
    def test_last_element(self):
        self.assertEqual(binary_search([1,2,3,4,5], 5), 4)
    
    def test_empty(self):
        self.assertEqual(binary_search([], 1), -1)

if __name__ == '__main__':
    unittest.main()

Writing tests isn't just for "production" code — it's essential for debugging and verifying your fixes.

Bug #2: The consult_experts Skill That Wouldn't Load

Next up: debugging why the consult_experts skill wasn't working.

The Setup

The skill is supposed to let an AI agent ask other AI agents for help. I tried to use it to verify if a number was prime, but it kept failing.

The Investigation

1. Found the skill location: /home/ubuntu/.openclaw/workspace/skills/consult_experts/
2. Read the SKILL.md: It listed model IDs like openrouter/tngtech/deepseek-r1t2-chimera:free
3. Tested the script: Failed with "invalid model ID" errors
4. Checked actual API: The model IDs in documentation were outdated

The Fix

Update the skill documentation with correct model IDs. But this raised a bigger question: how do we keep skill documentation in sync with reality?

Key Debugging Takeaways

Principle	Application
Start simple	Check for typos first (= vs ==)
Read the docs	But verify they're up to date
Test your fixes	Write a test suite
Think systemically	Fix the root cause, not just symptoms
Document your findings	Update SKILL.md or create a new one

What's Next?

Debugging is an essential skill for working with AI agents. Whether it's a simple typo or outdated documentation, the methodology is the same:

1. Reproduce the bug
2. Isolate the problem
3. Fix it
4. Test the fix
5. Document what you learned

Now I need to actually update that consult_experts skill documentation. Stay tuned!