# Troubleshooting AI Agent Skills: A Debugging Story

URL: https://anyech.github.io/jingxiao-cai-blog/troubleshooting-ai-agent-skills.html
Markdown mirror: https://anyech.github.io/jingxiao-cai-blog/troubleshooting-ai-agent-skills.html.md
Date: 2026-02-18
Tags: ai-agents, troubleshooting

Summary: Common issues and solutions when working with AI agent skills.

---

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# 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 Sometimes it's hidden in documentation that's months out of date.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
```


 **Lesson #1:** In binary search, always update boundaries with `mid + 1` or `mid - 1`. Otherwise you can get stuck in an infinite loop or skip elements.



### 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



- **Found the skill location:** `/home/ubuntu/.openclaw/workspace/skills/consult_experts/`

- **Read the SKILL.md:** It listed model IDs like `openrouter/tngtech/deepseek-r1t2-chimera:free`

- **Tested the script:** Failed with "invalid model ID" errors

- **Checked actual API:** The model IDs in documentation were outdated



 **The Problem:** Skill documentation had outdated model IDs. The actual available models were different from what the docs claimed.



### 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?*


 **Better solution:** Add automatic model discovery to the skill script, so it queries the API at runtime instead of hardcoding model IDs.



## 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:



- Reproduce the bug

- Isolate the problem

- Fix it

- Test the fix

- Document what you learned


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



### Related Posts



- [Setting Up Google APIs for Self-Hosted AI Agents](/jingxiao-cai-blog/google-api-setup-guide.html) — Another technical deep dive

- [Getting OneDrive Working with Self-Hosted AI Agents](/jingxiao-cai-blog/onedrive-multcloud-story.html) — More troubleshooting adventures






### About the Author

 **Jingxiao Cai** is a Principal Member of Technical Staff with a background in distributed ML runtime systems. PhD in Radar Signal Processing from University of Oklahoma. Previously worked on backend/runtime systems for production ML workloads.






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