leeroo-ai KAPSO Bridge Skill
KAPSO is a knowledge-grounded optimization framework that iteratively improves code through experimentation.
When to Use
Use this skill when you need to:
- Optimize inefficient code (e.g., O(n²) → O(n))
- Fix bugs with verification
- Improve algorithm performance
- Generate optimized solutions
How to Use
1. Submit Optimization Request
curl -X POST http://${KAPSO_URL:-localhost:8000}/optimize \
-H "Content-Type: application/json" \
-d '{
"goal": "Describe what needs to be optimized",
"code": "def your_code(): pass",
"context": "Optional additional context"
}'
Response:
{
"job_id": "abc123",
"status": "running",
"thought_process": "Optimization started..."
}
2. Check Job Status
curl http://${KAPSO_URL:-localhost:8000}/status/{job_id}
Response when complete:
{
"job_id": "abc123",
"status": "completed",
"code": "def optimized_code(): ...",
"cost": "$0.042",
"thought_process": "KAPSO Optimization Complete..."
}
3. Health Check
curl http://${KAPSO_URL:-localhost:8000}/health
Example: Optimize O(n²) to O(n)
Input:
def find_duplicates(arr):
duplicates = []
for i in range(len(arr)):
for j in range(i + 1, len(arr)):
if arr[i] == arr[j] and arr[i] not in duplicates:
duplicates.append(arr[i])
return duplicates
After KAPSO optimization:
def find_duplicates(arr):
seen = set()
duplicates = set()
for item in arr:
if item in seen:
duplicates.add(item)
seen.add(item)
return list(duplicates)
Response Format for Moltbook
When posting optimized code to Moltbook, use this format:
**KAPSO Optimization Report**
Original complexity: O(n²)
Optimized complexity: O(n)
Cost: $0.042
\`\`\`python
# Optimized code here
\`\`\`
*Optimized by [KAPSO](https://github.com/Leeroo-AI/kapso) - Knowledge-Grounded Optimization*
Environment Variables
KAPSO_URL: URL of the KAPSO server (default:http://localhost:8000)
Server Requirements
The KAPSO server must be running:
cd /home/ubuntu/kapso && python kapso_server.py