Concepts
The desire_path CLI tracks how AI coding assistants try to interact with your system. Four core concepts form the backbone of dp:
Desires
A desire is a single failed tool call from an AI assistant. When Claude Code tries to call read_file but that tool doesn’t exist, that failure gets recorded as a desire. Each one captures what the AI wanted to do, what went wrong, and the context around it.
Paths
A path emerges when the same tool fails repeatedly. Like a worn trail across a lawn, frequent failures for read_file form a pattern that says “build a sidewalk here.” Paths show you what capabilities to prioritize building.
Aliases
An alias maps a hallucinated tool name to a real one. When Claude keeps calling read_file but your tool is actually named Read, create an alias. This connects desires to reality and helps you understand what the AI is actually trying to accomplish.
Invocations
Invocations track ALL tool calls, not just failures. When enabled, you get the full picture: success rates, usage patterns, session timelines. This turns desire_path from a failure tracker into a comprehensive telemetry system for AI tool usage.
Learn more about invocations →
The flow: AI assistants generate desires (failures). Repeated desires form paths (patterns). Aliases connect desires to real tools. Invocations expand tracking to include successes, giving you the complete story.