Cursor Sidebar Plugin

A digital twin and trust layer for AI-generated software.

CodeWalkerAI gives teams a live, continuously updated model of the codebase inside Cursor, analyzing architecture, dependencies, execution paths, risks, AI-generated code, and operational behavior.

Schedule a Walkthrough See the Digital Twin Engine

CodeWalkerAI Echo screenshot — **Echo** See how trust breaks ripple through logic and system flow before risky code ships.

Trust Layer Modules

Recon Track architecture movement, dependency clusters, and generated-code drift across the software system.

Microscope Reveal where insecure logic, phantom references, and invalid symbols concentrate inside code.

Retrace Show how a risky change alters execution behavior, operational flow, and downstream trust.

Cursor

A sidebar workflow inside the coding environment where AI-generated software is actually reviewed.

2D + 3D

Logic-level and repository-scale views of the same continuously updated software model.

PR → Runtime

Trace generated changes from pull request context to execution-path and operational impact.

TokenWise Inside CodeWalkerAI

Token, cost, and context governance inside Cursor.

TokenWise extends the trust layer into prompt operations so teams can understand context usage, model cost, and prompt efficiency before every send.

TokenWise live

Prompt budget overview

Ready to send

Context window 31.2k / 46k

Estimated cost $0.43 on GPT-5.4

selected files 22.6k

system + prompt 6.8k

response budget 1.8k

Collapse duplicate context Drop generated assets Use cheaper model for summarization

Real-time token counter in Cursor

Watch prompt and response token usage update live as you work.

Pre-flight token estimation

Estimate usage for selected files before a model call is sent.

Cost estimation per model

Compare spend across models before choosing how to run the job.

Context window usage meter

See how close a prompt is to exhausting available context.

Token breakdown by file

Identify which files are consuming the most context inside the request.

Optimization suggestions

Get practical ways to reduce token load before submit or retry.

CodeWalkerAI Recon screenshot — **Recon** Repository-wide visibility into dependency clusters and architecture drift.

CodeWalkerAI Microscope screenshot — **Microscope** Function-level inspection of vulnerable paths and risky execution branches.

The digital twin stays live as the codebase changes.

CodeWalkerAI turns architecture, dependencies, execution paths, and risky AI-generated changes into one continuously updated software model teams can inspect in context.

Architecture drift and dependency movement
Hallucinated libraries and phantom references
Exposed secrets and vulnerable paths
Prompt injection and AI-generated logic risk
Runtime and operational context
Trust decisions with remediation context

Trust Signals

The core trust checks behind the digital twin.

AST analysis, dependency verification, and repository context work together to validate whether AI-generated code is real, connected, and safe to ship.

Broken build prevention

Catch fake components before they destabilize the repository and release flow.

Security posture

Reduce vulnerabilities introduced by AI-generated code that references non-existent dependencies.

Runtime resilience

Find hidden execution failures and invalid logic branches earlier in the lifecycle.