che-xray

Scans a repository to detect stack, structure, and conventions, generating a persistent project profile.

Updated May 12, 2026
One-click install
npx skills add https://github.com/laionazeredo/che-ai --skill che-xray-laionazeredo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: che-xray
Source: https://github.com/laionazeredo/che-ai/tree/main/skills/che-xray
Command: npx skills add https://github.com/laionazeredo/che-ai --skill che-xray-laionazeredo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Onboarding an AI agent (or a new engineer) to an unfamiliar repository requires manually discovering the tech stack, monorepo layout, folder conventions, test setup, CI/CD, and architectural risks. This Skill automates that first-contact analysis and persists the findings in a shared project registry so every future session starts with full context. ## Core Features & Use Cases - 7-Step Scan Pipeline: Detects language, frameworks, monorepo type (pnpm/npm workspaces, Nx, Turborepo), entry points, data layer, auth model, testing stack, and CI/CD provider. - 12-Section Project Profile: Generates a structured project_profile.md plus an auto-filled architecture.md in a global registry (Level 1.5), shared across worktrees and sessions. - Architectural Red Flags: Flags god packages, circular imports, raw SQL outside migrations, missing tests, and hardcoded secrets with evidence. - Use Case: The first time Che touches a new repository, run the X-Ray scan once; every later agent session reads the persisted profile instead of re-scanning the codebase. ## Quick Start Run the che-xray scan on this repository to detect its stack and structure and generate the project profile in the registry.

Frequently Asked Questions about che-xray

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automatically detect a repository's tech stack and structure?▼

Run the che-xray scan once per project. It detects language, frontend/backend frameworks, database drivers, auth providers, testing frameworks, and CI/CD from package manifests, config files, and a graphify knowledge graph, then writes a 12-section project_profile.md.

How to detect monorepo type like Nx, Turborepo, or pnpm workspaces?▼

The scan classifies structure by looking for marker files: pnpm-workspace.yaml for pnpm workspaces, nx.json for Nx, turbo.json for Turborepo, and a workspaces field in package.json for npm workspaces. It also counts apps and shared packages.

When should I run the repository X-Ray scan?▼

Run it exactly once per project, the first time Che touches the repository, or again when architecture changes significantly such as a monolith-to-monorepo migration. It is idempotent and merges new findings without overwriting manually edited sections.

Does the repo scan work without the graphify knowledge graph?▼

Yes. If graphify is unavailable (GRAPHIFY_OK=0), the scan falls back to a lightweight heuristic pass using glob and grep over file extensions, package manifests, CI configs, and migration folders to infer the stack and structure.

What architectural red flags does the repository scan detect?▼

It flags god packages with excessive file counts, suspicious circular imports, raw SQL outside migration folders, projects with zero unit test files, hardcoded secrets, and hardcoded environment URLs, each with one line of evidence.

Where are the X-Ray scan results stored?▼

Results are written outside the worktree into the global project registry (Level 1.5): project_profile.md, an auto-filled architecture.md, and an appended audit line in registry.jsonl, so all worktrees and sessions share the same profile.