Archiet Xray — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Archiet Xray (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
mcp-name: io.github.Anioko/archiet-xray
See what your AI sees. Your codebase is too big for any AI agent's context window. The agent reads 40 files out of 4,000, makes a change, and you have no way to know whether it respected the architecture — or quietly violated it.
X-Ray extracts the actual architecture of any repo — deterministically, no LLM, code never leaves your machine — and gives it to both you (an interactive map) and your AI agent (an MCP server + context pack).
$ python xray.py .
Archiet X-Ray v0.2.0 — your-repo
visibility score : 78/100
prod readiness : 71/100 (near production-ready)
code files : 3,412 (1,907 read, 1,584 with elements)
routes : 214
entities : 87
async tasks : 31
findings : 6
wrote : .archiet/ARCHITECTURE.md
.archiet/AGENT_CONTEXT.md
.archiet/architecture.json
.archiet/diagrams/diagrams.html (+3 .mmd)| File | What it is |
|---|---|
ARCHITECTURE.md | Human-readable map: module dependency graph (Mermaid), domain model, every route with auth status, dependency hotspots, risk findings, production-readiness scorecard |
AGENT_CONTEXT.md | Drop into your CLAUDE.md / rules file — makes Claude Code, Cursor, and Windsurf respect your architecture today |
architecture.json | The machine-readable model (the "repo genome") |
diagrams/ | Architecture diagrams as Mermaid sources + a self-contained HTML viewer — module dependency graph, ER / domain-model diagram, HTTP route map |
A deterministic 0–100 production-readiness score over 8 dimensions: route auth coverage, secrets hygiene, client token storage, data-layer discipline, test footprint, migration discipline, ops readiness (Docker/CI/.env contract), and docs/API contract. Same repo always scores the same — so the score is comparable across commits, branches, and repos, and you can put it in CI.
Each dimension reports its evidence ("142/214 statically-guarded routes carry an auth guard") and the report ends with the top fixes ranked by points lost. Dimensions that don't apply (a CLI tool has no routes) take half credit and say so — honesty over flattery.
Useful when: reviewing AI-generated code before shipping it, auditing an inherited codebase, or tracking whether your repo is drifting away from production readiness over time.
generate_diagrams turns any repo into Mermaid architecture diagrams, deterministically extracted from the code itself:
Django, and Prisma models
You get the raw .mmd sources (paste into any README, wiki, GitHub, GitLab, Notion, Obsidian, or VS Code — they all render Mermaid natively) plus a self-contained diagrams.html viewer.
# Claude Code (from PyPI — pip install archiet-xray)
claude mcp add archiet-xray -- archiet-xray mcp /path/to/repo
# or from a checkout
claude mcp add archiet-xray -- python /path/to/mcp_server.py /path/to/repoClaude Desktop — download archiet-xray-<version>.mcpb from the latest release, then double-click it (or Settings → Extensions → Install Extension). It prompts for the repository to analyze; one-click after that.
Official MCP Registry — listed as io.github.Anioko/archiet-xray, so registry-aware clients can install it by name.
Your agent can now ask — before it edits:
and line. No LLM guesses anywhere in the pipeline.
— never invented. A wrong map is worse than no map.
never leaves your machine.
Django models with relations, Celery tasks, import graph — via ast, not regex
Prisma models, import graph
localStorage/AsyncStorage, routes without auth guards
More stacks (Go, Java, Rails, .NET) welcome — the extractor pattern is one class per language. PRs invited.
examples/ holds unedited X-Ray output for repos you know — microblog (Flask), the official FastAPI full-stack template, and vercel/commerce (Next.js). GitHub renders the Mermaid maps inline. On the FastAPI template, X-Ray correctly detects CurrentUser dependency auth on 18 routes and flags a real auth-token-in-localStorage write in useAuth.ts.
How is this different from a dependency-graph MCP server (Codegraph, dependency-mcp)? Those show call/import edges. X-Ray extracts web-architecture semantics on top of the graph: which routes exist, which carry auth guards, where the domain entities live, and where security boundaries leak (hardcoded secrets, raw SQL, tokens in localStorage). No graph tool tells you "510 of your routes have no detectable auth guard."
How is this different from a CLAUDE.md generator? CLAUDE.md generators write instructions and conventions — usually with an LLM. X-Ray extracts facts: every claim in its output traces to a file and line, and what it can't extract it labels unknown. Use both: your conventions plus X-Ray's ground truth.
What does auth status `?` mean? "Not detectable from per-function analysis." FastAPI routers often attach auth at include_router(dependencies=...) level, which is invisible when analyzing the route function. X-Ray reports unknown rather than guessing a confident "no auth" — a wrong map is worse than no map.
Is the production-readiness score a security audit? No. It is a deterministic static-signal score — it measures whether the repo carries the marks of production discipline (auth guards on routes, no hardcoded secrets, tests, migrations, ops + docs contracts). The payload says so explicitly. It is a fast, repeatable triage number, not a substitute for code review, load testing, or a security audit.
Why Mermaid for the diagrams instead of images? Mermaid sources are diffable, render natively on GitHub/GitLab/Notion/VS Code/Obsidian, and stay in sync with the repo because you can regenerate them in one command. The bundled diagrams.html gives you a browser view without installing anything.
Does it phone home? No. Stdlib-only, zero network calls, zero telemetry. The only outbound anything is a link in the generated footer.
X-Ray never touches the network. If you want the hosted counterpart — a procurement-grade architecture audit (traceability %, severity-ranked gaps, 30/60/90-day roadmap) generated from your architecture documents — the archiet-audit MCP server bridges your agent to archiet.com/audit-my-architecture. It is a separate server precisely so X-Ray's "your code never leaves your machine" guarantee stays absolute.
X-Ray is local-only by design. Specifically:
usage data, and no personal information of any kind.
build the architecture model, in-process, on your machine.
the files written to .archiet/ inside the repository you scan (ARCHITECTURE.md, AGENT_CONTEXT.md, architecture.json, diagrams/). Delete them at any time; nothing else is stored anywhere.
no HTTP client is even imported). Your code never leaves your machine.
for us to retain or delete.
The company-wide policy at archiet.com/privacy covers Archiet's hosted products; it applies to X-Ray only in the trivial sense that X-Ray sends us nothing.
X-Ray is the free, open companion to Archiet — the architecture-to-code platform. The map X-Ray extracts is the same formal model Archiet uses to enforce architecture on every PR (boundary gates, drift scoring, consulting-grade architecture reports) and to regenerate production-ready applications from it.
MIT licensed.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.