GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
作者提供的说明(英文原文)
Trust score for AI-generated code: scan repos, guard agent file writes, get a 0-100 score.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"umbra": {
"command": "npx",
"args": [
"-y",
"@elberacasa/umbra",
"mcp"
]
}
}
}在终端中运行这条命令
claude mcp add umbra -- npx -y @elberacasa/umbra mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"umbra": {
"command": "npx",
"args": [
"-y",
"@elberacasa/umbra",
"mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"umbra": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@elberacasa/umbra",
"mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 elberacasa 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(1.2.0)于3个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:GitHub, Desktop Commander, Cloudflare MCP
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
Cloudflare MCP servers
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Everyone is vibecoding. Nobody is verifying. Umbra scores it.
Umbra is a deterministic Trust Score (0–100) for AI-generated code: the vibe coding security scanner that verifies what your agent shipped, not what it claimed. One command, fully local, evidence for every finding.
→ [umbra-badge.umbrabadge.workers.dev](https://umbra-badge.umbrabadge.workers.dev) — the landing, live badges and report pages
Quickstart · Demo · The Audit · How it works · The Four Axes · FAQ · Roadmap · Contributing · Website
Studies put exploitable vulnerabilities in 40 to 60 percent of AI-generated code, and coding agents routinely claim "all tests pass" when three do. The tooling for *writing* code with AI is a year ahead of the tooling for *trusting* it. Umbra closes that gap: SAST rebuilt for how software gets written now, plus sandboxed verification that catches what static rules cannot.
One command scans any repo an agent produced (Claude Code, Cursor, Copilot, Windsurf, Lovable) and returns a score with file:line evidence for every finding. With --deep it goes further: Umbra builds and boots the repo in a locked-down Docker sandbox, then replays the agent's own claims against reality. If the agent is lying about tests, the score is capped below passing, with receipts.
We ran Umbra over 61 public, actively-maintained AI-built repos and published everything. The Vibe-Coding Security Audit:
| Finding | Repos hit |
|---|---|
Hardcoded-secret findings (committed .env, service keys in source) | 25% |
| API routes with no auth check | 26% |
| Injection sinks (SQL interpolation, unsafe HTML injection) | 49% |
| Entire databases / SQL dumps committed to git | 13% |
| At least one critical finding | 10% |
| Zero scored findings (genuinely clean) | 7 of 61 |
Mean trust score: 74/100. One in five repos fails outright. The full report has per-class deep dives with representative snippets and fixes, the complete per-repo table, and an honest methodology section — including the false positives we found in our own rules while running it, and fixed (rubric v4).
npx umbra-scan # check — scans the directory you're standing in
npx umbra-scan --fix # heal — applies provably-safe fixes, shows the score climbing npx umbra-scan --setup # protect — pre-commit gate, PR checks, agent guardrails
That's the whole interface. Three verbs: check, heal, protect.
Using an AI coding agent? Umbra is built to be driven by agents, not just run by humans:
--setup installs PreToolUse hooks so every file the agent writes is guarded before it lands.umbra-mcp (npx --yes -p @elberacasa/umbra umbra-mcp) and the agent gets scan_repo, guard_content, and get_score as tools.Real output, scanning a typical vibe-coded Next.js app (fixtures/bad-app in this repo, Trust Score 30/100):
$ npx umbra-scan ./fixtures/bad-app
UMBRA TRUST SCORE: 30/100 🔴
SAFE 🔴 5/100 — 14 findings CLEAN ✅ 87/100 — 10 findings RUNS — not measured — run with --deep HONEST — not measured — run with --deep
Score computed over measured axes only (full rubric: SAFE 35%, RUNS 25%, HONEST 25%, CLEAN 15%). Rubric v4. …plus 7 further findings beyond the per-rule cap (see report)
Top findings: [safe/hardcoded-secrets] Hardcoded Supabase service_role JWT — bypasses all row level security — .env:2 [safe/hardcoded-secrets] Hardcoded Supabase service_role JWT — bypasses all row level security — lib/supabase.ts:5 [safe/supabase-antipatterns] Supabase service_role key reachable from client-side code — full database bypass for anyone who opens the bundle — .env:2 [safe/supabase-antipatterns] Supabase service_role key reachable from client-side code — full database bypass for anyone who opens the bundle — app/components/UserList.tsx:10 [safe/hardcoded-secrets] Committed environment file with secret values: .env — .env:1
Notes (low confidence — not scored): [safe/missing-rate-limit] Auth endpoint with no rate-limiting signal in the repo — brute-force / credential-stuffing exposure (heuristic) — app/api/login/route.ts:3
Badge:
The exit code is 1 when the score is below 50, so CI can gate on it.
All commands and flags (the expert layer — most users never need these)
umbra [path] # path defaults to the current directory
umbra [path] --json # machine-readable output umbra [path] --offline # skip npm registry checks, fully local umbra [path] --deep # verify RUNS and HONEST in a Docker sandbox umbra [path] --report # write UMBRA.md: an agent-actionable task list umbra [path] --fix # apply provably-safe fixes and re-scan (score before → after) umbra [path] --dry-run # preview --fix without writing anything umbra [path] --baseline-write # write .umbra-baseline.json: grandfather current findings, gate only on new ones umbra [path] --baseline # use an explicit baseline file ("write" is shorthand for --baseline-write) umbra [path] --publish # self-report the score to the hosted badge service (live README badge) umbra setup # install everything (hooks + Action + agent guards) umbra init # only the pre-commit hook + GitHub Action umbra protect # only the agent PreToolUse hooks (--remove uninstalls) umbra guard --stdin # hook entrypoint (agents call this, not humans) umbra mcp # run the MCP server (bin: umbra-mcp)
The canonical package is @elberacasa/umbra; umbra-scan is the short alias. Same engine either way.
repo in
│ ▼ Layer 0 · static rules (17 SAFE + CLEAN rules, 0 tokens, <1s) ▼ Layer 1 · evidence gating (confidence-scored, low never moves the score) ▼ Layer 2 · --deep sandbox (Docker: build, boot, HTTP probe, claim replay) │ ▼ deterministic Trust Score + verdict + badge
Every finding carries a confidence level and file:line evidence. Only high and medium confidence findings move the score; hunches go to a notes section. The rubric is versioned (currently v3), so the same repo always gets the same score. Full math in RUBRIC.md.
Scanning finds problems after they land. The immune layer checks every file your agent writes before it lands. umbra protect installs PreToolUse hooks into Claude Code and Kimi Code (auto-detected, one command); the same engine backs the umbra-mcp server for MCP-native agents.
flowchart LR
CC[Claude Code hook] --> E KC[Kimi Code hook] --> E MCP["umbra-mcp: guard_content"] --> E E{"guardContent(file, content) file rules + path guard"} -->|allow / warn| W[write lands] E -->|"block (exit 2)"| B["reason fed back: agent fixes the root cause"]
npx umbra-scan protect # install the hooks; --remove uninstalls cleanly
A leaked Stripe key or an alg: none JWT never reaches the file. The path guard hard-blocks agent writes into .git/hooks and .git/config (CVE-2026-26268, the agent-planted git hook escape), and live credentials going into .env. Blocking is reserved for high-confidence critical/high findings; everything else warns, and every failure fails open. Verdicts land in ~0.2 ms, so the guard never slows the agent down. Full story: docs/immune-layer.md.
--deep: verify AI code, don't trust itThe fast scan is static. --deep is LLM code verification with evidence. Umbra copies the repo into a throwaway Docker container (no network at runtime, 512 MB / 1 CPU hard limits, 120-second kill switch), builds it, boots it, HTTP-probes its endpoints, and replays every claim found in READMEs and agent artifacts against what actually happens. Slower (minutes, not seconds) and needs a running Docker daemon. Without Docker the sandboxed axes are skipped and left out of the score; unverifiable is never punished.
Real output, deep-scanning a repo whose README lies (fixtures/claims-app, capped at 49/100 by the liar cap):
$ npx @elberacasa/umbra ./fixtures/claims-app --deep
UMBRA TRUST SCORE: 49/100 🔴
SAFE ✅ 100/100 — 0 findings CLEAN ✅ 100/100 — 2 findings RUNS — not measured — No detectable run path (no Dockerfile, no package.json start script or main entry) HONEST ⚠️ 50/100 — 2 claims failed, 2 verified, 1 unverifiable
Score computed over measured axes only (full rubric: SAFE 35%, RUNS 25%, HONEST 25%, CLEAN 15%). Rubric v4. Score capped below passing: a documented claim was verified false. Trust is the product.
Claim receipts: CLAIM FAILED: "14 tests pass" — README.md:7 — actually 3 tests pass, 0 fail CLAIM FAILED: "build passes" — README.md:9 — actually build exits 1 CLAIM VERIFIED: "All tests pass" — CLAUDE.md:3 — 3 tests pass CLAIM VERIFIED: "All tests are passing" — README.md:8 — 3 tests pass
An
只显示了开头部分;其余内容请在仓库中阅读