TaskBounty Check

TaskBounty Check

Local-only GitHub Actions and CI maintenance scanner for AI-built apps. Exposes scan, explanation, and fix-planning tools to MCP clients; modifies nothing and makes no outbound requests by default.

Category
访问服务器

README

taskbounty-check

A local check for GitHub Actions and CI maintenance hygiene (third-party action pinning, workflow token permissions, and update automation), built for apps shipped with Lovable, Bolt, Replit, Cursor, or v0.

Local by default. No uploads. No telemetry. It reads only your workflow files, on your machine. The default code path makes no outbound network requests, writes its report locally, and sends nothing anywhere. There is no analytics or phone-home of any kind. Only the opt-in --gh-org mode uses the network (through your own gh session).

Works with Cursor, Claude Code, and Codex (local MCP server, below).

Three ways to use it

1. GitHub Action — add a maintenance check to CI that writes a summary to the run (no PR comments, no source upload):

permissions:
  contents: read
steps:
  - uses: actions/checkout@v4
  - run: npx taskbounty-check@0.1.6 . --github-summary --no-network

Want a human to interpret or fix what the Action surfaces? Request a free launch-safety review. TaskBounty gets no access to your repo, source, or workflows unless you submit that form.

2. Agent / MCP — a local stdio server for Cursor, Claude Code, and Codex:

npx -y taskbounty-check@0.1.6 mcp

3. One-off CLI — scan the current repo locally and write a report:

npx -y taskbounty-check@0.1.6 .

Pin a version (@0.1.6) in committed config and CI for reproducibility. @latest is convenient for a quick one-off, but a pinned version is the reproducible choice.

The GitHub job summary

The Action writes a counts-only maintenance summary to the workflow run (categories and next steps, no filenames, line numbers, or repo source). Below is that exact summary, rendered from this repo's own CI output:

TaskBounty check: GitHub Actions maintenance summary

See it produced live by the self-check job in this repository's Actions runs.

Prefer a guided walkthrough? Follow the five-minute real-repository quickstart.

Learn more

Supported checks (and honest limitations)

Checks (GitHub Actions + CI maintenance hygiene):

  • Third-party actions pinned to a movable tag/branch instead of a commit SHA
  • Broad (write-all) workflow token permissions
  • Missing explicit permissions: block
  • Update automation (Dependabot/Renovate) presence
  • Context-dependent workflow patterns flagged for private review (e.g. pull_request_target, script injection)

Does NOT check (these need a manual review): exposed secrets, auth/authorization, payments, webhooks, runtime behavior. It is a maintenance/hygiene check, not a full security audit or a penetration test.

What it does

  • Reads only your GitHub Actions workflow files and update-automation config, scans them in-process with a deterministic ruleset (the same rules as the public checker), and writes a local HTML + JSON report. It does not execute workflows, install dependencies, or run any repository code.

Modes

Mode Command Network
Single repo npx taskbounty-check . none
Directory of repos npx taskbounty-check ./all-repos none
Explicit paths npx taskbounty-check --manifest repos.json none
GitHub org (your gh session) npx taskbounty-check --gh-org <org> yes, opt-in

--gh-org uses your existing gh CLI session to fetch each repo's workflow files to this machine (read-only). Your GitHub token is never read by this tool and never sent to TaskBounty.

What is read, written, transmitted

Run --explain-data to print this at any time.

  • Reads (allowlist — nothing else is opened): <repo>/.github/workflows/*.yml|*.yaml and update-automation config (dependabot.yml/renovate.json*). Never source files, .env, secrets, SSH keys, credential stores, or anything outside the selected repository roots. Symlinks that escape a root are skipped, never followed.
  • Writes (local only): <out>.json (full detail) and <out>.html.
  • Transmits: nothing by default. --share uploads nothing — it writes a sanitized, counts-only file (scan id, label, candidate counts by category, private-review count, scanner version, timestamps; repo names only with --include-repo-names) for you to submit manually. Network stays off under --share. Only --gh-org intentionally uses the network.

Flags

--share · --gh-org <org> · --manifest <file> · --org-label <label> · --include-repo-names · --dry-run · --explain-data · --delete-local-report · --no-network (default everywhere except --gh-org) · --out <basename> · --version · --help

Want help interpreting or fixing these results?

Request a free 20-minute launch-safety review: https://www.task-bounty.com/ai-app-security-check/review?utm_source=npm&utm_medium=npm_readme&utm_campaign=workflow_security

TaskBounty receives nothing unless you submit that form. The scan runs locally and the full report stays on your machine; the review form gives us no access to your repositories, source, workflows, or secrets.

GitHub Code Scanning (SARIF)

Emit SARIF 2.1.0 and surface findings in your repo's Security → Code scanning tab:

npx taskbounty-check@latest . --format sarif --output taskbounty.sarif

The SARIF carries deterministic rule ids (taskbounty/<rule>), severity levels, and file/line references — no source contents, secrets, or environment values, and no network access. Confirmed findings are emitted as kind: fail; lower-confidence items as kind: review.

Help interpreting SARIF results: https://www.task-bounty.com/ai-app-security-check/review?utm_source=github&utm_medium=sarif_docs&utm_campaign=workflow_security

Upload it with the official action (full example in examples/code-scanning.yml):

permissions:
  contents: read
  security-events: write
steps:
  - uses: actions/checkout@v4
  - run: npx taskbounty-check@0.1.6 . --format sarif --output taskbounty.sarif
  - uses: github/codeql-action/upload-sarif@v3
    with:
      sarif_file: taskbounty.sarif

Local agent (MCP)

Run a local stdio MCP server so Codex, Claude Code, or Cursor can scan and reason about findings in your editor. It runs locally, makes zero outbound network requests, uploads no source, and never modifies filesgenerate_fix_plan returns a plan as text for you to apply yourself.

npx taskbounty-check@latest mcp

Tools: scan_repo (local scan summary), explain_finding (plain-language explanation), generate_fix_plan (text fix plan).

Want a human to review the plan? https://www.task-bounty.com/ai-app-security-check/review?utm_source=mcp&utm_medium=mcp_docs&utm_campaign=workflow_security

Cursor.cursor/mcp.json:

{ "mcpServers": { "taskbounty-check": { "command": "npx", "args": ["-y", "taskbounty-check@latest", "mcp"] } } }

Claude Code:

claude mcp add taskbounty-check -- npx -y taskbounty-check@latest mcp

Codex — in ~/.codex/config.toml:

[mcp_servers.taskbounty-check]
command = "npx"
args = ["-y", "taskbounty-check@latest", "mcp"]

Security

Zero runtime dependencies. Published to npm with provenance (verify on the package's npm page). The default run makes no outbound requests and uploads nothing; see the methodology for the full data-handling and scope boundaries.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选