dobbe

dobbe

Automates DevOps workflows like vulnerability resolution, code review, test generation, and DORA metrics through Claude Code slash commands, using a state machine for reliable execution.

Category
访问服务器

README

<p align="center"> <img src="docs/banner.svg" alt="dobbe -- DevOps autopilot for Claude Code" width="100%"> </p>

<p align="center"> <a href="https://github.com/nareshnavinash/dobbe-mcp/actions"><img src="https://github.com/nareshnavinash/dobbe-mcp/actions/workflows/ci.yml/badge.svg" alt="CI"></a> <a href="https://www.npmjs.com/package/dobbe"><img src="https://img.shields.io/npm/v/dobbe.svg" alt="npm"></a> <a href="https://github.com/nareshnavinash/dobbe-mcp/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License"></a> <a href="https://nodejs.org"><img src="https://img.shields.io/badge/node-%3E%3D18-brightgreen.svg" alt="Node.js"></a> <img src="https://img.shields.io/badge/tests-373%20passed-brightgreen" alt="Tests"> <img src="https://img.shields.io/badge/coverage-94%25-brightgreen" alt="Coverage"> </p>


$ /dobbe-vuln-resolve

[scan]    Found 3 Dependabot alerts (2 critical, 1 high)
[fix]     Upgraded lodash 4.17.20 -> 4.17.21, axios 0.21.1 -> 0.21.4
[verify]  Running tests... FAILED (TypeError in utils.js:42)
[retry]   Attempt 2/3 -- feedback injected, retrying with error context
[fix]     Pinned axios 0.21.4, added @types/lodash
[verify]  Running tests... PASSED (147/147)
[pr]      Created PR #142: "fix: resolve vulnerable dependencies"

Why dobbe?

  • Your AI skips steps. dobbe can't. Every pipeline is a finite state machine with Zod validation at each transition. Claude submits results, the server validates them, and only then advances to the next step. If tests fail, the server loops back automatically with the exact error output injected as context. This is program control flow, not prompt engineering.

  • 45 minutes to 3 minutes. A typical Dependabot triage: open GitHub, read 12 alerts, figure out which matter, checkout a branch, upgrade packages, run tests, debug failures, run tests again, push, create a PR. With dobbe: type /dobbe-vuln-resolve. The pipeline handles all steps including up to 3 retry loops if tests break.

  • One command. No config files. npx dobbe install registers the MCP server and 24 slash commands. No YAML. No dashboard. No SaaS signup. Uninstall with npx dobbe uninstall.

Before vs. After

Task Without dobbe With dobbe
Resolve Dependabot alerts Open GitHub, read alerts, checkout, upgrade, test, fix, test again, PR (~45 min) /dobbe-vuln-resolve -- auto-retry, PR created (~3 min)
Code review all open PRs Read each diff, write comments, check security/tests/quality /dobbe-review-post -- deep review + comments posted
Find coverage gaps + write tests Run coverage, find gaps, write tests, run, fix, repeat /dobbe-test-gen -- analyze, generate, verify, retry, PR
DORA metrics Query GitHub API, compute 4 metrics, format report /dobbe-metrics-dora -- one command
Triage Sentry incidents Open Sentry, read stack traces, search codebase, write analysis /dobbe-incident-triage -- fetch, analyze, report

Quick Start

npx dobbe install

Restart Claude Code, then try:

/dobbe-vuln-scan
/dobbe-review-digest
/dobbe-metrics-dora
# Uninstall
npx dobbe uninstall

How It Works

The MCP server runs a finite state machine. Each step declares what needs to happen (intent, mode, context) and Claude decides how to accomplish it. Results are validated with Zod schemas before advancing.

graph LR
    A[scan] --> B[fix]
    B --> C[commit]
    C --> D{verify}
    D -->|pass| E[report]
    D -->|fail| B
    E --> F[pr]
    F --> G((done))
    D -->|max retries| H((failed))

The server controls the workflow, not the prompt. Each step has a declarative intent (what to do), mode (plan, act, gather, or report), and context (structured parameters). Claude uses its best tools and UX for the job. If results fail Zod validation, the server rejects them. If tests fail at the verify step, the server loops back to fix with the error output injected as feedback -- up to 3 iterations.

Commands

AI-Powered Pipelines

Command What it does Pipeline flow Retry
/dobbe-vuln-scan Scan + triage Dependabot alerts scan -> report -> done --
/dobbe-vuln-resolve Scan, fix, test, retry, create PR scan -> fix -> commit -> verify -> report -> pr -> done 3x
/dobbe-review-digest Fetch PRs, deep review, generate digest fetch -> review -> done --
/dobbe-review-post Review PRs, post comments to GitHub fetch -> review -> post -> done --
/dobbe-audit-report Security audit (vulns, licenses, secrets, quality) analyze -> done --
/dobbe-deps-analyze Dependency health, licensing, usage analysis analyze -> done --
/dobbe-test-gen Find coverage gaps, generate tests, verify, PR analyze -> generate -> verify -> commit -> pr -> done 3x
/dobbe-changelog-gen Git history to categorized release notes analyze -> done --
/dobbe-migration-plan Plan + execute dependency migrations plan -> apply -> verify -> commit -> pr -> done 3x
/dobbe-incident-triage Sentry issue triage with AI root cause analysis fetch -> triage -> done --

Multi-Perspective Reviews

Command What it does
/dobbe-review-as-pm Product Manager review -- feature gaps, prioritization, roadmap
/dobbe-review-as-engineer Engineering review -- architecture, code quality, tech debt
/dobbe-review-as-designer Design review -- UX, accessibility, interaction patterns
/dobbe-review-as-qa QA review -- test coverage, edge cases, reliability
/dobbe-review-as-test-architect Test architecture review -- strategy, frameworks, coverage
/dobbe-review-as-marketing Marketing review -- positioning, messaging, go-to-market
/dobbe-review-as-sales Sales review -- competitive positioning, pricing, objections
/dobbe-project-review Run all 7 perspectives + synthesized summary

Each review pipeline uses gather mode to understand the project interactively, then plan mode for deep analysis.

Metrics & Scanning

Command What it does
/dobbe-metrics-dora DORA metrics (deploy frequency, lead time, failure rate, MTTR)
/dobbe-metrics-velocity PR velocity and cycle time metrics
/dobbe-scan-secrets Secrets and credentials scanner

Utilities

Command What it does
/dobbe-setup Interactive configuration wizard
/dobbe-doctor Environment health check
/dobbe-config View and manage configuration

Works With

Integration Used by
GitHub (Dependabot, PRs, Actions) vuln-scan, vuln-resolve, review-, metrics-, changelog-gen
Sentry incident-triage
Slack Notification delivery (configurable channel)
npm / pip / bundler / Cargo / Go mod vuln-resolve, deps-analyze, migration-plan
Jest / pytest / Vitest / Go test / RSpec test-gen (auto-detects framework)

Auto-detects your framework: Django, Angular, React, Next.js, Express, Flask, FastAPI, Spring Boot, Rails.

Prerequisites

  • Claude Code -- installed and authenticated
  • Node.js 18+ -- for the MCP server
  • gh CLI -- for GitHub API access (brew install gh)
  • MCP servers (optional) -- GitHub, Sentry, Slack for enhanced capabilities

Architecture

Why a state machine? LLMs are stateless -- they forget context between tool calls. A state machine ensures every step executes in order, results are validated with Zod schemas before advancing, and retry loops inject the exact error output from the previous attempt. This is control flow, not prompt engineering.

Claude Code (executor)
    |
    v
dobbe MCP Server (state machine controller)
    |
    +-- 21 Pipeline definitions
    |   +-- Each pipeline: states, transitions, Zod schemas, intent/mode/context/hints
    |   +-- 3 pipelines with retry loops (vuln-resolve, test-gen, migration-plan)
    |   +-- 7 role-based review pipelines + 1 aggregate project-review
    |
    +-- State machine engine (generic FSM)
    |   +-- Zod validation per step
    |   +-- Retry logic with feedback injection
    |   +-- Persistent sessions (crash recovery)
    |
    +-- 14 MCP Tools
    |   +-- pipeline_start, pipeline_step, pipeline_complete, pipeline_status
    |   +-- pipeline_list, pipeline_list_sessions, pipeline_abort
    |   +-- config_read, config_write
    |   +-- cache_get, cache_set
    |   +-- session_load, session_save
    |
    +-- Utilities
        +-- Atomic file writes (crash-safe)
        +-- Structured logging (JSON + pretty mode)
        +-- Framework detection (Django, React, Angular, Express, etc.)
        +-- File-based cache with TTL

Built to Ship

  • 373 tests with 94% coverage -- every pipeline path is tested
  • 21 pipelines with Zod validation at every state transition
  • 3 retry pipelines with automatic feedback injection
  • Zero global mutable state -- PipelineService is fully isolated and testable
  • Atomic file writes -- crash-safe session persistence via write-to-temp + rename
  • CI on Node 18, 20, 22 -- tested across all active LTS versions

<details> <summary><strong>Configuration</strong></summary>

Config is stored in ~/.dobbe/config.toml. Run /dobbe-setup in Claude Code to configure.

[general]
default_org = "acme"
default_format = "table"
default_severity = "critical,high,medium,low"

[notifications]
slack_channel = "#security-alerts"

[timeouts]
scan = 300
resolve = 600
review = 300

Environment variables:

Variable Description Default
DOBBE_HOME Override ~/.dobbe directory ~/.dobbe
DOBBE_LOG_LEVEL debug / info / warn / error info
DOBBE_LOG_FORMAT json / pretty json

</details>

Development

git clone https://github.com/nareshnavinash/dobbe-mcp.git
cd dobbe-mcp
npm install
npm test              # 373 tests
npm run test:coverage # 94%+ coverage
npm run build
npm run lint

See CONTRIBUTING.md for development guidelines.

License

MIT

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