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Agent Skill · 开发

gomboc-security-community(Gomboc 安全社区)

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

gomboc-security-community 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 8,250。

总安装 3,262周安装 125GitHub Stars 公开资料未说明下载 1,156核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.2.5

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:gomboc-security-community(Gomboc 安全社区)
来源仓库:https://github.com/gomboc-ai/gomboc-security-community
安装命令:
openclaw skills install gomboc-security-community
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install gomboc-security-community

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

使用 Gomboc.ai 的开放修复语言自动扫描基础设施、应用程序代码和配置并生成确定性、可合并的代码修复。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install gomboc-security-community;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

gomboc-security-community 怎么安装?

在终端执行 openclaw skills install gomboc-security-community。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

gomboc-security-community 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

gomboc-security-community 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

gomboc-security-community 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

gomboc-security-community 用的人多吗?

公开数据是累计安装 3,262 次,近一周安装 125 次,下载 1,156 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

gomboc-security-community 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
gomboc-security-community

Gomboc 安全社区 · 本页

3,262—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Gomboc Code Remediation Skill

Deterministic, merge-ready code fixes powered by ORL (Open Remediation Language).


Provenance & External Dependencies

Source: This skill wraps the official Gomboc.ai Community Edition code remediation engine.

Official Documentation: https://docs.gomboc.ai Community Discussions: https://github.com/Gomboc-AI/gomboc-ai-feedback/discussions GitHub App: https://github.com/apps/gomboc-ai-community

External API Dependency:

  • Endpoint: https://api.app.gomboc.ai/graphql
  • Authentication: Bearer token (Personal Access Token from https://app.gomboc.ai)
  • Required: Free account at Gomboc.ai (no credit card)
  • Setup: Generate token in Settings → Personal Access Tokens

Token Scope & Security (Least Privilege):

  • Scope: Read-only API access (minimal required permissions)
  • Capabilities: Query account, scans, runs, and fix events only
  • Restrictions: Cannot modify, delete, or alter Gomboc configurations
  • Safe for CI/CD: No destructive capabilities, suitable for GitHub Actions and workflow secrets
  • Best Practice: Generate a dedicated token per environment/workflow; never commit to code
  • Storage: Use GitHub Secrets (Actions) or CI provider's secure secret management
  • Verification: Check token scope in Gomboc settings before use in production

Shipped Files:

  • SKILL.md — This documentation
  • README.md — Quick start guide
  • SECURITY.md — Security audit
  • scripts/cli-wrapper.py — Python CLI wrapper
  • scripts/docker-compose.yml — MCP server configuration
  • examples/vulnerable.tf — Example vulnerable code
  • references/ — Integration guides

License: MIT (see LICENSE.md)


Gomboc.ai Community Edition automatically scans and fixes code issues across your entire codebase — infrastructure, applications, configurations, and more — using a deterministic remediation engine (no hallucinations). Unlike traditional scanners that generate alerts, Gomboc delivers merge-ready pull requests that clear your code issues backlogs. This skill wraps Gomboc's power into agent workflows, CLI tools, and CI/CD pipelines, making it the perfect complement to agentic coding.

How Gomboc Works

Gomboc's Open Remediation Language (ORL) engine operates through a multi-stage deterministic process:

Policy Definition → Code Analysis → Deterministic Fix Generation → PR Delivery
  1. Understands your environment and code context
  2. Turns policies into executable rules using ORL
  3. Analyzes your code with full syntax-tree precision (via Tree-sitter)
  4. Generates deterministic fixes (same input = same output, always)
  5. Delivers merge-ready pull requests ready to review and ship

Key difference: While generative AI is probabilistic (helpful for reasoning but unpredictable), Gomboc's ORL provides deterministic remediation — predictable, repeatable, and auditable.

What It Does

  • Scan any codebase for issues (infrastructure, application code, configs)
  • Generate deterministic, merge-ready pull requests with fixes
  • Remediate continuously via GitHub Actions or interactive CLI/MCP
  • Trust 94%+ fix acceptance rate with zero hallucinations (ORL Engine)
  • Pair with agents — deterministic remediation that works perfectly alongside agentic coding systems

Supported Languages & Frameworks

ORL supports remediation across multiple languages and code types via syntax-tree matching (Tree-sitter):

  • Infrastructure as Code

- Terraform (HCL) - CloudFormation (YAML/JSON) - Kubernetes manifests (YAML) - Helm charts

  • Configuration Files

- JSON, YAML, HCL - Shell scripts - Docker files

  • Code Languages

- Python - JavaScript/TypeScript - Go, Java, C#, Rust, and 20+ more

  • Cloud Providers

- AWS, Azure, GCP, OCI - Kubernetes, Helm

  • Policy Coverage

- CIS benchmarks - AWS best practices - Security hardening - Custom org policies

Full language list: docker run --rm gombocai/orl language

Quick Start

1. Get a Token

# Sign up at https://app.gomboc.ai (free, Community Edition)
# Generate Personal Access Token in Settings
export GOMBOC_PAT="gpt_your_token"

2. Scan Code

python scripts/cli-wrapper.py scan --path ./src

3. Generate Fixes

python scripts/cli-wrapper.py fix --path ./src

4. Apply Fixes (Optional)

python scripts/cli-wrapper.py remediate --path ./src --commit

Key Features

✅ Deterministic AI — Same fix every time, no hallucinations ✅ 94%+ Accuracy — Merge-ready fixes users actually accept ✅ Free Forever — Community Edition of Gomboc.ai ✅ Production-Ready — Battle-tested implementation ✅ Secure by Design — No token leaking, proper error handling ✅ Agent-Friendly — Perfect for autonomous code improvement loops

CLI Commands

scan

Detect issues in your codebase

gomboc scan path:./terraform
gomboc scan path:./src policy:aws-cis format:markdown

fix

Generate merge-ready fixes

gomboc fix path:./terraform format:pull_request
gomboc fix path:./src format:json

remediate

Apply fixes directly to code

gomboc remediate path:./src commit:true
gomboc remediate path:./terraform commit:true push:true

config

Manage authentication

gomboc config --show-token

Why This Matters for Agents

Gomboc solves the determinism problem in AI-driven code improvement:

  • Deterministic Fixes — ORL ensures same code always produces same fix (no randomness)
  • Repeatable — Policies execute consistently across runs, repos, and teams
  • Trustworthy — 94%+ merge rate because fixes are syntax-aware, not brittle regex patterns
  • Safe for Agents — Agents can autonomously scan → generate → apply without human fear
  • Continuous Improvement — Perfect for agentic loops: code generation → scanning → remediation → iterate

The Agent Workflow:

  1. Agent generates code
  2. Gomboc scans and identifies issues
  3. ORL generates deterministic fixes
  4. Agent reviews and applies fixes
  5. Repeat with next feature/iteration

This creates a feedback loop where agents learn and improve continuously.

Integration Methods

Gomboc integrates into your workflow through multiple paths:

1. VS Code / Cursor IDE (Developers)

Fastest way to try Gomboc interactively:

# Install: Gomboc VS Code Extension
# Set: GOMBOC_PAT in extension settings
# Run: Gomboc: Scan current file
# Review: Problems panel → Apply Fix
  • Real-time scanning as you save
  • Interactive fix review and apply
  • See: https://docs.gomboc.ai/getting-started-ce

2. MCP Server (Agents)

Run the MCP server for agent integration:

docker-compose -f scripts/docker-compose.yml up
# Server runs on http://localhost:3100

# Or: docker run -p 3100:3100 -e GOMBOC_PAT='your_token' gombocai/mcp:latest

Agents interact with Gomboc via MCP protocol:

@gomboc scan path:./src
@gomboc fix path:./src format:pull_request
@gomboc remediate path:./code commit:true

See references/mcp-integration.md for details.

3. CLI Tool (Developers & Scripts)

Use the Python CLI for local scanning:

export GOMBOC_PAT="your_token"
python scripts/cli-wrapper.py scan --path ./src --format markdown
python scripts/cli-wrapper.py fix --path ./src --format pull_request
python scripts/cli-wrapper.py remediate --path ./src --commit

See references/setup.md for detailed instructions.

4. GitHub App (Automated)

Install Gomboc GitHub App for automatic PRs:

  • https://github.com/apps/gomboc-ai-community
  • Select repos to monitor
  • Gomboc automatically scans on PR, generates fixes

5. GitHub Actions (CI/CD)

Automate continuous remediation in pipelines:

- uses: gomboc-action@v1
  with:
    path: ./terraform
    auto-fix: true
    policy: default

See references/github-action.md for configuration.

Configuration

All configuration is via environment variables:

VariablePurposeRequiredExample
GOMBOC_PATPersonal Access TokenYesgpt_abc123...
GOMBOC_MCP_URLMCP server URLNohttp://localhost:3100
GOMBOC_POLICYRemediation policyNodefault or aws-cis

Security & Audit

This skill has been:

  • ✅ Security-audited for token handling
  • ✅ Verified against live Gomboc API
  • ✅ Tested with real vulnerabilities
  • ✅ Confirmed production-ready

See SECURITY.md for complete audit details.

Open Remediation Language (ORL)

ORL is the deterministic execution engine that powers Gomboc fixes:

Deterministic Remediation

  • Same input = same output, always (no probabilistic AI)
  • Syntax-tree aware (via Tree-sitter) instead of brittle regex
  • Safe for bulk remediation across large codebases
  • Results are auditable and reviewable

How ORL Works

  1. Detects policy violations by analyzing syntax trees
  2. Evaluates context and code structure
  3. Applies precise transformations (insert, modify, delete)
  4. Validates fixes are safe and complete

Policy-Driven Fixes

  • Policies define what should be fixed (CIS, AWS best practices, custom rules)
  • ORL rules execute those policies to generate code changes
  • Policy Sets let you customize enforcement by environment/team

See: https://docs.gomboc.ai/orl

Support & Documentation

  • Setup Guide: references/setup.md
  • MCP Integration: references/mcp-integration.md
  • GitHub Actions: references/github-action.md
  • Security Audit: SECURITY.md
  • Changelog: CHANGELOG.md
  • Gomboc Official Docs: https://docs.gomboc.ai
  • ORL Documentation: https://docs.gomboc.ai/orl
  • GitHub Feedback: https://github.com/Gomboc-AI/gomboc-ai-feedback/discussions

License

MIT License — See LICENSE file


Ready to remediate? Start with the Quick Start section above, then explore integration methods that fit your workflow.

平台分布

OpenClaw

85.36%
按下载量换算987

安全审计

VirusTotal

未展示

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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