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jz-agent-guardrailsjzAgent 护栏

Agent Skill

jz-agent-guardrails 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

9,456

周安装

394

GitHub Stars

公开资料未说明

下载量

3,152
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:jz-agent-guardrails(jzAgent 护栏)
来源仓库:https://github.com/jzocb/jz-agent-guardrails
安装命令:
openclaw skills install jz-agent-guardrails
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 jz-agent-guardrails

简介

用于防止 AI Agent 绕过预设规则或安全策略。

  • 通过 git hooks 与部署验证实现自动化管控。
  • 适合开发环境中强制执行代码审查与秘密检测。
  • 使用时需配置相应钩子脚本与注册表项。jz-agent-guardrails 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 安装前建议评估对现有工作流的侵入性影响。

SKILL.md

name
agent-guardrails
version
1.1.0
author
Anonymous
license
MIT
category
development-tools
description
Stop AI agents from secretly bypassing your rules. Mechanical enforcement with git hooks, secret detection, deployment verification, and import registries. Born from real production incidents: server crashes, token leaks, code rewrites. Works with Claude Code, Clawdbot, Cursor. Install once, enforce forever.
tags
keywords
repository
https://github.com/anon/agent-guardrails
homepage
https://github.com/anon/agent-guardrails#readme
bugs
https://github.com/anon/agent-guardrails/issues
compatibility
requirements
bash
>=4.0
git
>=2.0
pricing
FREE

Agent Guardrails

Mechanical enforcement for AI agent project standards. Rules in markdown are suggestions. Code hooks are laws.

Quick Start

cd your-project/
bash /path/to/agent-guardrails/scripts/install.sh

This installs the git pre-commit hook, creates a registry template, and copies check scripts into your project.

Enforcement Hierarchy

  1. Code hooks (git pre-commit, pre/post-creation checks) — 100% reliable
  2. Architectural constraints (registries, import enforcement) — 95% reliable
  3. Self-verification loops (agent checks own work) — 80% reliable
  4. Prompt rules (AGENTS.md, system prompts) — 60-70% reliable
  5. Markdown rules — 40-50% reliable, degrades with context length

Tools Provided

Scripts

ScriptWhen to RunWhat It Does
install.shOnce per projectInstalls hooks and scaffolding
pre-create-check.shBefore creating new .py filesLists existing modules/functions to prevent reimplementation
post-create-validate.shAfter creating/editing .py filesDetects duplicates, missing imports, bypass patterns
check-secrets.shBefore commits / on demandScans for hardcoded tokens, keys, passwords
create-deployment-check.shWhen setting up deployment verificationCreates .deployment-check.sh, checklist, and git hook template
install-skill-feedback-loop.shWhen setting up skill update automationCreates detection, auto-commit, and git hook for skill updates

Assets

AssetPurpose
pre-commit-hookReady-to-install git hook blocking bypass patterns and secrets
registry-template.pyTemplate __init__.py for project module registries

References

FileContents
enforcement-research.mdResearch on why code > prompts for enforcement
agents-md-template.mdTemplate AGENTS.md with mechanical enforcement rules
deployment-verification-guide.mdFull guide on preventing deployment gaps
skill-update-feedback.mdMeta-enforcement: automatic skill update feedback loop
SKILL_CN.mdChinese translation of this document

Usage Workflow

Setting up a new project

bash scripts/install.sh /path/to/project

Before creating any new .py file

bash scripts/pre-create-check.sh /path/to/project

Review the output. If existing functions cover your needs, import them.

After creating/editing a .py file

bash scripts/post-create-validate.sh /path/to/new_file.py

Fix any warnings before proceeding.

Setting up deployment verification

bash scripts/create-deployment-check.sh /path/to/project

This creates:

  • .deployment-check.sh - Automated verification script
  • DEPLOYMENT-CHECKLIST.md - Full deployment workflow
  • .git-hooks/pre-commit-deployment - Git hook template

Then customize:

  1. Add tests to .deployment-check.sh for your integration points
  2. Document your flow in DEPLOYMENT-CHECKLIST.md
  3. Install the git hook

See references/deployment-verification-guide.md for full guide.

Adding to AGENTS.md

Copy the template from references/agents-md-template.md and adapt to your project.

中文文档 / Chinese Documentation

See references/SKILL_CN.md for the full Chinese translation of this skill.

Common Agent Failure Modes

1. Reimplementation (Bypass Pattern)

Symptom: Agent creates "quick version" instead of importing validated code. Enforcement: pre-create-check.sh + post-create-validate.sh + git hook

2. Hardcoded Secrets

Symptom: Tokens/keys in code instead of env vars. Enforcement: check-secrets.sh + git hook

3. Deployment Gap

Symptom: Built feature but forgot to wire it into production. Users don't receive benefit. Example: Updated notify.py but cron still calls old version. Enforcement: .deployment-check.sh + git hook

This is the hardest to catch because:

  • Code runs fine when tested manually
  • Agent marks task "done" after writing code
  • Problem only surfaces when user complains

Solution: Mechanical end-to-end verification before allowing "done."

4. Skill Update Gap (META - NEW)

Symptom: Built enforcement improvement in project but forgot to update the skill itself. Example: Created deployment verification for Project A, but other projects don't benefit because skill wasn't updated. Enforcement: install-skill-feedback-loop.sh → automatic detection + semi-automatic commit

This is a meta-failure mode because:

  • It's about enforcement improvements themselves
  • Without fix: improvements stay siloed
  • With fix: knowledge compounds automatically

Solution: Automatic detection of enforcement improvements with task creation and semi-automatic commits.

Key Principle

Don't add more markdown rules. Add mechanical enforcement. If an agent keeps bypassing a standard, don't write a stronger rule — write a hook that blocks it. Corollary: If an agent keeps forgetting integration, don't remind it — make it mechanically verify before commit.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

78.33%
按下载量换算2,469

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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