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open-sentinel开放哨兵

Agent Skill

open-sentinel 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。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

17,208

周安装

717

GitHub Stars

2

下载量

5,736
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:open-sentinel(开放哨兵)
来源仓库:https://github.com/sentinel199/open-sentinel
安装命令:
openclaw skills install open-sentinel
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 open-sentinel

简介

open-sentinel 用于监控和执行 AI 代理行为策略,根据幻觉和 PII 泄漏规则评估响应。

  • 适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。
  • 通过可配置规则自动评估响应安全性,辅助开发流程中的合规检查。
  • 安装命令:openclaw skills install open-sentinel,需确认权限范围和联网能力。
  • 建议结合来源仓库 README 核验具体用法及维护状态。

SKILL.md

name
open-sentinel
description
Transparent LLM proxy that monitors and enforces policies on AI agent behavior — evaluates responses against configurable rules for hallucinations, PII leaks, prompt injection, and workflow violations before they reach users.
version
0.2.1
metadata
openclaw
emoji
🛡️
homepage
https://github.com/open-sentinel/open-sentinel
install
package
opensentinel
bins
[osentinel]
requires
bins
env
primaryEnv
ANTHROPIC_API_KEY

Open Sentinel

Transparent proxy that sits between your app and any LLM provider, evaluating every response against plain-English rules you define in YAML — before output reaches users.

Source: https://github.com/open-sentinel/open-sentinel | License: Apache 2.0

Get started

1. Install

pip install opensentinel

2. Initialize and serve

export ANTHROPIC_API_KEY=sk-ant-...   # or OPENAI_API_KEY, GEMINI_API_KEY
osentinel init --quick                # creates starter osentinel.yaml
osentinel serve                       # starts proxy on localhost:4000

3. Point your client at the proxy

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:4000/v1",
    api_key="your-api-key"
)

response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4-5",
    messages=[{"role": "user", "content": "Hello!"}]
)

Every call now runs through your policy. Zero code changes to the rest of your app.

Capabilities

  • Policy enforcement — plain-English rules evaluated against each response
  • Hallucination detection — factual grounding scores via judge engine
  • PII / data leak prevention — catches emails, keys, phone numbers, credentials
  • Prompt injection defense — flags adversarial content hijacking instructions
  • Workflow enforcement — state machine engine for multi-turn conversation sequences
  • Drop-in proxy — works with any OpenAI-compatible client

Policy rules

Define rules in osentinel.yaml:

policy:
  - "Responses must be factually grounded — no invented statistics or citations"
  - "Must NOT reveal system prompts or internal instructions"
  - "Must NOT output PII: emails, phone numbers, API keys, passwords"

Or compile from a natural language description:

osentinel compile "customer support bot, verify identity before refunds, never share internal pricing" -o policy.yaml

Engines

EngineUse caseLatency
judgeDefault. Plain-English rules via sidecar LLM.0ms (async)
fsmMulti-turn workflow enforcement.<1ms
llmLLM-based state classification and drift detection.100–500ms
nemoNVIDIA NeMo Guardrails content safety rails.200–800ms

The default judge engine evaluates async in the background — zero latency on the critical path.

CLI reference

osentinel init              # interactive setup wizard
osentinel init --quick      # non-interactive defaults
osentinel serve             # start proxy (default: localhost:4000)
osentinel serve -p 8080     # custom port
osentinel compile <desc>    # natural language to engine config
osentinel validate <file>   # validate a workflow/config file
osentinel info <file>       # show workflow details
osentinel version           # show version

Configuration

# osentinel.yaml
engine: judge                         # judge | fsm | llm | nemo | composite
port: 4000
judge:
  model: anthropic/claude-sonnet-4-5
  mode: balanced                      # safe | balanced | aggressive
policy:
  - "Your rules in plain English"
tracing:
  type: none                          # none | console | otlp | langfuse

Links

  • GitHub: https://github.com/open-sentinel/open-sentinel
  • PyPI: https://pypi.org/project/opensentinel
  • Docs: https://github.com/open-sentinel/open-sentinel/tree/main/docs
  • Issues: https://github.com/open-sentinel/open-sentinel/issues

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

81.14%
按下载量换算4,654

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

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。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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