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Agent Skill · 研究检索

navil-policy(导航政策)

navil-policy 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。

navil-policy 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 25,786。

总安装 3,694周安装 148GitHub Stars 公开资料未说明下载 1,196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
1.0.2

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:navil-policy(导航政策)
来源仓库:https://github.com/ivanpantheon/navil-policy
安装命令:
openclaw skills install navil-policy
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 navil-policy

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

navil-policy 创建 YAML 策略以控制 MCP 工具的最小权限访问。可降低令牌成本达 94%,适用于多代理权限隔离场景。定义角色与资源映射关系,支持细粒度 API 调用限制。策略文件需人工审核后部署,变更应遵循变更管理流程。生产环境启用前应在测试集验证策略有效性。

navil-policy 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

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 navil-policy;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

    它需要调用外部服务,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

navil-policy 怎么安装?

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

navil-policy 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。

navil-policy 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

navil-policy 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。

navil-policy 用的人多吗?

公开数据是累计安装 3,694 次,近一周安装 148 次,下载 1,196 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。

navil-policy 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
navil-policy

导航政策 · 本页

3,694—调用外部服务MIT-0通过

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

name
navil-policy
description
Reduce MCP token costs by up to 94% and enforce least-privilege tool access. Creates YAML policies that control which MCP tools each agent can see and call. Use when user mentions token costs, context window bloat, too many tools, tool scoping, reducing tokens, saving money on API calls, least privilege, restricting tool access, creating access policies, or agent permissions. Also when user says "my context window is full" or "too many tool schemas" or "MCP is too expensive".
version
1.0.2
metadata
openclaw
emoji
📋
homepage
https://github.com/navilai/navil
requires
bins
install
kind
pip
package
navil
bins
[navil]
label
Install Navil policy engine

Navil Policy — MCP Tool Scoping and Cost Optimization

GitHub's MCP server exposes 90+ tools consuming 50,000+ tokens before your agent thinks about your question. At scale, MCP tool definitions can exceed model context limits entirely.

Navil Policy solves this by controlling which tools each agent sees in tools/list responses. A code review agent sees 3 tools instead of 90. That is a 94% reduction in schema tokens — cheaper inference, faster responses, and a smaller attack surface.

When to Use This Skill

  • User complains about token costs or context window bloat from MCP
  • User has multiple MCP servers and agents are seeing too many tools
  • User wants to restrict which tools specific agents can access
  • User says "MCP is too expensive" or "my context is full of tool schemas"
  • User asks about least privilege, tool scoping, or agent permissions
  • User wants to auto-generate policies from observed agent behavior

How Tool Scoping Works

Navil's policy engine sits in the proxy layer (set up by navil-shield). When an agent requests tools/list, Navil filters the response based on the policy file before the agent ever sees it.

This means:

  • The agent doesn't know the filtered tools exist — they never enter the context window
  • Token savings are immediate — fewer tool schemas = less context consumed
  • Security improves — agents can't call tools they can't see
  • No code changes — the policy file controls everything

Creating a Policy

Step 1: Check if Navil Shield is Active

navil --version

If navil is not installed, install it first:

pip install navil --break-system-packages 2>/dev/null || pip install navil

If MCP servers are not yet wrapped with navil shim, the policy engine cannot filter tool lists. Recommend installing navil-shield first.

Step 2: Observe Current Tool Usage

To see what tools are currently being exposed to agents:

navil policy check --tool "*" --agent default --action list

Step 3: Generate a Starter Policy

Navil can auto-generate policies by watching how agents actually use tools:

navil policy auto-generate

This creates ~/.navil/policy.auto.yaml based on observed baselines. Review it, then copy rules you want to keep into ~/.navil/policy.yaml.

Step 4: Write Custom Policy

For manual policy creation, create ~/.navil/policy.yaml:

# Example: Scope tools by workflow
scopes:
  code-review:
    allow:
      - get_pull_request
      - list_files
      - create_review_comment
    description: "Code review agent sees only PR-related tools"

  deploy:
    allow:
      - create_deployment
      - get_deployment_status
    description: "Deploy agent sees only deployment tools"

  read-only:
    allow:
      - get_*
      - list_*
      - search_*
    description: "Read-only agents cannot modify anything"

  default:
    allow: "*"
    description: "Backward compatible — unrestricted access"

# Rate limiting per agent
rate_limits:
  default:
    requests_per_minute: 60
  deploy:
    requests_per_minute: 10

Step 5: Apply and Test

navil policy check --tool create_deployment --agent code-review --action call
# Expected: DENIED — code-review scope doesn't include deployment tools

navil policy check --tool get_pull_request --agent code-review --action call
# Expected: ALLOWED

Step 6: Monitor Policy Decisions

View the live decision log to verify policies are working:

navil policy suggest

This shows pending auto-generated rules with confidence scores. Accept the ones that make sense.

Step 7: Rollback if Needed

navil policy rollback

This undoes auto-generated policy changes. Your manually written policy.yaml is never modified by the auto-generator.

Token Savings Calculator

Present this to the user when they ask about cost:

ScenarioTools ExposedApprox Schema TokensWith Navil ScopingSavings
GitHub MCP (all tools)90+~50,0003 tools (~1,600)97%
Database MCP106~54,6008 tools (~4,100)92%
Full enterprise stack (5 servers)300+~150,000+20 tools (~10,000)93%

At typical API pricing, scoping a heavy MCP setup saves $50-200/month in token costs alone.

Policy Templates

Navil ships with community templates for common MCP servers. Ask the user which servers they use, then suggest the appropriate template:

  • GitHub MCP: Read-only, code-review, full-access scopes
  • Filesystem MCP: Read-only, workspace-scoped, full-access scopes
  • kubectl MCP: View-only, namespace-scoped, admin scopes

Important Notes

  • policy.yaml (manual rules) always takes precedence over policy.auto.yaml (generated rules)
  • Policies filter what agents see in tools/list, separate from what they can call
  • The default scope with allow: "*" ensures backward compatibility
  • Scoped responses are cached with 60s TTL in the Rust proxy — near-zero performance cost

Links

  • Policy documentation: https://github.com/navilai/navil#tool-scoping
  • Community policy templates: https://github.com/navilai/navil/tree/main/policies
  • Token cost guide: https://navil.ai/docs/token-costs

平台分布

OpenClaw

80.87%
按下载量换算967

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。目前只有一个公开来源,建议同时核对源仓库。

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