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navil-policy导航政策

Agent Skill

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。

总安装

3,694

周安装

148

GitHub Stars

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下载量

1,196
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: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

简介

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。

SKILL.md

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

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

80.87%
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权限和风险

external-service

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。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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