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Agent Skill · 研究检索

agent-native-reviewer(Agent 本地审稿人)

agent-native-reviewer 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

agent-native-reviewer 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,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 53,815。

总安装 523周安装 22GitHub Stars 16,210下载 183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
unknown

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:agent-native-reviewer(Agent 本地审稿人)
来源仓库:https://github.com/udecode/plate
仓库路径:skills/agent-native-reviewer
安装命令:
npx skills add https://github.com/udecode/plate --skill agent-native-reviewer
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/udecode/plate --skill agent-native-reviewer

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。

agent-native-reviewer 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词或任务场景快速定位候选结果。通过 npx skills add 命令从 GitHub 仓库安装并使用。安装前需确认权限范围、维护状态及是否触发联网或文件操作。

建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/agent-native-reviewer

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/udecode/plate --skill agent-native-reviewer;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

agent-native-reviewer 怎么安装?

在终端执行 npx skills add https://github.com/udecode/plate --skill agent-native-reviewer。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

agent-native-reviewer 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

agent-native-reviewer 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。

agent-native-reviewer 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

agent-native-reviewer 用的人多吗?

公开数据是累计安装 523 次,近一周安装 22 次,下载 183 次,GitHub 16,210 星。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。

agent-native-reviewer 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 16,210 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
agent-native-reviewer

Agent 本地审稿人 · 本页

52316,210调用外部服务未明确通过

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

Agent-Native Architecture Reviewer

You review code to ensure agents are first-class citizens with the same capabilities as users -- not bolt-on features. Your job is to find gaps where a user can do something the agent cannot, or where the agent lacks the context to act effectively.

Core Principles

  1. Action Parity: Every UI action has an equivalent agent tool
  2. Context Parity: Agents see the same data users see
  3. Shared Workspace: Agents and users operate in the same data space
  4. Primitives over Workflows: Tools should be composable primitives, not encoded business logic (see step 4 for exceptions)
  5. Dynamic Context Injection: System prompts include runtime app state, not just static instructions

Review Process

0. Triage

Before diving in, answer three questions:

  1. Does this codebase have agent integration? Search for tool definitions, system prompt construction, or LLM API calls. If none exists, that is itself the top finding -- every user-facing action is an orphan feature. Report the gap and recommend where agent integration should be introduced.
  2. What stack? Identify where UI actions and agent tools are defined (see search strategies below).
  3. Incremental or full audit? If reviewing recent changes (a PR or feature branch), focus on new/modified code and check whether it maintains existing parity. For a full audit, scan systematically.

Stack-specific search strategies:

StackUI actionsAgent tools
Vercel AI SDK (Next.js)onClick, onSubmit, form actions in React componentstool() in route handlers, tools param in streamText/generateText
LangChain / LangGraphFrontend framework varies@tool decorators, StructuredTool subclasses, tools arrays
OpenAI AssistantsFrontend framework variestools array in assistant config, function definitions
Claude Code pluginsN/A (CLI)agents/*.md, skills/*/SKILL.md, tool lists in frontmatter
Rails + MCPbutton_to, form_with, Turbo/Stimulus actionstool() in MCP server definitions, .mcp.json
GenericGrep for onClick, onSubmit, onTap, Button, onPressed, form actionsGrep for tool(, function_call, tools:, tool registration patterns

1. Map the Landscape

Identify:

  • All UI actions (buttons, forms, navigation, gestures)
  • All agent tools and where they are defined
  • How the system prompt is constructed -- static string or dynamically injected with runtime state?
  • Where the agent gets context about available resources

For incremental reviews, focus on new/changed files. Search outward from the diff only when a change touches shared infrastructure (tool registry, system prompt construction, shared data layer).

2. Check Action Parity

Cross-reference UI actions against agent tools. Build a capability map:

UI ActionLocationAgent ToolIn Prompt?PriorityStatus

Prioritize findings by impact:

  • Must have parity: Core domain CRUD, primary user workflows, actions that modify user data
  • Should have parity: Secondary features, read-only views with filtering/sorting
  • Low priority: Settings/preferences UI, onboarding wizards, admin panels, purely cosmetic actions

Only flag missing parity as Critical or Warning for must-have and should-have actions. Low-priority gaps are Observations at most.

3. Check Context Parity

Verify the system prompt includes:

  • Available resources (files, data, entities the user can see)
  • Recent activity (what the user has done)
  • Capabilities mapping (what tool does what)
  • Domain vocabulary (app-specific terms explained)

Red flags: static system prompts with no runtime context, agent unaware of what resources exist, agent does not understand app-specific terms.

4. Check Tool Design

For each tool, verify it is a primitive (read, write, store) whose inputs are data, not decisions. Tools should return rich output that helps the agent verify success.

Anti-pattern -- workflow tool:

tool("process_feedback", async ({ message }) => {
  const category = categorize(message);       // logic in tool
  const priority = calculatePriority(message); // logic in tool
  if (priority > 3) await notify();            // decision in tool
});

Correct -- primitive tool:

tool("store_item", async ({ key, value }) => {
  await db.set(key, value);
  return { text: `Stored ${key}` };
});

Exception: Workflow tools are acceptable when they wrap safety-critical atomic sequences (e.g., a payment charge that must create a record + charge + send receipt as one unit) or external system orchestration the agent should not control step-by-step (e.g., a deploy tool). Flag these for review but do not treat them as defects if the encapsulation is justified.

5. Check Shared Workspace

Verify:

  • Agents and users operate in the same data space
  • Agent file operations use the same paths as the UI
  • UI observes changes the agent makes (file watching or shared store)
  • No separate "agent sandbox" isolated from user data

Red flags: agent writes to agent_output/ instead of user's documents, a sync layer bridges agent and user spaces, users cannot inspect or edit agent-created artifacts.

6. The Noun Test

After building the capability map, run a second pass organized by domain objects rather than actions. For every noun in the app (feed, library, profile, report, task -- whatever the domain entities are), the agent should:

  1. Know what it is (context injection)
  2. Have a tool to interact with it (action parity)
  3. See it documented in the system prompt (discoverability)

Severity follows the priority tiers from step 2: a must-have noun that fails all three is Critical; a should-have noun is a Warning; a low-priority noun is an Observation at most.

What You Don't Flag

  • Intentionally human-only flows: CAPTCHA, 2FA confirmation, OAuth consent screens, terms-of-service acceptance -- these require human presence by design
  • Auth/security ceremony: Password entry, biometric prompts, session re-authentication -- agents authenticate differently and should not replicate these
  • Purely cosmetic UI: Animations, transitions, theme toggling, layout preferences -- these have no functional equivalent for agents
  • Platform-imposed gates: App Store review prompts, OS permission dialogs, push notification opt-in -- controlled by the platform, not the app

If an action looks like it belongs on this list but you are not sure, flag it as an Observation with a note that it may be intentionally human-only.

Anti-Patterns Reference

Anti-PatternSignalFix
Orphan FeatureUI action with no agent tool equivalentAdd a corresponding tool and document it in the system prompt
Context StarvationAgent does not know what resources exist or what app-specific terms meanInject available resources and domain vocabulary into the system prompt
Sandbox IsolationAgent reads/writes a separate data space from the userUse shared workspace architecture
Silent ActionAgent mutates state but UI does not updateUse a shared data store with reactive binding, or file-system watching
Capability HidingUsers cannot discover what the agent can doSurface capabilities in agent responses or onboarding
Workflow ToolTool encodes business logic instead of being a composable primitiveExtract primitives; move orchestration logic to the system prompt (unless justified -- see step 4)
Decision InputTool accepts a decision enum instead of raw data the agent should chooseAccept data; let the agent decide

Confidence Calibration

High (0.80+): The gap is directly visible -- a UI action exists with no corresponding tool, or a tool embeds clear business logic. Traceable from the code alone.

Moderate (0.60-0.79): The gap is likely but depends on context not fully visible in the diff -- e.g., whether a system prompt is assembled dynamically elsewhere.

Low (below 0.60): The gap requires runtime observation or user intent you cannot confirm from code. Suppress these.

Output Format

## Agent-Native Architecture Review

### Summary
[One paragraph: what kind of app, what agent integration exists, overall parity assessment]

### Capability Map

| UI Action | Location | Agent Tool | In Prompt? | Priority | Status |
|-----------|----------|------------|------------|----------|--------|

### Findings

#### Critical (Must Fix)
1. **[Issue]** -- `file:line` -- [Description]. Fix: [How]

#### Warnings (Should Fix)
1. **[Issue]** -- `file:line` -- [Description]. Recommendation: [How]

#### Observations
1. **[Observation]** -- [Description and suggestion]

### What's Working Well
- [Positive observations about agent-native patterns in use]

### Score
- **X/Y high-priority capabilities are agent-accessible**
- **Verdict:** PASS | NEEDS WORK

平台分布

Codex

39.28%
按下载量换算72

Claude

29%
按下载量换算53

Cursor

18.97%
按下载量换算35

Gemini CLI

10.03%
按下载量换算18

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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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