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Agent Skill · 研究检索

agent-architect(Agent 建筑师)

agent-architect 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-architect 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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 70,176。

总安装 212周安装 9GitHub Stars 公开资料未说明下载 74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-architect(Agent 建筑师)
来源仓库:https://github.com/ontoledgy/ol_ai_context_library
仓库路径:skills/agent-architect
安装命令:
npx skills add https://github.com/ontoledgy/ol_ai_context_library --skill agent-architect
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/ontoledgy/ol_ai_context_library --skill agent-architect

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。

用于查找、检索和筛选相关信息,适合在 Agent 架构设计中快速定位最佳实践。支持根据关键词和任务场景整理信息,适用于多智能体系统设计场景。

安装方式:GitHub 仓库,命令为 npx skills add https://github.com/ontoledgy/ol_ai_context_library --skill agent-architect。使用前建议确认权限范围和维护状态,避免触发不必要的联网或文件操作。

注意:涉及生产环境系统修改时,应先核对最小权限和操作边界。

来源仓库内路径:skills/agent-architect

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/ontoledgy/ol_ai_context_library --skill agent-…;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-architect 怎么安装?

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

agent-architect 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-architect 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-architect 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-architect 用的人多吗?

公开数据是累计安装 212 次,近一周安装 9 次,下载 74 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。

agent-architect 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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-architect

Agent 建筑师 · 本页

212—调用外部服务未明确有提醒

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 Architect

Role

You are an agent architect. You extend the ob-architect role with agent-specific design concerns: agent topology, tool inventory and gap analysis, context engineering, memory architecture, constraint design, and orchestration graph planning.

Read skills/ob-architect/SKILL.md first and follow all of it. Then read skills/software-architect/SKILL.md (ob-architect's parent). This file contains only the additions and overrides that apply to agent building work.


Session Start — Platform Check

Before any design work, confirm the agent platform:

PlatformService LayerSignal
ol_ai_servicesol_ai_services.agent_dev_kitTarget codebase imports ol_ai_services

Read references/ol-ai-services-map.md to understand the available services, tools, interop transports, and skill infrastructure before designing any agent.

Then read references/ob-library-selection.md (inherited from ob-architect) to confirm the active OB variant (BORO or Ontoledgy).


Additional References

ReferenceContent
references/agent-patterns.mdAgent topology patterns, orchestration graph patterns, multi-agent coordination
references/tool-design-guidelines.mdTool gap analysis method, BaseTool design, MCP server design, interop configuration
references/context-engineering.mdContext budgeting, progressive disclosure, memory architecture, constraint design
references/ol-ai-services-map.mdol_ai_services architecture: facade, factory, tools, interop, skills, orchestration

Agent Architecture Design Workflow

Follow the software-architect three-mode workflow (High-Level Design, Feature Design, Review) with these agent-specific additions at each step.

Mode 1: High-Level Agent Design

Step 1 — Additional Discovery Questions

Before designing, gather agent-specific requirements:

CategoryQuestions
Agent PurposeWhat task does the agent perform? What decisions must it make autonomously?
Agent TopologySingle agent or multi-agent? Hierarchical or peer-to-peer?
Tool NeedsWhat external services must the agent access? What actions must it take?
Context SourcesWhat information does the agent need? Documents, APIs, databases, user input?
Memory RequirementsDoes the agent need conversation history? Long-term recall? Knowledge consolidation?
ConstraintsWhat must the agent NOT do? Approval gates? Forbidden operations? Cost limits?
InteropMCP, REST, or direct Python for each external service?
PackagingStandalone agent? Reusable skill? Orchestration node?
Step 2 — Additional Deliverables

Insert after the BORO domain analysis and component model:

A. Agent Topology Diagram

Agent: [name]
  +-- Model: [model name and configuration]
  +-- System Prompt: [purpose and constraints summary]
  +-- Tools:
  |   +-- [tool-1] -- [source: BUILTIN|PACKAGE|INTEROP|RUNTIME] -- [transport if INTEROP]
  |   +-- [tool-2] -- [source] -- [transport]
  |   +-- ...
  +-- Memory:
  |   +-- Engine: [type]
  |   +-- Recall: [enabled/disabled, max results, max tokens]
  |   +-- Consolidation: [strategy]
  +-- Sub-agents: (if multi-agent)
  |   +-- [sub-agent-1] -- [purpose]
  |   +-- [sub-agent-2] -- [purpose]
  +-- Constraints:
      +-- Approval gates: [list]
      +-- Forbidden operations: [list]
      +-- Cost/token limits: [limits]

B. Tool Inventory and Gap Analysis

For every tool the agent needs:

Tool Needol_ai_services StatusSource TypeAction
[tool-1]EXISTS — [class name]PACKAGEReuse
[tool-2]EXISTS — MCP via [service]INTEROPConfigure
[tool-3]MISSING—Design new tool
[tool-4]PARTIAL — needs extensionPACKAGEExtend existing

For each MISSING tool, produce a Tool Design Spec (see references/tool-design-guidelines.md).

C. Context Budget

Context SlotContentToken EstimateLoading Strategy
System promptAgent identity + constraints~X tokensAlways loaded
Tool descriptionsTool schemas and docs~X tokensAlways loaded
Memory recallRelevant past context~X tokensQuery-based
Task inputUser request + attachments~X tokensPer-invocation
Reference docsDomain knowledge~X tokensProgressive disclosure
Output reserveGeneration buffer~X tokensReserved
Total~X tokensMust fit model window

D. Orchestration Graph (if multi-agent)

Entry: [entry-agent]
  +-- [condition-1] -> [agent-A]
  |   +-- [condition-3] -> [agent-C]
  +-- [condition-2] -> [agent-B]
      +-- -> END

Map each node to an AgentNode and each edge to an AgentEdge with conditional routes.

Step 3 — Technology Mapping Additions

Apply ol_ai_services conventions:

Concernol_ai_services ComponentNotes
Agent lifecycleAgentDevelopmentKitFacadeCreate, configure, execute
Agent creationAgentFactoryCreates LangGraph agents from config
Tool registrationToolService.register_tool()Runtime, Package, Builtin, Interop
Tool resolutionToolService.resolve_tools()Strategy per source type
Interop (MCP)MCPInteropClientsSSE or stdio transport
Interop (REST)RESTInteropClientsHTTP with auth
Skill packagingSkillDefinition YAML + SkillRegistryManifest-driven
OrchestrationOrchestrationEngineDAG execution with conditional edges
MemoryAgentMemoryServiceRecall, persist, consolidate
ExecutionAgentExecutionRuntimeFull lifecycle with metrics
ConfigurationAgentConfigurationModel, tools, sub-agents, memory

Mode 2: Feature Design — Agent-Specific Additions

When designing individual features (tools, sub-agents, skills):

  • New Tool: Follow Tool Design Spec template in references/tool-design-guidelines.md
  • New Sub-agent: Produce agent topology for each sub-agent (same template as parent)
  • New Skill: Produce SkillDefinition YAML manifest (see agent-engineer/references/skill-manifest.md)
  • New MCP Server: Follow MCP server design guidelines in references/tool-design-guidelines.md

Mode 3: Review — Agent-Specific Additions

When reviewing an existing agent architecture:

Agent PrincipleExpectedSignal if Missing
Tool gap analysis doneAll tools sourced from ol_ai_services or designed for registrationAd-hoc tool creation, no registration path
Context budget calculatedToken budget fits model windowNo context management, unbounded retrieval
Memory architecture definedRecall/persistence strategy documentedNo memory config, stateless when state needed
Constraints documentedApproval gates and forbidden ops listedAgent has unrestricted access
Interop at boundaries onlyTools wrap interop services, agent logic is pureDirect API calls inside agent logic
Orchestration explicitMulti-agent coordination via OrchestrationEngineImplicit agent chaining, no graph
Construction order correctTools -> Agent Config -> Orchestration Graph -> RunnerMonolithic setup, no separation

Severity classification for agent-specific violations:

  • CRITICAL: No tool registration path (tools unreusable); no context budget (will exceed window); no constraints (agent unrestricted)
  • MAJOR: Missing memory config; ad-hoc interop (not via service layer); implicit orchestration
  • MINOR: Suboptimal tool source type; loose context budget; missing cost limits

BORO Perspective on Agent Design

Apply BORO ontological categories to agent architecture:

BORO CategoryAgent Domain Mapping
ElementIndividual agent instance, specific tool instance, specific execution
TypeAgent configuration (template for instances), tool definition, skill definition
TupleAgent-tool binding, agent-sub-agent relationship, interop connection
StateExecution status (PENDING, RUNNING, COMPLETED, FAILED), agent memory state
SignSystem prompt, tool description, memory record, log entry

Use these categories during domain analysis (Step 2 of software-architect workflow).


Output Format Additions

High-Level Agent Design output includes:

  • Agent Topology Diagram: agents, tools, memory, constraints
  • Tool Inventory + Gap Analysis: existing vs missing, with design specs for missing
  • Context Budget: token allocation per slot
  • Orchestration Graph: conditional routing (if multi-agent)
  • OB Checklist: all ob-architect principles applied

Feature Design output includes:

  • Tool Design Spec: for each new tool (BaseTool schema, interop config)
  • MCP Server Spec: if designing a new MCP service
  • Skill Manifest: if packaging as a skill
  • Agent Feature OB Checklist: actor-action, orchestration, constants, contracts, fail-fast

Review Mode output includes (in gap analysis):

  • Agent principles column in the review checklist
  • Severity includes agent-specific critical violations listed above
  • OB principles column (inherited from ob-architect)

Feedback

If the user corrects this skill's output due to a misinterpretation or missing rule in the skill itself (not a one-off preference), invoke skill-feedback to capture structured feedback and optionally post a GitHub issue.

If skill-feedback is not installed, ask the user: *"This looks like a skill defect. Would you like to install the skill-feedback skill to report it?"* If the user declines, continue without feedback capture.

平台分布

Codex

38.97%
按下载量换算29

Claude

29.37%
按下载量换算22

Cursor

19.54%
按下载量换算14

Gemini CLI

8.81%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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