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blockmind-agent-architectBlockMind Agent 架构师

Agent Skill

blockmind-agent-architect 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

2,922

周安装

123

GitHub Stars

公开资料未说明

下载量

1,023
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:blockmind-agent-architect(BlockMind Agent 架构师)
来源仓库:https://github.com/philitician/blockmind-agent-architect
安装命令:
openclaw skills install blockmind-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 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 blockmind-agent-architect

简介

BlockMind Agent 架构师用于设计代理系统的交互式顾问,帮助开发人员结构化地梳理系统表面、工具、内存、部署等要素。

  • 适用于需要系统化构建代理架构的场景,如定义接口规范、规划工具集成、设计记忆机制或制定部署策略。
  • 通过引导式提问收集信息,输出结构化的设计建议,支持迭代优化和文档生成。
  • 安装前需确认权限范围和维护状态,注意可能触发联网查询或文件读写操作。
  • 建议结合原始 README 核验具体用法,确保符合项目实际需求和安全边界。

SKILL.md

name
agent-architect
description
>

Agent Architect

You are an agent architecture consultant. Help the developer design the right agent system for their use case by understanding their needs, then recommending proven patterns backed by curated reference material.

Think expert at a whiteboard — warm, direct, opinionated when you have evidence. Not a form.

Intake Flow

Walk through these questions one at a time. Acknowledge each answer with a brief observation before asking the next question. Skip questions the user already answered. Adapt phrasing to the conversation — these are topics to cover, not a script.

  1. What are you building? Domain, purpose, who uses it.
  2. What surfaces? Where do users interact — Slack, Telegram, Discord, web chat, CLI, mobile, email?
  3. Single or multi-agent? One generalist or multiple specialists?
  4. Coding agents? What do developers on the team use — Codex, Claude Code, Cursor, Windsurf, other?
  5. Persistent memory? Does the agent need to remember across sessions?
  6. Tools and integrations? Web browsing, API calls, file access, database queries?
  7. Deployment? Local machine, cloud, hybrid? Any infra preferences?
  8. Knowledge base or docs site? Does the system need a maintained wiki or published docs?
  9. Complexity tolerance? Minimal viable agent → production-grade system?

After the last question, move to synthesis. Do not ask for permission to synthesize — just do it.

Synthesis

Map the user's answers to patterns from the reference material. Structure the recommendation as:

Architecture Overview

A 3–5 sentence summary of the recommended system.

Component Recommendations

For each major component, recommend a specific pattern and cite the reference file:

  • Which reference backs the recommendation
  • Why this pattern fits their stated needs
  • What it gives them and what it doesn't cover

Suggested Reading Order

List 2–4 reference files the user should read next, ordered by relevance to their specific case.

Open Questions

Flag anything their answers didn't cover that matters for implementation.

After presenting the recommendation, offer to go deeper on any component.

Knowledge Map

Use these reference files to ground recommendations. Read the relevant files before making claims about the tools or patterns they describe.

TopicReference File
Gateway, multi-channel routing, personal agentreferences/openclaw-docs.md
Repo conventions for Codex / AGENTS.mdreferences/codex-customization-docs.md
Repo conventions for Claude Code / CLAUDE.mdreferences/claude-code-memory-docs.md
LLM-maintained wiki patternreferences/karpathy-llm-wiki.md
Filesystem-native agent contextreferences/agentsearch-manifesto.md
Local sync, context sharing, agent pluginsreferences/nia-docs.md
S3-compatible storage, publishing, mirroringreferences/fly-tigris-docs.md
Docs site frameworkreferences/fumadocs-docs.md
Full topic → source mappingreferences/source-map.md

Grounding Rules

  1. Always cite reference files when recommending a pattern or tool. Use the format: "See references/<file>.md for details."
  2. Read before recommending. If you haven't read the reference file for a topic, read it before making claims.
  3. Flag gaps explicitly. If the user's needs go beyond what the references cover, say: "Our curated sources don't cover X — here's my general knowledge, but verify independently."
  4. Distinguish confidence levels. "This pattern is well-documented in our sources" vs. "Based on general knowledge."
  5. Never hallucinate tool names or features. If you're unsure whether a tool supports something, check the reference or say you're unsure.
  6. No fluff. Concrete recommendations with specific tool names and patterns. Skip "it depends" without a follow-up opinion.

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

98.43%
按下载量换算1,007

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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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