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meta-systems-thinking元系统思维

Agent Skill

meta-systems-thinking 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。

总安装

432

周安装

18

GitHub Stars

125

下载量

144
CodexClaudeCursorGemini CLI

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

unknown

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:meta-systems-thinking(元系统思维)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/meta-systems-thinking
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill meta-systems-thinking
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/asgard-ai-platform/skills --skill meta-systems-thinking

简介

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于关键词搜索、任务场景匹配和来源线索整理等研究检索场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前需确认权限范围、维护状态,注意是否涉及联网或文件操作。
  • 建议结合原始 README 核验具体用法和功能边界。

SKILL.md

Systems Thinking

Framework

IRON LAW: First-Order Fixes in Complex Systems Produce Second-Order
Backlash Within 2 Cycles — Map the Feedback Loop BEFORE Intervening

Agents default to "fix the symptom directly" (e.g., high turnover → raise
salaries). In systems with feedback loops, the direct fix triggers a
compensating response that makes the original problem worse OR creates
a new one (raise salaries → budget squeeze → cut training → worse
onboarding → higher turnover). Before recommending any intervention,
draw the causal loop diagram and identify at least one reinforcing and
one balancing loop. If you can't find any, the problem may not be a
systems problem — don't force the framework.

Analysis Steps

Key concepts assumed known: feedback loops (reinforcing/balancing), emergence, delays, leverage points, stocks and flows. For system archetypes (Fixes That Fail, Shifting the Burden, Limits to Growth, etc.) see references/system-archetypes.md.

  1. Define the system boundary: What's in, what's out?
  2. Map key variables: What are the important stocks (quantities that accumulate)?
  3. Identify feedback loops: Which loops are reinforcing? Which are balancing?
  4. Find delays: Where is cause separated from effect in time?
  5. Locate leverage points: Where would small interventions produce the biggest shift?
  6. Check for unintended consequences: What might this intervention break elsewhere in the system?

Output Format

# Systems Analysis: {Problem}

## System Boundary
- In scope: ...
- Out of scope: ...

## Key Variables
- {Variable A}: {description}

## Feedback Loops
- Reinforcing: {A → B → A (amplifying)}
- Balancing: {A → B → C → opposes A (stabilizing)}

## Delays
- {Input} → {Effect} (delay: {timeframe})

## Leverage Points
1. {where small change = big impact}

## Unintended Consequences Risk
- If we {intervention}, it might also {side effect} because {loop/connection}

Examples

Correct Application

Scenario: Why does hiring more engineers not speed up the project?

Reinforcing loop (intended): More engineers → more code → faster progress Balancing loop (unintended): More engineers → more communication overhead → more meetings → less coding time → slower progress (Brooks' Law) Delay: New engineers need 3-6 months to become productive

Leverage point: Instead of adding people, reduce communication overhead (smaller teams, clearer ownership, better documentation) ✓

Incorrect Application

  • "Revenue is down. Increase marketing spend." → Linear, single-cause thinking. Ignoring: Why is revenue down? Is it demand (balancing loop from saturation)? Is it churn (reinforcing loop of poor quality → complaints → more churn)? Different root causes require different interventions.

Gotchas

  • Systems resist change: Balancing feedback loops maintain the status quo. Pushing against them without addressing the loop structure leads to "fixes that fail."
  • Mental models are partial: Everyone's mental model of a system is incomplete. Mapping the system with diverse stakeholders reveals blind spots.
  • Unintended consequences are the norm, not the exception: In complex systems, interventions always produce side effects. The question is whether you've identified the important ones.
  • Not everything is a system: Simple problems with clear cause-and-effect don't need systems thinking. Use it for problems where linear thinking fails.

References

  • For system archetypes (Limits to Growth, Shifting the Burden, etc.), see references/system-archetypes.md

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Codex

37.53%
按下载量换算54

Claude

28.08%
按下载量换算40

Cursor

17.17%
按下载量换算25

Gemini CLI

10.21%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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