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systems-thinking系统思维

Agent Skill

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

759

周安装

31

GitHub Stars

3

下载量

246
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:systems-thinking(系统思维)
来源仓库:https://github.com/oldwinter/skills
仓库路径:skills/systems-thinking
安装命令:
npx skills add https://github.com/oldwinter/skills --skill 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/oldwinter/skills --skill systems-thinking

简介

用于查找、检索和筛选相关信息,支持系统思维相关研究任务。

  • 适用于需要根据关键词快速定位系统架构或复杂问题分析的场景。
  • 通过 GitHub 安装并使用 npx skills add 命令集成到系统设计流程。
  • 建议在使用前确认权限范围和维护状态,避免触发未预期的网络请求。
  • systems-thinking 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Systems Thinking

Scope

Covers

  • Seeing the “whole system” behind a problem (actors, incentives, feedback loops, culture/rules)
  • Anticipating second- and third-order effects (including time delays)
  • Finding leverage points (small changes with outsized impact)
  • Converting recurring pain into a reusable system (process, automation, or operating mechanism)

When to use

  • “This is a complex ecosystem; we’re missing the bigger picture.”
  • “What are the second-order effects if we do X?”
  • “We keep solving symptoms—what’s the system causing this?”
  • “Map the players + incentives and how they interact.”
  • “We need to redesign a process/org without unintended consequences.”

When NOT to use

  • The problem is simple/linear and mostly execution (use a project plan/timeline).
  • You need primary user research or data you don’t have (do discovery first).
  • You need deep quantitative forecasting/simulation (this skill produces a qualitative map + risks, not a full model).
  • The decision is low-impact and fully reversible (don’t over-invest).

Inputs

Minimum required

  • The focal decision or problem statement (1–2 sentences)
  • Desired outcome + time horizon (default: 6–12 months)
  • Known constraints/guardrails (trust, safety, compliance, budget, headcount)
  • Known actors/stakeholders (teams, users, partners, regulators, vendors)
  • What has been tried already (and what happened)

Missing-info strategy

  • Ask up to 5 questions from references/INTAKE.md.
  • If answers aren’t available, proceed with clearly labeled assumptions and provide 2–3 alternative system framings/boundaries.

Outputs (deliverables)

Produce a Systems Thinking Pack in Markdown (in-chat; or as files if requested) in this order:

  1. Context + System boundary (goal, scope, non-scope, time horizon)
  2. Actors & incentives map (players, goals, constraints, power, conflicts)
  3. System map (key variables + causal links) + feedback loops (reinforcing/balancing) + time delays
  4. Second-/third-order effects ledger for the top 1–3 decisions
  5. Leverage points + intervention plan (actions, owners, sequencing, guardrails)
  6. System-build opportunities (what to automate/standardize to reduce recurring pain)
  7. Risks / Open questions / Next steps (required)

Templates: references/TEMPLATES.md

Workflow (8 steps)

1) Intake + pick the focal decision/problem

  • Inputs: User context; use references/INTAKE.md.
  • Actions: Restate the focal decision/problem, desired outcome, and time horizon; list constraints/guardrails.
  • Outputs: Draft Context + System boundary.
  • Checks: The problem is not a solution in disguise; scope and non-scope are explicit.

2) Define the system boundary (what’s “in” vs “out”)

  • Inputs: Problem statement + constraints.
  • Actions: Choose a boundary that is useful (not everything). Name the primary outcome metric(s) and a few leading indicators.
  • Outputs: Boundary statement + success measures.
  • Checks: Boundary is tight enough to act on, but wide enough to include key externalities.

3) Map actors + incentives (multi-agent reality)

  • Inputs: Boundary + stakeholder list.
  • Actions: Enumerate actors/players; capture incentives, constraints, power, and likely behaviors.
  • Outputs: Actors & incentives map (table).
  • Checks: Includes at least 1–2 “invisible” actors (e.g., policies, culture norms, platform constraints) if relevant.

4) Build a simple system map (variables + causal links)

  • Inputs: Actors map + known dynamics.
  • Actions: List key variables; map causal links (“A increases B”, “C decreases D”); mark time delays.
  • Outputs: System map (text/table) with 10–20 high-signal links.
  • Checks: Links are directional and testable; avoids buzzwords (“alignment”, “quality”) without definition.

5) Identify feedback loops + time delays

  • Inputs: System map.
  • Actions: Extract reinforcing and balancing loops; note where delays create overshoot/oscillation; flag common traps.
  • Outputs: Feedback loops section (2–6 loops) + delays list.
  • Checks: Each loop has a short “so what” describing the pattern it creates.

6) Run second-/third-order effects on 1–3 candidate moves

  • Inputs: Candidate decisions/actions.
  • Actions: For each move, enumerate first-, second-, and third-order effects; include who wins/loses and what constraints tighten over time.
  • Outputs: Second-/third-order effects ledger.
  • Checks: Includes at least one unintended consequence + one mitigating action per move.

7) Choose leverage points + design interventions (including “build a system”)

  • Inputs: Loops + effects ledger.
  • Actions: Identify leverage points (policy, incentives, information flows, tooling, process); propose interventions; include at least one system-build/automation opportunity for recurring pain.
  • Outputs: Leverage points + intervention plan + System-build opportunities.
  • Checks: Each intervention has an owner, a measurable leading indicator, and a guardrail.

8) Quality gate + finalize pack

  • Inputs: All draft sections.
  • Actions: Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks / Open questions / Next steps.
  • Outputs: Final Systems Thinking Pack.
  • Checks: A reader can act without a live meeting; trade-offs and uncertainties are explicit.

Quality gate (required)

Examples

Example 1 (Org/process): “Our on-call load keeps rising and teams are burned out. Map the system and propose leverage points.” Expected: an actors/incentives map (teams, incidents, incentives), feedback loops (firefighting loop), effects ledger for candidate changes, and an intervention plan with guardrails.

Example 2 (Product ecosystem): “We’re changing API pricing; what are the second-order effects across partners and customer segments?” Expected: system boundary + actors map (customers/partners/internal), loops and delays, effects ledger, and a sequencing/mitigation plan.

Boundary example: “Write a status update about this week’s tasks.” Response: this skill is for complex systems/decisions. Suggest a project update format instead; only use this skill if there’s a systemic pattern to diagnose.

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

37.45%
按下载量换算92

Claude

31.08%
按下载量换算76

Cursor

16.72%
按下载量换算41

Gemini CLI

9.81%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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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