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constructionismconstructionism 搜索

Agent Skill

constructionism 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

321

周安装

13

GitHub Stars

37

下载量

101
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:constructionism(constructionism 搜索)
来源仓库:https://github.com/simhacker/moollm
仓库路径:skills/constructionism
安装命令:
npx skills add https://github.com/simhacker/moollm --skill constructionism
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/simhacker/moollm --skill constructionism

简介

Constructionism 倡导通过动手构建可检视的对象来深化理解,反对被动接受抽象理论。

  • 适用于教育科技、编程入门与创客文化等领域,强调“做中学”的学习方法论。
  • 通过 GitHub 安装并使用 npx skills add 命令添加,以 Logo 语言为例演示 microworld 教学理念。
  • MOOLLM 作为 turtle graphics 的现代化实现,支持符号推理与可视化调试结合。
  • constructionism 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Constructionism

*"If you can build it, you can understand it. If you can inspect it, you can trust it."*

Seymour Papert's educational philosophy: you learn best by building things you can inspect and modify. Not passive consumption. Not abstract explanation. Construction.

The Tradition

Logo Microworlds — Children don't learn geometry from textbooks. They teach a turtle to draw shapes:

TO SQUARE :SIZE
  REPEAT 4 [FORWARD :SIZE RIGHT 90]
END

The child:

  1. Builds the procedure
  2. Runs it and sees results
  3. Debugs when it's wrong
  4. Understands geometry through construction

MOOLLM as Microworld

LogoMOOLLM
TurtleAgent/Character
CanvasRoom floor
ProceduresSkills
VariablesYAML state
DrawingFile creation

Everything is inspectable. Open ROOM.yml — see the state. Read session-log.md — see the history. Modify character.yml — change the world.

Core Principles

Low Floor

Easy to start — no setup, just explore:

> LOOK
You are in the workshop.
> EXAMINE hammer
A simple claw hammer.

High Ceiling

Unlimited complexity — build custom skills, complex pipelines, new protocols.

Wide Walls

Many paths to many goals — adventure games, workflow automation, knowledge organization.

Learning by Doing

The Debug Cycle

  1. Try something — it doesn't work
  2. Inspect state — see what happened
  3. Hypothesize — "maybe the path is wrong"
  4. Modify and retry — test the hypothesis
  5. Understand — now you know how it works

Cheating is Learning

From Don's Logo Adventure:

Type PRINT:ITEMS to see where everything is. Type MAKE "RNUM 5 to teleport to the treasure room. If you cheat, you win by learning Logo.

"Cheating" in MOOLLM:

> Open character.yml directly
> Add "magic_sword" to inventory
> You've learned YAML and file structure!

The system rewards curiosity with knowledge.

Micropolis: The Dream

Don's Micropolis for OLPC applied the same philosophy to SimCity:

  • Open source simulation
  • Scriptable in Python
  • Kids can modify the rules
  • The city IS the curriculum

MOOLLM applies this to LLM agents:

  • Open file state
  • Scriptable in any language
  • Users can modify the rules
  • The filesystem IS the microworld

Embed Micropolis in MOOLLM

cities/downtown/
├── ROOM.yml           # Room metadata
├── city.save          # Micropolis save file
├── state.yml          # Extracted game state
├── newspaper/         # Generated stories
├── advisors/          # Expert cards
└── session-log.md

LLM reads state, plays the game, summons advisors.

PLAY-LEARN-LIFT

Constructionism in action:

  1. PLAY — Explore manually, make messes
  2. LEARN — Notice patterns, understand
  3. LIFT — Extract principles, create skills

You don't design skills in the abstract. You build them from experience.


Drescher's Schema Mechanism

Gary Drescher's *Made-Up Minds* (1991) extends constructionism into a computational theory of how minds learn causal models. Drescher was a student of Minsky at MIT.

See: ../schema-mechanism/ for the full treatment.

The Core Idea

A schema is a causal unit: Context → Action → Result

The agent discovers schemas through experience, refining them via marginal attribution -- tracking which conditions correlate with success.

Connection to PLAY-LEARN-LIFT

Schema MechanismPLAY-LEARN-LIFT
ACT + OBSERVEPLAY
ATTRIBUTE (marginal attribution)LEARN
SPIN OFF (refine schemas)LIFT

Why LLMs Complete Drescher

AspectDeterministicLLM + YAML Jazz
ItemsOpaque tokensGrounded meanings
PatternsStatistical correlationSemantic understanding
Spin-offsMechanicalCreative generalization
ExplanationsNoneNatural language
*"The YAML provides the skeleton; the LLM provides the soul."*

The Insight

*"If you can build it, you can understand it."* *"If you can inspect it, you can trust it."* *"The filesystem IS the microworld."*

See also: schema-mechanism for Drescher's computational extension.

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

36.97%
按下载量换算37

Claude

29.18%
按下载量换算29

Cursor

20.48%
按下载量换算21

Gemini CLI

9.78%
按下载量换算10

安全审计

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通过

Socket

通过

Snyk

通过

权限和风险

可写文件

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安装前确认

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来源信息

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