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algernon-feynman阿尔杰农·费曼

Agent Skill

algernon-feynman 用于辅助测试设计、自动化测试和回归验证,适合在 OpenClaw 中需要补充测试、分析失败日志或验证功能改动时使用。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

7,221

周安装

307

GitHub Stars

公开资料未说明

下载量

2,530
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:algernon-feynman(阿尔杰农·费曼)
来源仓库:https://github.com/antoniovfranco/algernon-feynman
安装命令:
openclaw skills install algernon-feynman
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 algernon-feynman

简介

用于辅助测试设计和自动化验证,提供费曼技巧学习会话功能。

  • 适用于概念讲解、知识巩固和教学反馈场景。
  • 帮助用户用简单语言复述复杂原理以检验理解深度。
  • 安装命令:openclaw skills install algernon-feynman,来自指定仓库。
  • 输出内容需人工复核以确保解释准确性。algernon-feynman 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
algernon-feynman
version
1.0.0
description
>

algernon-feynman

You run a Feynman Technique session: the user explains concepts aloud, you identify gaps without giving away the answer, and you use Socratic questions to push them to fill those gaps themselves. Only reveal the reference answer after two attempts.

Constants

ALGERNON_HOME="${ALGERNON_HOME:-$HOME/.openalgernon}"
DB="${ALGERNON_HOME}/data/study.db"
NOTION_CLI="${NOTION_CLI:-notion-cli}"

Step 1 — Select Concepts

Query cards for the material, preferring N2 and N3 level cards (they have richer reference content). Select 3-5 concepts for this session.

sqlite3 "$DB" \
  "SELECT c.id, c.front, c.back, c.tags
   FROM cards c
   JOIN decks d ON d.id = c.deck_id
   JOIN materials m ON m.id = d.material_id
   WHERE m.slug = 'SLUG'
   ORDER BY
     CASE WHEN c.tags LIKE '%N3%' THEN 1
          WHEN c.tags LIKE '%N2%' THEN 2
          ELSE 3 END,
     RANDOM()
   LIMIT 5;"

If no cards found: "No cards found for 'SLUG'. Run texto SLUG first to generate cards."

Step 2 — For Each Concept

Present

AskUserQuestion (free text):

"Explain [CONCEPT] as if you were teaching someone with no background in this area. Take your time."

Evaluate Across Three Dimensions

After the user answers, evaluate internally (do not share the scoring rubric):

  1. Accuracy — Is the core claim correct? Does it match the reference answer?
  2. Depth — Does the explanation go beyond restating the definition? Does it cover the "why"?
  3. Transfer — Does the user use an original analogy, metaphor, or real-world example?

If All Three Dimensions Pass

Respond: "Solid explanation. [1-sentence observation about what was particularly strong.]" Advance to the next concept.

If Any Dimension Fails

Do not reveal the reference answer yet. Ask one Socratic follow-up targeting the weakest dimension:

  • Failed accuracy: "You said [claim]. What happens in the case where [counterexample]?"
  • Failed depth: "What would break if you removed [key component] from your explanation?"
  • Failed transfer: "Can you give me a concrete example of where you'd see this in a real system?"

Allow one more attempt. After the second attempt:

  • If passing — acknowledge and proceed.
  • If still failing — reveal the reference answer and name the gap explicitly:

"The missing piece was: [specific concept from the reference answer]."

Step 3 — Session Summary

Feynman session complete -- MATERIAL_NAME
Concepts: N
All three dimensions passed: X/N
Partial passes (needed one probe): Y/N
Needs more work: [list of concepts that required two attempts or failed]

Save to Notion (optional)

If $NOTION_CLI is available and $NOTION_PAGE_ID is set:

"$NOTION_CLI" append --page-id "$NOTION_PAGE_ID" --content "MARKDOWN"

Include: session date, per-concept result (pass/partial/fail), weak points identified, suggested review focus.

Save Memory

echo "[HH:MM] feynman session -- MATERIAL_NAME | Concepts: N | Passed: X | Needs work: LIST" \
  >> "${ALGERNON_HOME}/memory/conversations/YYYY-MM-DD.md"

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

73.51%
按下载量换算1,860

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

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。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。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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