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algernon-texto阿尔杰农·泰克托

Agent Skill

algernon-texto 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。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

6,350

周安装

270

GitHub Stars

公开资料未说明

下载量

2,225
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-texto(阿尔杰农·泰克托)
来源仓库:https://github.com/antoniovfranco/algernon-texto
安装命令:
openclaw skills install algernon-texto
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-texto

简介

algernon-texto 提供材料的逐块读取模式,支持分步阅读和理解。

  • 适用于需要分段处理长文档、学术论文或复杂说明材料的场景。
  • 通过 /algernon texto SLUG 或 /algernon paper SLUG 命令调用,逐步呈现内容。
  • 安装前请确认权限范围和维护状态,注意可能涉及网络请求和文件访问。
  • 建议结合原始 README 了解支持的 SLUG 命名规则和内容来源。

SKILL.md

name
algernon-texto
version
1.0.0
description
>

algernon-texto

You deliver material content block by block with an interactive tool menu after each block. The goal is active reading — the user engages with each block before moving on.

Constants

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

Step 1 — Load Material

sqlite3 "$DB" "SELECT id, name, local_path FROM materials WHERE slug = 'SLUG';"

If no result, stop: "Material 'SLUG' not found. Run list to see installed materials."

Read LOCAL_PATH/algernon.yaml to get:

  • content: list of content files
  • sections: section titles mapped to file names

Read all content files and split into blocks of approximately 300 words each. Preserve section boundaries — never split mid-sentence at a section change.

Step 2 — Display Session Header

================================================
 SLUG — mode: texto (or: paper)
 N blocks total
================================================

Step 3 — Block Delivery Loop

For each block, display:

────────────────────────────────────────────────
 Block N/TOTAL · SECTION_TITLE
────────────────────────────────────────────────

[Block content]

────────────────────────────────────────────────
 /continue    /explain [term]    /example
 /analogy     /summarize         /test
 /map         /deep-dive
────────────────────────────────────────────────

Present as an AskUserQuestion with the tool options above.

Tool Behaviors

ToolWhat to do
/continueAdvance to the next block
/explain XDefine X at N1 level first. Ask if user wants N2 before going deeper.
/exampleGive a concrete real-world example of the main concept in this block
/analogyCreate an original analogy that maps the concept to something familiar
/summarizeSummarize the block in 2-3 sentences; ask user to add anything missed
/testAsk 1 quick comprehension question about this block; give feedback
/mapShow how this concept connects to others already covered in this material
/deep-diveExpand the block's core concept to N2/N3 depth; note as focus for cards

After any tool response, re-display the current block menu so the user can continue or use another tool.

Paper Mode Additions

In paper mode, content is structured as: Abstract → Methodology → Results → Implications

Between sections, before showing the first block of the new section:

"Summarize what you understood from [previous section] before we continue."

(Free text — acknowledge and move on without grading.)

Track which terms the user used /explain or /deep-dive on. Pass this list to card generation at the end as additional focus concepts.

Step 4 — Session End

When the last block is delivered and the user selects /continue:

Material complete: MATERIAL_NAME
Sections covered: N
Key concepts explored: [list of terms where user used /explain or /deep-dive]

Generate Cards

Generate cards for this material. Follow the card generation rules in algernon-content:

  • Distribution: 50% flashcard, 30% dissertative, 20% argumentative
  • All cards start at N1
  • Prioritize concepts from the /explain and /deep-dive list

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"

Content to include: key concepts (N1/N2/N3), concepts the user explored deeply, flashcards generated. This step is skipped silently if Notion is not configured.

Save Memory

Append a summary to today's conversation log:

echo "[HH:MM] texto session -- MATERIAL_NAME | Blocks: N/TOTAL | Cards: N" \
  >> "${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

94.35%
按下载量换算2,099

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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