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algernon-synthesis阿尔杰农合成

Agent Skill

algernon-synthesis 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

5,988

周安装

252

GitHub Stars

公开资料未说明

下载量

2,097
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-synthesis(阿尔杰农合成)
来源仓库:https://github.com/antoniovfranco/algernon-synthesis
安装命令:
openclaw skills install algernon-synthesis
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-synthesis

简介

algernon-synthesis 实现跨材料知识综合会议,促进知识点关联与整合。

  • 适合在 OpenClaw 中需要连接不同学习资料、构建知识图谱或总结归纳时使用。
  • 通过运行 /algernon Synthesis 命令激活,自动分析材料并生成关联内容。
  • 安装前请确认权限范围和维护状态,注意可能涉及文件读取和文本处理。
  • 建议结合原始 README 了解支持的输入格式和输出结构。

SKILL.md

name
algernon-synthesis
description
>

algernon-synthesis

You run a cross-material synthesis session. The goal is to build explicit connections between concepts learned in different materials — the kind of holistic understanding that separates someone who memorized facts from someone who can actually design systems.

Constants

DB=/home/antonio/Documents/huyawo/estudos/vestibular/data/vestibular.db
NOTION_CLI=~/go/bin/notion-cli

Step 1 — Check Eligibility

sqlite3 $DB \
  "SELECT m.slug, m.name, COUNT(r.id) as review_count
   FROM materials m
   JOIN decks d ON d.material_id = m.id
   JOIN cards c ON c.deck_id = d.id
   JOIN reviews r ON r.card_id = c.id
   GROUP BY m.id
   HAVING review_count > 0
   ORDER BY review_count DESC;"

If fewer than 2 materials have reviews: "Synthesis requires at least 2 studied materials. Study more material first."

Step 2 — Identify Cross-Material Concept Overlaps

From the tags and topics of reviewed cards across all studied materials, identify 3-5 concept pairs that appear in multiple materials but may be understood differently in each context.

Examples of strong synthesis pairs:

  • "evaluation" in RAG vs LLMOps contexts
  • "chunking" in embedding vs RAG contexts
  • "latency" in inference vs retrieval contexts
  • "context" in prompt engineering vs agent memory contexts
  • "retrieval" in BM25 vs vector similarity vs caching contexts

Prefer pairs where the same word genuinely means something different in each context — that contrast is the richest learning opportunity.

Step 3 — Synthesis Questions

For each concept pair, ask:

AskUserQuestion (free text):

"[CONCEPT] appears in both [MATERIAL_A] and [MATERIAL_B]. How does the meaning or role of [CONCEPT] differ between these two contexts? Where do they overlap?"

After each answer, give brief feedback:

  • Name what the user connected well.
  • Name any distinction they missed (without lecturing — one sentence).

Step 4 — Production Scenario Challenge

AskUserQuestion (free text):

"If you were building a production AI system, how would the knowledge from [MATERIAL_A] and [MATERIAL_B] work together? Give a concrete scenario with specific design decisions."

Evaluate for:

  1. Coherence — does the scenario make technical sense?
  2. Specificity — are there real design decisions, not just buzzwords?
  3. Correct use of concepts — are terms from both materials used accurately?

Step 5 — Summary

Display:

Synthesis session complete.
Materials covered: [list]
Conceptual bridges built well: [list]
Bridges that need reinforcement: [list]

Send to Notion

Send to the Notion page of the most recent phase studied:

~/go/bin/notion-cli append --page-id PHASE_PAGE_ID --content "MARKDOWN"

Include:

  • Cross-material concepts explored
  • Gaps identified (bridges that need reinforcement)
  • The production scenario the user described

Save Memory

Append to today's conversation log:

[HH:MM] synthesis session
Materials: [list] | Bridges built: N | Needs reinforcement: [list]

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

93.01%
按下载量换算1,950

安全审计

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可疑

ClawScan

可疑

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