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archive-suggest存档建议

Agent Skill

archive-suggest 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

419

周安装

18

GitHub Stars

4

下载量

147
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:archive-suggest(存档建议)
来源仓库:https://github.com/cdeistopened/opened-vault
仓库路径:skills/archive-suggest
安装命令:
npx skills add https://github.com/cdeistopened/opened-vault --skill archive-suggest
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/cdeistopened/opened-vault --skill archive-suggest

简介

用于从存档内容生成每日社交媒体发布建议。

  • 挖掘可再利用的常青内容进行重新分发。archive-suggest 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 提供主内容索引扫描和候选内容筛选机制。
  • 安装需从指定 GitHub 仓库获取,使用时需确认内容数据库路径。
  • 涉及内容生成时应注意版权和引用规范。

SKILL.md

Archive Suggest

Generate daily social post suggestions from archive content. Surfaces evergreen gems for repurposing.

When to Use

  • Daily content suggestion routine
  • When looking for easy content wins
  • To keep evergreen content circulating

Sources to Scan

  1. Master Content Index - .claude/references/Master_Content_Index.md

- 48 Blog posts - 286 Daily newsletters - 66 Podcast episodes

  1. Content Database - Content/Master Content Database/

- Full content files

  1. Podcast Transcripts - Studio/Podcast Studio/*/transcript.md

The Process

Step 1: Candidate Selection

Scan Master Content Index for pieces worth resurfacing.

Selection Criteria:

CriteriaWeightNotes
Evergreen topicHighNot time-sensitive news
Seasonal relevanceHighBack-to-school (Aug), tax season (Jan-Apr), summer (May-Jun)
News hookHighCurrent event relates to old content
High-performing tagsMediumTopics with proven engagement
UnderutilizedMediumGood content not recently shared

Seasonal Triggers:

MonthThemes
Jan-FebNew year resolutions, tax planning, semester start
Mar-AprSpring planning, testing season, summer prep
May-JunEnd of year, summer activities, deschooling
Jul-AugBack to school, curriculum planning, getting started
Sep-OctSettling in, adjustments, fall activities
Nov-DecHolidays, gift guides, year reflection

Output: 3-5 candidate pieces

Step 2: Extract Key Snippets

For each candidate, read the full content and extract:

  1. Best standalone insight (1-2 sentences)
  2. Key quote or stat (if applicable)
  3. Why share now (evergreen/seasonal/news hook)

Snippet format:

## Candidate: [Title]
**Published:** [Date]
**URL:** [URL]
**Type:** Blog / Daily / Podcast

**Best snippet:** "[1-2 sentence insight]"

**Why now:** [Evergreen / Seasonal: [reason] / News hook: [event]]

**Tags:** [relevant tags]

Step 3: Quick Framework Fit

Load TEMPLATE_INDEX.md and generate draft posts.

For each snippet:

  1. Match to 1-2 best templates
  2. Generate LinkedIn draft
  3. Generate X draft
  4. Include link to original article
  5. Add framing ("Still relevant:" or "From our archive:" etc.)

Framing options:

  • "Still relevant today:"
  • "From the archive:"
  • "This holds up:"
  • "Timely reminder:"
  • [No framing - just post the content]

Step 4: Post to Slack

Post suggestions to #content-inbox (C0ABV2VQQKS) using the standard format.

Slack Message Format:

*[Article Title]*
_Archive | [Type: Blog/Daily/Podcast] | Published [date]_

[Best standalone snippet - 1-2 sentences]

OpenEd angle: [Why share now - evergreen/seasonal/news hook]
Suggested: LinkedIn, X

[URL]

Note: No emojis in the main format. Reactions (✍️ to develop, to skip) are added by users.


Daily Run Checklist

  • Scan Master Content Index
  • Identify 3-5 candidates (mix of evergreen + seasonal)
  • Extract snippets from each
  • Generate draft posts (LinkedIn + X minimum)
  • Post to Slack #content-inbox (C0ABV2VQQKS)
  • Log selections in Performance tracking

Archive Scoring Heuristics

High Priority (Always Consider)

  • Back-to-school content - Jul/Aug
  • Getting started guides - Any new family influx period
  • Method explainers - (Montessori, Classical, Unschooling) - Evergreen
  • Career prep - High engagement topic
  • Socialization - Evergreen FAQ

Medium Priority

  • Curriculum guides - Before semester starts
  • Subject-specific - When seasonal (science fair season, etc.)
  • Tool recommendations - When tool is in news

Lower Priority (Rotate In)

  • Day in the life profiles - Mix variety
  • Podcast clips - If not recently shared
  • Announcements - Usually not evergreen

Integration Points

  • Slack MCP - Post suggestions
  • Master Content Index - Source database
  • GetLate - If auto-scheduling approved posts
  • Performance tracking - Log what gets used

Related Skills

  • newsletter-to-social - For current content
  • text-content - Template library
  • quality-loop - Quality gates for drafts

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

37.63%
按下载量换算55

Claude

28.63%
按下载量换算42

Cursor

18.96%
按下载量换算28

Gemini CLI

8.81%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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