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Agent Skill · 研究检索

feed-catchup(饲料追赶)

feed-catchup 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。

feed-catchup 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 17,710。

总安装 6,452周安装 261GitHub Stars 201下载 2,025核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:feed-catchup(饲料追赶)
来源仓库:https://github.com/readwiseio/readwise-skills
仓库路径:skills/feed-catchup
安装命令:
npx skills add https://github.com/readwiseio/readwise-skills --skill feed-catchup
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/readwiseio/readwise-skills --skill feed-catchup

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

feed-catchup 用于批量管理与阅读 Readwise 中的文章收藏与稍后读列表。适合知识工作者快速浏览信息流、标记已读或归档重要内容。支持按兴趣分类批量操作,如移至稍后、加入收件箱或展开详情。依赖 Readwise 账户授权,无法访问未同步至该平台的内容源。

每次调用返回有限数量条目,需多次交互完成全量浏览任务。

来源仓库内路径:skills/feed-catchup

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/readwiseio/readwise-skills --skill feed-catchu…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

feed-catchup 怎么安装?

在终端执行 npx skills add https://github.com/readwiseio/readwise-skills --skill feed-catchup。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

feed-catchup 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

feed-catchup 需要什么权限?

权限级别是调用外部服务。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。

feed-catchup 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

feed-catchup 用的人多吗?

公开数据是累计安装 6,452 次,近一周安装 261 次,下载 2,025 次,GitHub 201 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

feed-catchup 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
feed-catchup

饲料追赶 · 本页

6,452201调用外部服务未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

You are helping the user catch up on their Readwise Reader RSS feed. Follow this process carefully.

Readwise Access

Check if Readwise MCP tools are available (e.g. mcp__readwise__reader_list_documents). If they are, use them throughout. If not, use the equivalent readwise CLI commands instead (e.g. readwise list, readwise read <id>, readwise move <id> <location>). The instructions below reference MCP tool names — translate to CLI equivalents as needed.

Setup

IMPORTANT — do this in a single parallel turn before anything else: Call ToolSearch with query "readwise list documents" AND read reader_persona.md at the same time. Both must happen in the same message as parallel tool calls. The ToolSearch loads the deferred readwise MCP tools so you can call them directly. Never use a Task/subagent to fetch feed data — the overhead makes startup brutally slow.

  1. Check for persona file. (Done in parallel above.) Use it throughout the session to personalize commentary and picks. If no persona file exists, note briefly that feed catchup will be less personalized and suggest running build-persona first — but proceed without waiting. If you show this message, add · · · after it.
  2. Fetch feed documents. Call mcp__readwise__reader_list_documents with location="feed", limit=20, and response_fields=["title", "author", "category", "word_count", "reading_time", "summary", "url", "site_name", "published_date", "saved_at", "first_opened_at"]. Documents come back most-recently-saved first. Filter to items where first_opened_at is null (unseen). If you have fewer than 20 unseen items and a nextPageCursor is returned, paginate until you have 20 unseen items OR the cursor runs out. Hold all unseen items in memory. (Note: the list API does not support server-side seen filtering — client-side first_opened_at check is required.)
  3. If truly nothing left: Only declare the feed fully caught up if you paginated through multiple pages and found zero unseen items. In that case, say so briefly and end.
  4. Pick the top 5. From the collected unseen items, select the 5 most worth reading based on the persona (if available) or general signal quality. Prioritize: high-density insight, direct relevance to their current interests, first-person operator takes, and novelty.

Opening Format

Render the overview exactly like this:

📡 Reader Feed

{1-2 sentences explaining what you looked at and what stood out — e.g. "Scanned the last 20 unseen items. AI and software architecture dominate, with a few standouts worth pulling."}

Today's picks *(spanning {human-readable time range, e.g. "the last 8 hours" or "Feb 24–26"})*:

#TitleSourceTimeWhy
1Titlesite_namereading_timeOne-line reason this made the cut
2............

{1-2 sentences of commentary on the picks as a set — what the pattern is, or why these five in particular.}

· · ·

Want to act on any of these, or browse everything?

  • Later N / Inbox N / Shortlist N / Archive N — move a pick
  • Show N — get a deeper summary
  • Read N — open in Reader
  • Browse all — go through all unseen items in batches of 20

Browse Loop

If the user says "browse all" (or similar), enter the batch-by-batch loop. Present unseen items 20 at a time:

The Table

Before the table, add a single line with the time range covered by the batch, e.g. *"Feb 26, 3:00–11:00 PM"* or *"last 4 hours"* — derived from the saved_at values of the items in that batch.

#TitleSourceTimeSummary
1Titlesite_namereading_timeBrief summary from metadata — one line, truncated if needed
2............

After the table, give a brief commentary (1-2 sentences) on the batch — what stands out relative to their interests.

Options

  • Mark all seen — mark the batch as seen and load the next 10
  • Later N — move to Later (you can also move to Inbox/Shortlist/Archive)
  • Show N — get a deeper summary (or the full content if short)
  • Read N — open in Reader

*(You can act on multiple items at once, e.g. "later 2, 5, 8")*

Handling Responses

  • "Mark all seen" / "next" / "seen" — Call mcp__readwise__reader_bulk_edit_document_metadata with documents set to [{document_id: <id>, seen: true},...] for every document in the current batch. This is a single call, not one per document. Do not move or archive them. Then display the next batch of 20.
  • "Later N" — Move that document to later location. Confirm briefly, then continue.
  • "Later N, N, N" — Move multiple documents to later. Confirm briefly.
  • "Inbox N" / "Shortlist N" / "Archive N" — Move to the specified location (new, shortlist, or archive). Confirm briefly.
  • "Show N" — Fetch full content using mcp__readwise__reader_get_document_details. If the document is 3 mins or under, show the full content verbatim — no summary. If over 3 mins, give a richer summary with why-read/why-skip reasoning. Then re-present the options.
  • "Read N" — Provide the Reader link (https://read.readwise.io/read/{id}) so they can open it directly.
  • "Stop" / "done" — End the session with a brief summary of what was processed (how many seen, how many pulled).

Transitions

When loading the next batch, use · · · as a visual separator before the next table.

平台分布

Codex

34.4%
按下载量换算697

Claude

32.4%
按下载量换算656

Cursor

19.06%
按下载量换算386

Gemini CLI

10.75%
按下载量换算218

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

调用外部服务

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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