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feed-catchup饲料追赶

Agent Skill

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。

总安装

6,452

周安装

261

GitHub Stars

201

下载量

2,025
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: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

简介

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。

SKILL.md

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.

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

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

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。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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