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reader-recap读者回顾

Agent Skill

reader-recap 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。

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6,528

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272

GitHub Stars

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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:reader-recap(读者回顾)
来源仓库:https://github.com/readwiseio/readwise-skills
仓库路径:skills/reader-recap
安装命令:
npx skills add https://github.com/readwiseio/readwise-skills --skill reader-recap
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 reader-recap

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在需要围绕仓库状态、代码变更或协作事项进行整理的场景中使用。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限和维护状态。
  • 使用前建议核实是否会触发联网、命令执行或文件读写等操作。
  • 可结合原始 README 和仓库路径进一步核验具体用法和功能边界。

SKILL.md

You are summarizing the user's recent reading activity from Readwise Reader. 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 highlights <id>). The instructions below reference MCP tool names — translate to CLI equivalents as needed.

Setup

  1. Check for persona file. Read reader_persona.md in the current working directory if it exists. Use it to personalize the briefing tone and to contextualize the user's annotations (e.g. connecting highlights to their known interests). If no persona file exists, proceed without it — the recap works fine standalone.
  2. Determine time window. Parse the argument as a number of days. Default to 1 (last 24 hours) if no argument is given.
/reader-recap           # last 24 hours
/reader-recap 7         # last 7 days
/reader-recap 30        # last 30 days

Step 1: Fetch Recent Documents

Query Reader for documents the user archived or moved to "later" within the time window. Run both calls in parallel:

mcp__readwise__reader_list_documents(location="archive", updated_after=<cutoff>, limit=100, response_fields=["title", "author", "category", "notes", "source_url"])
mcp__readwise__reader_list_documents(location="later", updated_after=<cutoff>, limit=100, response_fields=["title", "author", "category", "notes", "source_url"])

Combine and deduplicate results by document ID.

If no documents are found, report "No archived or moved documents in the last [N] days" and stop.

Step 2: Get Highlights

For each document returned, fetch highlights:

mcp__readwise__reader_get_document_highlights(document_id=<id>)

After fetching, split documents into three groups:

  • Annotated — has highlights where the user left notes (the note field is non-empty), OR has a non-empty document-level notes field
  • Highlighted only — has highlights but none with user notes
  • No engagement — zero highlights and no document-level notes (archived without reading)

Step 3: Classify Annotations

For each annotated document, scan the user's notes on highlights and the document-level notes field. Flag anything actionable:

FlagPattern
QuestionContains "?" or asks something
TODOSays to look up, verify, follow up, try, or do something
IdeaConnects multiple concepts or proposes something new
DisagreementPushes back on the source's claim
Cross-referenceMentions another book, article, or author by name

Annotations that don't match any pattern are just context — the user thinking out loud. Still include them in the briefing.

Step 4: Write the Briefing

Write a conversational recap — like a well-read assistant catching someone up over coffee. Warm, concise, and useful. Not a data dump.

Structure:

  1. Opening line — a one-sentence overview of the time period. "You had a busy week — 12 articles and a book, mostly history and AI stuff." "Quiet day — just two articles, but you had a lot to say about one of them."
  2. Per-document paragraphs — one short paragraph per annotated document, ordered by engagement (most annotated first). Each paragraph should naturally weave together:

- What the piece was about (1 sentence) - How much the user highlighted (folded into the flow, not as a stat line) - The most interesting annotations, especially actionable ones — paraphrase the user's notes conversationally: "You had a question about whether this applies to mammals too." "You noted you want to try this workflow yourself." "You pushed back on the author's claim about pricing." - If the user left a document-level note, lead with it — it's usually the overall reaction

  1. Light reads — a single sentence listing documents the user highlighted but didn't annotate. "You also highlighted a few things in [Title] and [Title] but didn't leave notes."
  2. Action items — if any annotations were flagged as TODOs, questions, or ideas, collect them at the end as a short bulleted list under "Things you might want to follow up on:". Skip this section entirely if nothing is actionable.

Example output:

Busy couple of days — you finished 8 articles and a chunk of that Ottoman
history book. Most of your attention went to the logistics stuff.

You were really into Sarah Chen's piece on supply chain resilience. 14
highlights, and you left a note saying the comparison to Roman grain
logistics was "exactly what I've been looking for." You also flagged a
question — whether the same bottleneck pattern shows up in digital
infrastructure.

The Ottoman book got 9 new highlights across three chapters. Your note on
the harem education system connected it to that article about elite
training programs you read last month. You also marked a claim about
succession rates that you want to verify.

You also highlighted a few things in "Why Bridges Fail" and a Substack
post about medieval farming, but didn't leave notes on either.

**Things you might want to follow up on:**
- Does the supply chain bottleneck pattern apply to digital infrastructure?
- Verify the Ottoman succession rate claim (Chapter 7)
- You wanted to connect harem education to the elite training piece

Tone rules:

  • Second person ("you read", "you noted"), not third person
  • Contractions are fine
  • No bullet-point lists for the main body — prose paragraphs only
  • Keep it skimmable — short paragraphs, one idea each
  • Don't editorialize on the content itself — just report what the user did and said
  • The action items list at the end IS bulleted — that's the one exception
  • If the persona file exists, use it to add context (e.g. "this connects to your interest in X")

Notes

  • This skill is read-only. It doesn't move, tag, or modify any documents.
  • The "archive" and "later" locations catch most finished reading. Documents still in "feed" or "new" are works in progress and aren't included.
  • Annotations (the user's own notes on highlights) are the interesting part. Raw highlights without notes are counted but not shown individually.
  • For large time windows (30 days), there may be many documents. Prioritize the most-annotated ones and summarize the rest in aggregate rather than writing a paragraph for every single one.

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

37.46%
按下载量换算815

Claude

29.12%
按下载量换算634

Cursor

18.79%
按下载量换算409

Gemini CLI

9.9%
按下载量换算215

安全审计

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通过

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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

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来源信息

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