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Agent Skill · 研究检索

tapestry(tapestry 搜索)

tapestry 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。

tapestry 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,权限为需要联网;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 67,168。

总安装 250周安装 10GitHub Stars 54下载 81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:tapestry(tapestry 搜索)
来源仓库:https://github.com/natsufox/tapestry
仓库路径:skills/tapestry
安装命令:
npx skills add https://github.com/natsufox/tapestry --skill tapestry
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/natsufox/tapestry --skill tapestry

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。

用于查找、检索和筛选相关信息,支持基于关键词或任务场景的内容定位。适合在 Codex、Claude、Cursor、Gemini CLI 中快速获取候选结果,提升研究效率。通过 npx 安装,需确认权限范围和维护状态,注意潜在的联网或文件访问行为。建议结合来源仓库和原始 README 核验具体功能与使用限制。

tapestry 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/tapestry

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/natsufox/tapestry --skill tapestry;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

    它需要需要联网,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

tapestry 怎么安装?

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

tapestry 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。

tapestry 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

tapestry 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。

tapestry 用的人多吗?

公开数据是累计安装 250 次,近一周安装 10 次,下载 81 次,GitHub 54 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。

tapestry 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
tapestry

tapestry 搜索 · 本页

25054需要联网未明确有提醒

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

🧵 Tapestry

AI-Native Web Intelligence Workflow

Tapestry is a complete workflow for turning scattered web content into organized, searchable knowledge. It handles everything from crawling to synthesis across multiple platforms.

When to Use Tapestry

Use this skill when the user:

  • Shares URLs to ingest (from Zhihu, Reddit, HN, X/Twitter, Xiaohongshu, Weibo, or any webpage)
  • Wants to archive or capture web content
  • Asks to organize content into a knowledge base
  • Wants structured feeds or analysis of web content
  • Needs to visualize their knowledge base
  • Wants to manage RSS/Atom feed subscriptions (list, add, remove sources)
  • Wants to refresh or fetch the latest content from subscribed sources

Workflow Overview

Tapestry has eight internal sub-skills that work together:

  1. Init Deps Install (init-deps-install/SKILL.md) - Auto-triggered dependency installation (Phase 0: setup)
  2. Ingest (ingest/SKILL.md) - Crawl and capture URLs (Phase 1: deterministic)
  3. Synthesis (synthesis/SKILL.md) - Analyze and organize into knowledge base (Phase 2: AI-driven)
  4. Feed (feed/SKILL.md) - Generate structured, source-aware feeds
  5. Display (display/SKILL.md) - Visualize the knowledge base as a website
  6. Visual Card (visual-card/SKILL.md) - Generate visual note cards from KB content
  7. Subscriptions (subscriptions/SKILL.md) - Manage RSS/Atom sources and refresh them into the knowledge base
  8. Export (export/SKILL.md) - Export KB content to PDF, Markdown, or HTML files saved under _data/exports/

Two-Phase Architecture

Tapestry uses a two-phase design with automatic dependency setup:

Phase 0 (Init): Auto-triggered dependency installation when first launched or dependencies missing Phase 1 (Ingest): Deterministic extraction → _data/notes/YYYY/MM/ (date-organized) Phase 2 (Synthesis): AI-driven analysis → _data/books/{topic}/{chapter}/ (book-organized)

By default, synthesis runs automatically after each ingest (synthesis.mode: "auto" in tapestry.config.json). You can configure this to "manual" or "batch" mode.

How to Use This Skill

Step 1: Understand the User's Intent

Analyze what the user wants:

  • Just capture? → Use ingest only (set synthesis mode to "manual" if needed)
  • Structured feed? → Use ingest + feed
  • Analysis/organization? → Use ingest + synthesis (default with "auto" mode)
  • Browse knowledge base? → Use display
  • Visual summary? → Use visual-card to generate infographic cards
  • Complete workflow? → Ingest automatically triggers synthesis in "auto" mode
  • Manage subscriptions? → Use subscriptions (list/add/remove sources)
  • Refresh subscribed feeds? → Use subscriptions fetch → ingest → synthesis pipeline

Step 2: Execute the Appropriate Sub-Skill(s)

Read and follow the instructions in the relevant sub-skill SKILL.md files:

For Dependency Installation (Auto-Triggered)

Read the file init-deps-install/SKILL.md to understand the detection and installation workflow. All installation steps require explicit user confirmation before execution.

For URL Ingestion

Read the file ingest/SKILL.md, then follow the workflow described there.

For Structured Feeds

Read the file feed/SKILL.md, then follow the workflow described there.

For Synthesis & Knowledge Base

Read the file synthesis/SKILL.md, then follow the workflow described there.

For Visualization

Read the file display/SKILL.md, then follow the workflow described there.

For Visual Cards

Read the file visual-card/SKILL.md, then follow the workflow described there.

For Subscription Management & Feed Refresh

Read the file subscriptions/SKILL.md, then follow the workflow described there.

Step 3: Chain Sub-Skills When Needed

The sub-skills reference each other. When one sub-skill says to "route to" another, read that sub-skill's SKILL.md and follow its instructions.

Supported Platforms

  • 🇨🇳 Zhihu: Questions, answers, articles, profiles
  • 🐦 X/Twitter: Posts, threads
  • 📱 Xiaohongshu: Notes, profiles
  • 🇨🇳 Weibo: Posts
  • 🔶 Hacker News: Discussions with full comment trees
  • 🤖 Reddit: Threads
  • 🌐 Generic HTML: Any webpage (fallback)

Key Principles

  1. Read sub-skill instructions first: Each sub-skill's SKILL.md contains detailed workflow steps
  2. Follow the deterministic pipeline: Ingest → Feed → Synthesis → Display
  3. Respect user intent: Don't over-process if they just want capture
  4. Chain appropriately: Let sub-skills hand off to each other as described in their instructions
  5. Report clearly: Tell the user what was done and where artifacts are stored

Directory Structure

tapestry/
├── SKILL.md (this file)          # Main orchestrator
├── init-deps-install/SKILL.md     # Dependency installation sub-skill
├── ingest/SKILL.md                # URL crawling sub-skill
├── feed/SKILL.md                  # Feed generation sub-skill
├── synthesis/SKILL.md             # Analysis & KB sub-skill
├── display/SKILL.md               # Visualization sub-skill
├── visual-card/SKILL.md           # Visual card generation sub-skill
├── subscriptions/SKILL.md         # RSS/Atom subscription management sub-skill
├── _src/                          # Shared code (crawlers, parsers, storage)
└── _tests/                        # Unit tests

Example Usage Patterns

Pattern 1: Simple Capture

User: "Ingest this Zhihu answer: https://www.zhihu.com/question/123/answer/456"

Action:
1. Read ingest/SKILL.md
2. Follow its workflow to capture the URL
3. Report the created artifacts

Pattern 2: Structured Feed

User: "Give me a structured feed of this Reddit thread"

Action:
1. Read ingest/SKILL.md and capture the URL
2. Read feed/SKILL.md and generate the structured feed
3. Present the feed to the user

Pattern 3: Full Analysis (Default)

User: "Analyze this HN discussion and add it to my knowledge base"

Action:
1. Read ingest/SKILL.md and capture the URL
2. Synthesis runs automatically (default "auto" mode)
3. Report where the content was organized in the KB

Pattern 4: Manual Synthesis

User: "Just capture these URLs for now, I'll organize them later"

Action:
1. Set synthesis mode to "manual" in tapestry.config.json
2. Read ingest/SKILL.md and capture the URLs
3. User can later run synthesis on selected URLs

Pattern 4: Batch Processing

User: "Ingest these 5 URLs and organize them"

Action:
1. Read ingest/SKILL.md and batch process all URLs
2. With "auto" mode, synthesis runs for each URL automatically
3. Summarize the results

Pattern 5: Subscription Refresh

User: "Refresh all my subscribed feeds"

Action:
1. Read subscriptions/SKILL.md
2. Run: python subscriptions/_scripts/run.py fetch
3. Collect the printed URLs
4. Pass them to $tapestry-ingest for ingestion
5. Let the synthesis pipeline run per config

Pattern 6: Export KB Content

User: "Export the entire knowledge base as PDF"

Action:
1. Read export/SKILL.md
2. Run: python export/_scripts/export.py --format pdf --scope kb
3. Report the output paths from _data/exports/pdf/

Important Notes

  • Always read the sub-skill SKILL.md files: They contain the actual implementation details
  • Don't invent workflows: Follow what's written in the sub-skill instructions
  • Preserve artifacts: The pipeline creates _data/captures/, _data/feeds/, _data/notes/, _data/books/, and _data/exports/
  • Respect the architecture: Ingest is deterministic, synthesis is interpretive
  • Check for errors: Sub-skills may fail; handle gracefully and report to user

Resources

  • _src/: Shared Python code for crawlers, parsers, and storage
  • _tests/: Unit tests for the shared code
  • Each sub-skill has its own _scripts/, _specs/, or _kb_rules/ directories

Remember: This main skill is an orchestrator. The real work happens in the sub-skills. Read their SKILL.md files and follow their instructions.

平台分布

Codex

38.19%
按下载量换算31

Claude

28.2%
按下载量换算23

Cursor

18.71%
按下载量换算15

Gemini CLI

10.19%
按下载量换算8

安全审计

Gen Agent Trust Hub

可疑

Socket

可疑

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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