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Agent Skill · 研究检索

categorizing-bsky-accounts(Bsky 帐户分类)

categorizing-bsky-accounts 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。

categorizing-bsky-accounts 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 47,393。

总安装 734周安装 30GitHub Stars 118下载 238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:categorizing-bsky-accounts(Bsky 帐户分类)
来源仓库:https://github.com/oaustegard/claude-skills
仓库路径:skills/categorizing-bsky-accounts
安装命令:
npx skills add https://github.com/oaustegard/claude-skills --skill categorizing-bsky-accounts
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/oaustegard/claude-skills --skill categorizing-bsky-accounts

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。

categorizing-bsky-accounts 从 Bluesky 账户资料与动态中提取关键词,并由 Claude 完成主题分类,形成结构化标签体系。适用于舆情分析、社群洞察或内容运营场景,帮助识别不同领域的活跃用户与话题焦点。

依赖 extracting-keywords 技能提供的 YAKE 关键词引擎与领域停用词库,需提前安装并配置环境。输入为 Bluesky 用户名或帖子链接,输出为压缩后的关键词向量与分类结果,便于后续批量处理。注意隐私边界,仅处理公开数据,不得用于追踪个人或抓取敏感信息,遵守平台速率限制与使用条款。

来源仓库内路径:skills/categorizing-bsky-accounts

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/oaustegard/claude-skills --skill categorizing-…;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

categorizing-bsky-accounts 怎么安装?

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

categorizing-bsky-accounts 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。

categorizing-bsky-accounts 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。

categorizing-bsky-accounts 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。

categorizing-bsky-accounts 用的人多吗?

公开数据是累计安装 734 次,近一周安装 30 次,下载 238 次,GitHub 118 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。

categorizing-bsky-accounts 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
categorizing-bsky-accounts

Bsky 帐户分类 · 本页

734118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

Categorizing Bluesky Accounts

Fetch Bluesky account data and extract keywords for Claude to categorize by topic. The script compresses account context (bio + posts) into bio + keywords, then Claude performs intelligent categorization.

Prerequisites

Requires: extracting-keywords skill (provides YAKE venv + domain stopwords)

The analyzer delegates keyword extraction to the extracting-keywords skill, which provides:

  • Optimized YAKE installation with minimal dependencies
  • Domain-specific stopwords: English (574), AI/ML (1357), Life Sciences (1293)
  • Support for 34 languages

Core Workflow

When users request Bluesky account analysis:

  1. Ensure keyword extraction is set up - Invoke the extracting-keywords skill using the Skill tool to ensure YAKE venv exists (skip if already invoked in this session)
  2. Determine input mode based on user's request:

- Following list → use --following handle - Followers → use --followers handle - List of handles → use --handles "h1,h2,h3" - File provided → use --file accounts.txt

  1. Configure parameters:

- --accounts N - Number to analyze (default: 100, max: 100) - --posts N - Posts per account (default: 20, max: 100) - --stopwords [en|ai|ls] - Choose domain-specific stopwords: - en: English (general purpose) - ai: AI/ML domain (recommended for tech accounts) - ls: Life Sciences (for biomedical/research accounts) - --exclude "pattern1,pattern2" - Skip spam/bot accounts

  1. Run script - Outputs simple text format to stdout: @handle1.bsky.social (Display Name) Bio text here Keywords: keyword1, keyword2, keyword3 @handle2.bsky.social (Another Name) Bio text here Keywords: keyword4, keyword5, keyword6
  2. Categorize accounts - Claude analyzes bio + keywords to categorize by topic

Quick Start

Analyze following list with AI/ML stopwords:

python scripts/bluesky_analyzer.py --following austegard.com --stopwords ai

Analyze followers:

python scripts/bluesky_analyzer.py --followers austegard.com

Analyze specific handles:

python scripts/bluesky_analyzer.py --handles "user1.bsky.social,user2.bsky.social,user3.bsky.social"

From file:

python scripts/bluesky_analyzer.py --file accounts.txt --stopwords ai

Filter out bot accounts:

python scripts/bluesky_analyzer.py --following handle --exclude "bot,spam,promo" --stopwords ai

Parameters

Input Modes (choose one)

--handles "h1,h2,h3" Comma-separated list of Bluesky handles

--following HANDLE Analyze accounts followed by HANDLE

--followers HANDLE Analyze accounts following HANDLE

--file PATH Read handles from file (one per line)

Analysis Options

--accounts N Number of accounts to analyze (1-100, default: 100)

--posts N Posts to fetch per account (1-100, default: 20)

--stopwords [en|ai|ls] Stopwords to use for keyword extraction (default: en)

  • en: English stopwords (574 terms) - general purpose
  • ai: AI/ML domain stopwords (1357 terms) - tech-focused accounts
  • ls: Life Sciences stopwords (1293 terms) - biomedical/research accounts

--exclude "word1,word2" Skip accounts with these keywords in bio/posts

Output Format

The script outputs simple text format for Claude to process:

@alice.bsky.social (Alice Smith)
AI researcher working on LLM alignment and safety
Keywords: alignment, safety research, interpretability, llm evaluation

@bob.bsky.social (Bob Johnson)
Full-stack developer building web applications
Keywords: react, typescript, node.js, api design, postgresql

@carol.bsky.social (Carol Williams)
Biotech researcher studying CRISPR applications
Keywords: crispr, gene editing, therapeutics, clinical trials

Claude then categorizes accounts based on bio + keywords without hardcoded rules.

Common Workflows

Audit Your Following List

python scripts/bluesky_analyzer.py --following your-handle.bsky.social --stopwords ai

Claude will categorize accounts by topic and identify patterns in who you follow.

Find Experts in a Topic

python scripts/bluesky_analyzer.py --following alice.bsky.social --stopwords ai

Ask Claude: "Which of these accounts are ML researchers?" or "Who focuses on climate tech?"

Analyze a Curated List

cat > accounts.txt << 'EOF'
expert1.bsky.social
expert2.bsky.social
expert3.bsky.social
EOF

python scripts/bluesky_analyzer.py --file accounts.txt --stopwords ls

Filter Out Bot Accounts

python scripts/bluesky_analyzer.py --following handle --exclude "bot,spam,promo,follow back" --stopwords ai

Technical Details

Keyword Extraction

Delegates to extracting-keywords skill using YAKE venv:

  • Stopwords options (--stopwords):

- en: English (574 terms) - general purpose - ai: AI/ML domain (1357 terms) - filters technical noise, ML boilerplate - ls: Life Sciences (1293 terms) - filters research methodology, clinical terms

  • N-grams: 1-3 words
  • Deduplication: 0.9 threshold
  • Top keywords: 10 per account
  • Performance: ~5% overhead with domain stopwords vs English

API Rate Limits

Bluesky API limits:

  • 3000 requests per 5 minutes
  • 5000 requests per hour

The analyzer respects these limits with built-in delays.

Categorization Algorithm

Script's role:

  1. Fetch account data (bio + posts)
  2. Extract keywords to compress context
  3. Output bio + keywords in simple format

Claude's role:

  1. Read bio + keywords for each account
  2. Intelligently categorize by topic (no hardcoded rules)
  3. Group accounts, identify patterns, answer user questions

This agentic pattern is more flexible than hardcoded keyword matching.

Troubleshooting

"No accounts to analyze"

  • Verify handle format (include domain: handle.bsky.social)
  • Check if account exists and has public following/followers

"Insufficient content for keyword extraction"

  • Account has few posts (<5)
  • Posts are very short
  • Try increasing --posts parameter

Rate limit errors

  • Reduce --accounts parameter
  • Add delays between batches
  • Check Bluesky API status

Import errors

  • Verify extracting-keywords skill is available
  • Check YAKE venv exists: /home/claude/yake-venv/bin/python -c "import yake"
  • Verify Python 3.8+: python3 --version

Integration with Other Skills

Built-in integration:

  • extracting-keywords: Automatically delegates keyword extraction to this skill's optimized YAKE venv with domain-specific stopwords

Example Sessions

User: "Can you analyze the accounts I follow on Bluesky and tell me what topics they focus on?"

Claude:

python scripts/bluesky_analyzer.py --following user-handle.bsky.social --stopwords ai

Based on the output, I can see you follow:

  • AI/ML researchers (15 accounts): Focus on LLM safety, alignment, interpretability
  • Software engineers (20 accounts): Web development, React, TypeScript, DevOps
  • Writers (8 accounts): Tech journalism, newsletters, long-form content
  • Scientists (7 accounts): Climate science, biotech, physics

User: "Find ML researchers in @alice's network"

Claude:

python scripts/bluesky_analyzer.py --following alice.bsky.social --stopwords ai

I found 23 ML researchers in Alice's network:

  • 8 working on LLM alignment and safety
  • 6 focused on model evaluation and benchmarks
  • 5 in ML infrastructure and MLOps
  • 4 in computer vision and multimodal models

User: "Here's a list of 30 accounts, categorize them"

Claude:

python scripts/bluesky_analyzer.py --file accounts.txt --stopwords ai

Categorized into:

  • Climate Tech (8 accounts)
  • Biotech (6 accounts)
  • Fintech (5 accounts)
  • AI/ML (7 accounts)
  • Other (4 accounts)

平台分布

Codex

36.67%
按下载量换算87

Claude

30.21%
按下载量换算72

Cursor

19.38%
按下载量换算46

Gemini CLI

10.92%
按下载量换算26

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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