browsing-bluesky

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Browsing Bluesky

浏览Bluesky内容

Access Bluesky content through public APIs and real-time firehose. Supports optional authentication for personalized feeds. Includes account analysis for categorization.

通过公开API和实时firehose访问Bluesky内容。支持可选身份验证以获取个性化订阅源，还包含用于账户分类的账户分析功能。

Implementation

实现方式

Add skill directory to path and import:

python

import sys
sys.path.insert(0, '/path/to/skills/browsing-bluesky')  # or use .claude/skills symlink path
from browsing_bluesky import (
    # Core browsing
    search_posts, get_user_posts, get_profile, get_feed_posts, sample_firehose,
    get_thread, get_quotes, get_likes, get_reposts,
    get_followers, get_following, search_users,
    # Trending
    get_trending, get_trending_topics,
    # Account analysis
    get_all_following, get_all_followers, extract_post_text,
    extract_keywords, analyze_account, analyze_accounts,
    # Authentication utilities
    is_authenticated, get_authenticated_user, clear_session
)

将技能目录添加到路径并导入：

python

import sys
sys.path.insert(0, '/path/to/skills/browsing-bluesky')  # or use .claude/skills symlink path
from browsing_bluesky import (
    # Core browsing
    search_posts, get_user_posts, get_profile, get_feed_posts, sample_firehose,
    get_thread, get_quotes, get_likes, get_reposts,
    get_followers, get_following, search_users,
    # Trending
    get_trending, get_trending_topics,
    # Account analysis
    get_all_following, get_all_followers, extract_post_text,
    extract_keywords, analyze_account, analyze_accounts,
    # Authentication utilities
    is_authenticated, get_authenticated_user, clear_session
)

Authentication (Optional)

身份验证（可选）

Authentication enables personalized feeds (like Paper Skygest) that require knowing who's asking.

身份验证可启用需要识别请求者身份的个性化订阅源（如Paper Skygest）。

Setup

设置步骤

Create an app password at Bluesky: Settings → Privacy and Security → App Passwords

Set environment variables:

bash

export BSKY_HANDLE="yourhandle.bsky.social"
export BSKY_APP_PASSWORD="xxxx-xxxx-xxxx-xxxx"

在Bluesky创建应用密码：设置 → 隐私与安全 → 应用密码

设置环境变量：

bash

export BSKY_HANDLE="yourhandle.bsky.social"
export BSKY_APP_PASSWORD="xxxx-xxxx-xxxx-xxxx"

Behavior

行为特性

Transparent: All functions work identically with or without credentials
Automatic: Auth headers are added opportunistically when credentials exist
Graceful: Failed auth silently falls back to public access
Secure: Tokens cached in memory only, never logged or persisted

透明化：所有函数无论有无凭据都能以相同方式工作
自动化：当存在凭据时会自动添加身份验证头
优雅降级：身份验证失败时会静默回退到公开访问模式
安全性：令牌仅在内存中缓存，绝不会被记录或持久化存储

Check Auth Status

检查身份验证状态

python

if is_authenticated():
    print(f"Logged in as: {get_authenticated_user()}")
else:
    print("Using public access")

python

if is_authenticated():
    print(f"Logged in as: {get_authenticated_user()}")
else:
    print("Using public access")

Clear session if needed (e.g., switching accounts)

clear_session()

undefined

clear_session()

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Research Workflows

研究工作流

Investigate a Topic

研究特定话题

Use

search_posts()

with query syntax matching bsky.app advanced search:

Basic terms:
```
event sourcing
```
Exact phrases:
```
"event sourcing"
```
User filter:
```
from:acairns.co.uk
```
or use
```
author=
```
param
Date filter:
```
since:2025-01-01
```
or use
```
since=
```
param
Hashtags, mentions, domain links:
```
#python mentions:user domain:github.com
```

Combine query syntax with function params for complex searches.

使用

search_posts()

函数，支持与bsky.app高级搜索匹配的查询语法：

基础关键词：
```
event sourcing
```
精确短语：
```
"event sourcing"
```
用户筛选：
```
from:acairns.co.uk
```
或使用
```
author=
```
参数
日期筛选：
```
since:2025-01-01
```
或使用
```
since=
```
参数
话题标签、提及、域名链接：
```
#python mentions:user domain:github.com
```

可结合查询语法与函数参数进行复杂搜索。

Monitor a User

监控用户

Fetch profile with
```
get_profile(handle)
```
for context (bio, follower count, post count)
Get recent posts with
```
get_user_posts(handle, limit=N)
```
For topic-specific user content, use
```
search_posts(query, author=handle)
```

使用
```
get_profile(handle)
```
获取用户资料上下文（简介、粉丝数、帖子数）
使用
```
get_user_posts(handle, limit=N)
```
获取用户近期帖子
若要获取用户特定话题的内容，使用
```
search_posts(query, author=handle)
```

Discover What's Trending

发现热门内容

Recommended workflow — trending API first, firehose for deep dives:

推荐工作流：先使用热门话题API快速扫描，再用firehose进行深度探索：

1. Quick scan with trending topics (~500 tokens)

1. 快速扫描热门话题（约500个token）

python

topics = get_trending_topics(limit=10)

python

topics = get_trending_topics(limit=10)

Returns: {topics: [{topic, display_name, description, link}, ...],

返回结果：{topics: [{topic, display_name, description, link}, ...],

suggested: [...]}

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2. Rich trends with post counts and actors

2. 获取包含帖子数和参与用户的详细热门趋势

python

trends = get_trending(limit=10)
for t in trends:
    print(f"{t['display_name']} — {t['post_count']} posts ({t['status']})")

python

trends = get_trending(limit=10)
for t in trends:
    print(f"{t['display_name']} — {t['post_count']} posts ({t['status']})")

Each trend includes: topic, display_name, link, started_at,

每个趋势包含：topic、display_name、link、started_at、

post_count, status, category, actors

post_count、status、category、actors

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3. Targeted exploration of selected trends

3. 针对性探索选定的热门话题

python

posts = search_posts(trend["topic"], limit=25)

python

posts = search_posts(trend["topic"], limit=25)

4. Optional: Firehose for velocity monitoring or long-tail discovery

4. 可选：使用Firehose进行热度监控或长尾内容发现

Prerequisites: Install Node.js dependencies once per session:

bash

cd /home/claude && npm install ws https-proxy-agent 2>/dev/null

python

data = sample_firehose(duration=30)  # Full firehose sample
data = sample_firehose(duration=20, filter="python")  # Filtered sample

Returns:

topWords

topPhrases

entities

samplePosts

stats

前置条件：每个会话需安装一次Node.js依赖：

bash

cd /home/claude && npm install ws https-proxy-agent 2>/dev/null

python

data = sample_firehose(duration=30)  # 完整数据流采样
data = sample_firehose(duration=20, filter="python")  # 筛选后的采样

返回结果包含：

topWords

、

topPhrases

、

entities

、

samplePosts

、

stats

Read Feeds and Lists

读取订阅源和列表

get_feed_posts()

accepts:

List URLs:

https://bsky.app/profile/austegard.com/lists/3lankcdrlip2f

Feed URLs:

https://bsky.app/profile/did:plc:xxx/feed/feedname

AT-URIs:

at://did:plc:xxx/app.bsky.graph.list/xyz

The function extracts the AT-URI from URLs automatically.

get_feed_posts()

支持传入：

列表URL：

https://bsky.app/profile/austegard.com/lists/3lankcdrlip2f

订阅源URL：

https://bsky.app/profile/did:plc:xxx/feed/feedname

AT-URI：

at://did:plc:xxx/app.bsky.graph.list/xyz

该函数会自动从URL中提取AT-URI。

Explore a Thread

探索帖子线程

Fetch full thread context for a post with parents and replies:

python

thread = get_thread("https://bsky.app/profile/user/post/xyz", depth=10)

获取某篇帖子的完整线程上下文，包括父帖和回复：

python

thread = get_thread("https://bsky.app/profile/user/post/xyz", depth=10)

Returns: {post: {...}, parent: {...}, replies: [...]}

返回结果：{post: {...}, parent: {...}, replies: [...]}

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Find Quote Posts

查找引用帖子

Discover posts that quote a specific post:

python

quotes = get_quotes("https://bsky.app/profile/user/post/xyz")
for q in quotes:
    print(f"@{q['author_handle']}: {q['text'][:80]}")

发现引用特定帖子的内容：

python

quotes = get_quotes("https://bsky.app/profile/user/post/xyz")
for q in quotes:
    print(f"@{q['author_handle']}: {q['text'][:80]}")

Analyze Engagement

分析互动数据

Get users who engaged with a post:

python

likes = get_likes(post_url)
reposts = get_reposts(post_url)

获取与某篇帖子互动的用户：

python

likes = get_likes(post_url)
reposts = get_reposts(post_url)

Accepts both URLs and AT-URIs

同时支持URL和AT-URI

likes = get_likes("at://did:plc:.../app.bsky.feed.post/...")

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likes = get_likes("at://did:plc:.../app.bsky.feed.post/...")

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Explore Social Graph

探索社交关系图

Navigate follower/following relationships:

python

followers = get_followers("handle.bsky.social")
following = get_following("handle.bsky.social")

浏览粉丝/关注关系：

python

followers = get_followers("handle.bsky.social")
following = get_following("handle.bsky.social")

Returns list of actor dicts with handle, display_name, did, description, etc.

返回包含handle、display_name、did、description等字段的用户字典列表

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Find Users

查找用户

Search for users by name, handle, or bio:

python

users = search_users("machine learning researcher")
for u in users:
    print(f"{u['display_name']} (@{u['handle']}): {u['description'][:100]}")

按名称、handle或简介搜索用户：

python

users = search_users("machine learning researcher")
for u in users:
    print(f"{u['display_name']} (@{u['handle']}): {u['description'][:100]}")

API Endpoint Notes

API端点说明

Public AppView:
```
https://api.bsky.app/xrpc/
```
for unauthenticated reads
PDS:
```
https://bsky.social/xrpc/
```
for authenticated requests

Trending:

app.bsky.unspecced.getTrends

(rich) and

app.bsky.unspecced.getTrendingTopics

(lightweight)

Firehose:

wss://jetstream1.us-east.bsky.network/subscribe

Endpoint routing is automatic - authenticated requests go to PDS, public requests go to AppView
Rate limits exist but are generous for read operations

公开AppView：
```
https://api.bsky.app/xrpc/
```
用于未验证的读取请求
PDS：
```
https://bsky.social/xrpc/
```
用于已验证的请求

热门话题：

app.bsky.unspecced.getTrends

（详细版）和

app.bsky.unspecced.getTrendingTopics

（轻量版）

Firehose：

wss://jetstream1.us-east.bsky.network/subscribe

端点路由自动处理：已验证请求会发送到PDS，公开请求发送到AppView
速率限制：存在速率限制，但读取操作的限制较为宽松

Return Format

返回格式

All API functions return structured dicts with:

```
uri
```
: AT protocol identifier
```
text
```
: Post content
```
created_at
```
: ISO timestamp
```
author_handle
```
: User handle
```
author_name
```
: Display name
```
likes
```
,
```
reposts
```
,
```
replies
```
: Engagement counts
```
links
```
: Full URLs extracted from post facets (post text truncates URLs with "...")
```
image_alts
```
: Alt text from embedded images
```
url
```
: Direct link to post on bsky.app

Profile function returns:

handle

display_name

description

followers

following

posts

did

所有API函数均返回结构化字典，包含：

```
uri
```
：AT协议标识符
```
text
```
：帖子内容
```
created_at
```
：ISO时间戳
```
author_handle
```
：用户handle
```
author_name
```
：显示名称
```
likes
```
、
```
reposts
```
、
```
replies
```
：互动计数
```
links
```
：从帖子 facets 中提取的完整URL（帖子文本中URL会被截断为“...”）
```
image_alts
```
：嵌入图片的替代文本
```
url
```
：bsky.app上的帖子直接链接

资料函数返回字段：

handle

、

display_name

、

description

、

followers

、

following

、

posts

、

did

Account Analysis

账户分析

Analyze accounts for categorization by topic. Fetches profile and posts, extracts keywords, and returns structured data for Claude to categorize.

通过分析账户进行话题分类。获取用户资料和帖子，提取关键词，返回结构化数据供Claude进行分类。

Analyze a User's Network

分析用户的社交网络

python

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python

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Analyze accounts you follow

分析你关注的账户

results = analyze_accounts(following="yourhandle.bsky.social", limit=50)

Analyze your followers

分析你的粉丝

results = analyze_accounts(followers="yourhandle.bsky.social", limit=50)

Analyze specific handles

分析特定handle的账户

results = analyze_accounts(handles=["user1.bsky.social", "user2.bsky.social"])

undefined

results = analyze_accounts(handles=["user1.bsky.social", "user2.bsky.social"])

undefined

Single Account Analysis

单个账户分析

python

analysis = analyze_account("user.bsky.social")

python

analysis = analyze_account("user.bsky.social")

Returns: {handle, display_name, description, keywords, post_count, followers, following}

返回结果：{handle, display_name, description, keywords, post_count, followers, following}

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Keyword Extraction Options

关键词提取选项

Stopwords parameter filters domain-specific noise:

```
"en"
```
: English (general purpose, default)
```
"ai"
```
: AI/ML domain (filters tech boilerplate)
```
"ls"
```
: Life Sciences (filters research methodology)

python

results = analyze_accounts(following="handle", stopwords="ai")

Requires:

extracting-keywords

skill with YAKE venv for keyword extraction.

停用词参数可过滤特定领域的干扰词：

```
"en"
```
：英文（通用，默认值）
```
"ai"
```
：AI/ML领域（过滤技术套话）
```
"ls"
```
：生命科学领域（过滤研究方法相关内容）

python

results = analyze_accounts(following="handle", stopwords="ai")

依赖要求：需要

extracting-keywords

技能及YAKE虚拟环境以实现关键词提取。

Filtering Accounts

过滤账户

python

results = analyze_accounts(
    following="handle",
    exclude_patterns=["bot", "spam", "promo"]  # Skip accounts matching these
)

python

results = analyze_accounts(
    following="handle",
    exclude_patterns=["bot", "spam", "promo"]  # 跳过匹配这些关键词的账户
)

Paginated Following/Followers

分页获取关注/粉丝列表

For large account lists beyond the 100 limit of

get_following

get_followers

python

all_following = get_all_following("handle", limit=500)  # Handles pagination
all_followers = get_all_followers("handle", limit=500)

对于超过

get_following

get_followers

100条限制的大型账户列表：

python

all_following = get_all_following("handle", limit=500)  # 处理分页
all_followers = get_all_followers("handle", limit=500)

Account Analysis Output

账户分析输出

Each analyzed account returns:

python

{
    "handle": "user.bsky.social",
    "display_name": "User Name",
    "description": "Bio text here",
    "keywords": ["keyword1", "keyword2", "keyword3"],
    "post_count": 20,
    "followers": 1234,
    "following": 567
}

Claude uses bio + keywords to categorize accounts by topic without hardcoded rules

每个被分析的账户返回结果如下：

python

{
    "handle": "user.bsky.social",
    "display_name": "User Name",
    "description": "Bio text here",
    "keywords": ["keyword1", "keyword2", "keyword3"],
    "post_count": 20,
    "followers": 1234,
    "following": 567
}

Claude会结合简介和关键词对账户进行话题分类，无需硬编码规则。