browsing-bluesky

name	browsing-bluesky
description	Browse Bluesky content via API and firehose - search posts, fetch user activity, sample trending topics, read feeds and lists, analyze and categorize accounts. Supports authenticated access for personalized feeds. Use for Bluesky research, user monitoring, trend analysis, feed reading, firehose sampling, account categorization.
metadata	{"version":"0.5.1"}

Browsing Bluesky

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

Implementation

Add skill directory to path and import:

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)

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

Setup

1. Create an app password at Bluesky: Settings → Privacy and Security → App Passwords
2. Set environment variables:

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

Behavior

• 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

Check Auth Status

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()

Research Workflows

Investigate a Topic

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.

Monitor a User

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

Discover What's Trending

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

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

topics = get_trending_topics(limit=10)
# Returns: {topics: [{topic, display_name, description, link}, ...],
#           suggested: [...]}

2. Rich trends with post counts and actors

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,
#   post_count, status, category, actors

3. Targeted exploration of selected trends

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

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

Prerequisites: Install Node.js dependencies once per session:

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

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

Returns dict with keys:

• window: {startTime, endTime, durationSeconds} — sampling time range
• stats: {totalReceived, totalPosts, postsPerSecond, filter, languages} — volume metrics and language breakdown
• topWords: [[word, count], ...] — top 50 words (count >= 3)
• topPhrases: [[bigram, count], ...] — top 30 bigrams (count >= 2)
• topTrigrams: [[trigram, count], ...] — top 20 trigrams (count >= 2)
• entities: [[entity, count], ...] — top 25 handles/hashtags (count >= 2)
• samplePosts: [{text, altTexts, hasImages}, ...] — first 50 matching posts

Read Feeds and Lists

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.

Explore a Thread

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

thread = get_thread("https://bsky.app/profile/user/post/xyz", depth=10)
# Returns: {post: {...}, parent: {...}, replies: [...]}

Find Quote Posts

Discover posts that quote a specific post:

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

Analyze Engagement

Get users who engaged with a post:

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

# Accepts both URLs and AT-URIs
likes = get_likes("at://did:plc:.../app.bsky.feed.post/...")

Read Embed Images

Every parsed post carries an images field — a list of {alt, url, transcription} dicts, one per embed image. The legacy image_alts: list[str] field is preserved (non-empty alts only).

When alt text is missing and the image content matters, opt in to model transcription via the transcribe parameter on any post-fetch function (get_user_posts, search_posts, get_feed_posts, get_thread, get_quotes):

# Routine/bulk work (zeitgeist, inbox review, news scans) —
# gemini-2.5-flash-lite is the recommended default. Cheapest production
# model anywhere ($0.10/$0.40 per 1M tokens), ~95% accuracy on dense
# screenshots in May 2026 benchmarks:
posts = get_user_posts("ayourtch.bsky.social", limit=40, transcribe="gemini-lite")

# Token-perfect transcription, still cheap:
posts = get_user_posts(..., transcribe="gemini-flash")

# Frontier model with thinking_level=minimal — for cases where the image
# content needs reasoning, not just transcription:
posts = get_user_posts(..., transcribe="gemini-3.5-flash")

# Anthropic single-vendor option (note: empirically weaker prompt-following
# than Gemini on dense transcription — Haiku tends to summarize rather
# than transcribe):
posts = get_user_posts(..., transcribe="haiku")

# Interactive sessions where image is part of the active task and you want
# conversation context to inform interpretation (only available on Anthropic):
thread = get_thread(post_url, transcribe="opus")

# Default (no transcription) — current behavior preserved:
posts = get_user_posts("ayourtch.bsky.social", limit=40)

Policy is invariant across all callers: images with non-empty alt text are never transcribed (the author already described the image; trust it). Only images with missing or empty alt are sent to the model. Network or API failures leave transcription as None; callers degrade silently.

Cost/quality empirics (May 2026, n=3 dense terminal screenshots, single run each — sample size is small, treat as directional):

Alias	Latency	$/image	Chord-token recall
gemini-lite	~8s	~$0.001	95%
gemini-flash	~10s	~$0.003	100%
gemini-3.5-flash	~10s	~$0.014	100%
haiku	~7s	~$0.008	18% (summarizes)
opus	~20s	~$0.12	91%

Requires either ANTHROPIC_API_KEY (or API_KEY in /mnt/project/claude.env) for the haiku / opus aliases, or CF AI Gateway credentials in /mnt/project/proxy.env for the gemini-* aliases. Transcription only fires when the parameter is set, so callers without the relevant credentials can simply pick a different alias or leave the feature off.

Explore Social Graph

Navigate follower/following relationships:

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

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

Find Users

Search for users by name, handle, or bio:

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

API Endpoint Notes

• 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

Return Format

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

Account Analysis

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

Analyze a User's Network

# Analyze accounts you follow
results = analyze_accounts(following="yourhandle.bsky.social", limit=50)

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

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

Single Account Analysis

analysis = analyze_account("user.bsky.social")
# Returns: {handle, display_name, description, keywords, post_count, followers, following}

Keyword Extraction Options

Stopwords parameter filters domain-specific noise:

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

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

Requires: extracting-keywords skill with YAKE venv for keyword extraction.

Filtering Accounts

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

Paginated Following/Followers

For large account lists beyond the 100 limit of get_following/get_followers:

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

Account Analysis Output

Each analyzed account returns:

{
    "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