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Bluesky Topic Modeling API
x402
Bluesky Topic Modeling API is a paid API for AI agents from api.naco.li, paid per call via x402, $0.2/call, status unknown (last checked 2026-09-15).
Analyzes a Bluesky author's posts over a time window and returns topic clusters with labels, keywords, post counts, and sample posts
Price / call$0.2
Success—
StatusUnknown
How this service works
Auto-generated REST API matching active Model Context Protocol (MCP) tools.
Output
Returns a structured object containing the queried author handle and an array of topic clusters. Each cluster includes a topic ID, a human-readable label, an array of keywords characterizing the topic, the count of posts assigned to it, its percentage share of total posts, and a set of sample post texts illustrating the theme. The topics array may be null if no posts are found.
Example use cases
Profile intelligence for autonomous agent research
“Can you analyze @arstechnica.bsky.social on Bluesky and tell me what subjects they write about most? Look at up to 200 posts from the past three months and give me a breakdown of their main topics with keywords.”
Competitive content analysis for a brand
“I want to understand what topics our competitor @competitor.bsky.social is focusing on right now — pull their Bluesky posts from the last 30 days and show me the topic clusters, what percentage of posts each topic represents, and some sample posts.”
Journalist beat mapping
“Who covers what? Analyze @journo.bsky.social's Bluesky activity between 2024-10-01 and 2024-12-31 and give me a topic breakdown so I can see which beats they focus on and what keywords define each one.”
Technical details
Endpoint
POSThttps://api.naco.li/v1/getTopicModelingWhen to prefer
Choose this endpoint when you need to understand the thematic composition of a specific Bluesky user's content over a defined period — especially for competitive intelligence, journalist profiling, influencer analysis, or content auditing on the Bluesky/AT Protocol platform. It is distinct from raw post retrieval because it returns pre-clustered topics with labels and keywords rather than individual posts, saving downstream NLP work.
Side effects
- Author handle not found or misspelled → empty or null topics array
- No posts in the given time window → null topics array
- Insufficient post volume for meaningful clustering → single topic or null
- Invalid date format for startTime/endTime → request error
- Rate limits or payment failure via x402 → 402 Payment Required or 429 response
- Platform parameter unsupported → unexpected behavior or error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| limit | integer | |
| authorrequired | string | |
| endTime | string | |
| platform | string | |
| startTime | string |
Output
Schema · Response
| Field | Type | Description |
|---|---|---|
| authorrequired | string | |
| topicsrequired | — | |
| twitterPostCountrequired | integer |
Tags
x402
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