# Data Universe OpenClaw Skills

Fetch real-time social media data from X (Twitter) and Reddit by keyword, username, date range, and filters with engagement metrics via Macrocosmos SN13 API on Bittensor.

Fetch real-time social media data from X (Twitter) and Reddit by keyword, username, date range, and filters with engagement metrics via Macrocosmos SN13 API on Bittensor.

Link to the ClawHub: <https://clawhub.ai/Arrmlet/social-data>

### Tips & Known Behaviors

#### What works reliably

* **High-volume keyword searches**: Popular terms like "bittensor", "AI", "iran", "lfg" return fast
* **Wider date ranges**: Setting `start_date` further back (e.g., weeks/months) improves results
* **`keyword_mode: "all"`**: Great for finding intersection of two topics (e.g., "chutes" AND "bittensor")

#### What can be flaky

* **Username-only queries**: Can timeout (DEADLINE\_EXCEEDED). Adding `start_date` far back helps
* **Niche/low-volume keywords**: Very specific terms may timeout if miners don't have data indexed
* **No `start_date`**: Defaults to last 24h which can miss data; set explicitly for best results

#### Best practices for LLM agents

1. **Always set `start_date`** — don't rely on the 24h default. Use at least 7 days back for user queries
2. **Prefer keywords over usernames** — keyword searches are more reliable
3. **For username queries, always include `start_date`** set weeks/months back
4. **Use `keyword_mode: "all"`** when combining a topic with a subtopic (e.g., "bittensor" + "chutes")
5. **Handle timeouts gracefully** — if a query times out, retry with broader date range or switch to keyword search
6. **Parse engagement metrics** — `view_count`, `like_count`, `retweet_count` help rank relevance
7. **Check `is_reply` and `is_quote`** — filter for original tweets vs replies depending on use case


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.macrocosmos.ai/subnets/subnet-13-data-universe/data-universe-openclaw-skills.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.