Skills social-data

Name: social-data
Author: openclaw

Macrocosmos SN13 API - Social Media Data Skill

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/arrmlet/social-data" ~/.claude/skills/openclaw-skills-social-data && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/arrmlet/social-data" ~/.openclaw/skills/openclaw-skills-social-data && rm -rf "$T"

manifest: skills/arrmlet/social-data/SKILL.md

Macrocosmos SN13 API - Social Media Data Skill

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.

Metadata

name: macrocosmos-social-data
version: 1.0.1
homepage: https://github.com/macrocosm-os/macrocosmos-mcp
source: https://github.com/macrocosm-os/macrocosmos-mcp
pypi: https://pypi.org/project/macrocosmos-mcp
subnet: Bittensor SN13 (Data Universe)
author: Macrocosmos AI
license: MIT

Required Environment Variables

Variable	Required	Type	Description
`MC_API`	Yes	`secret`	Macrocosmos API key. Required for all API requests. Get your free key at https://app.macrocosmos.ai/account?tab=api-keys

Setup: The

MC_API

key must be set as an environment variable. It is passed as a Bearer token in the

Authorization

header for REST calls, or provided directly to the Python SDK client.

API Endpoint

POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData

Headers

Content-Type: application/json
Authorization: Bearer <YOUR_MC_API_KEY>

Request Format

{
  "source": "X",
  "usernames": ["@elonmusk"],
  "keywords": ["AI", "bittensor"],
  "start_date": "2026-01-01",
  "end_date": "2026-02-10",
  "limit": 10,
  "keyword_mode": "any"
}

Parameters

Parameter	Type	Required	Description
`source`	string	Yes	`"X"` or `"REDDIT"` (case-sensitive)
`usernames`	array	No	Up to 5 usernames. `@` optional. X only (not available for Reddit)
`keywords`	array	No	Up to 5 keywords/hashtags. For Reddit: use subreddit format `"r/subreddit"`
`start_date`	string	No	`YYYY-MM-DD` or ISO format. Defaults to 24h ago
`end_date`	string	No	`YYYY-MM-DD` or ISO format. Defaults to now
`limit`	int	No	1-1000 results. Default: 10
`keyword_mode`	string	No	`"any"` (default) matches ANY keyword, `"all"` requires ALL keywords

Response Format

{
  "data": [
    {
      "datetime": "2026-02-10T17:30:58Z",
      "source": "x",
      "text": "Tweet content here",
      "uri": "https://x.com/username/status/123456",
      "user": {
        "username": "example_user",
        "display_name": "Example User",
        "followers_count": 1500,
        "following_count": 300,
        "user_description": "Bio text",
        "user_blue_verified": true,
        "profile_image_url": "https://pbs.twimg.com/..."
      },
      "tweet": {
        "id": "123456",
        "like_count": 42,
        "retweet_count": 10,
        "reply_count": 5,
        "quote_count": 2,
        "view_count": 5000,
        "bookmark_count": 3,
        "hashtags": ["#AI", "#bittensor"],
        "language": "en",
        "is_reply": false,
        "is_quote": false,
        "conversation_id": "123456"
      }
    }
  ]
}

curl Examples

1. Keyword Search on X

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "X",
    "keywords": ["bittensor"],
    "start_date": "2026-01-01",
    "limit": 10
  }'

2. Fetch Tweets from a Specific User

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "X",
    "usernames": ["@MacrocosmosAI"],
    "start_date": "2026-01-01",
    "limit": 10
  }'

3. Multi-Keyword AND Search

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "X",
    "keywords": ["chutes", "bittensor"],
    "keyword_mode": "all",
    "start_date": "2026-01-01",
    "limit": 20
  }'

4. Reddit Search

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "REDDIT",
    "keywords": ["r/MachineLearning", "transformers"],
    "start_date": "2026-02-01",
    "limit": 50
  }'

5. User + Keyword Filter

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "X",
    "usernames": ["@opentensor"],
    "keywords": ["subnet"],
    "start_date": "2026-01-01",
    "limit": 20
  }'

Python Examples

Using the

macrocosmos

SDK

import asyncio
import macrocosmos as mc

async def search_tweets():
    client = mc.AsyncSn13Client(api_key="YOUR_API_KEY")

    response = await client.sn13.OnDemandData(
        source="X",
        keywords=["bittensor"],
        usernames=[],
        start_date="2026-01-01",
        end_date=None,
        limit=10,
        keyword_mode="any",
    )

    if hasattr(response, "model_dump"):
        data = response.model_dump()

    for tweet in data["data"]:
        print(f"@{tweet['user']['username']}: {tweet['text'][:100]}")
        print(f"  Likes: {tweet['tweet']['like_count']} | Views: {tweet['tweet']['view_count']}")

asyncio.run(search_tweets())

Using

requests

(REST)

import requests

url = "https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_API_KEY"
}
payload = {
    "source": "X",
    "keywords": ["bittensor"],
    "start_date": "2026-01-01",
    "limit": 10
}

response = requests.post(url, json=payload, headers=headers)
data = response.json()

for tweet in data["data"]:
    print(f"@{tweet['user']['username']}: {tweet['text'][:100]}")

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

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

Gravity API (Large-Scale Collection)

For datasets larger than 1000 results, use the Gravity endpoints:

Create Task

POST /gravity.v1.GravityService/CreateGravityTask

{
  "gravity_tasks": [
    {"platform": "x", "topic": "#bittensor", "keyword": "dTAO"}
  ],
  "name": "Bittensor dTAO Collection"
}

Note: X topics MUST start with

. Reddit topics use subreddit format.

Check Status

POST /gravity.v1.GravityService/GetGravityTasks

{
  "gravity_task_id": "multicrawler-xxxx-xxxx",
  "include_crawlers": true
}

Build Dataset

POST /gravity.v1.GravityService/BuildDataset

{
  "crawler_id": "crawler-0-multicrawler-xxxx",
  "max_rows": 10000
}

Warning: Building stops the crawler permanently.

Get Dataset Download

POST /gravity.v1.GravityService/GetDataset

{
  "dataset_id": "dataset-xxxx-xxxx"
}

Returns Parquet file download URLs when complete.

Workflow Summary

Quick Query (< 1000 results):
  OnDemandData → instant results

Large Collection (7-day crawl):
  CreateGravityTask → GetGravityTasks (monitor) → BuildDataset → GetDataset (download)

Error Reference

Error	Cause	Fix
`401 Unauthorized`	Missing or invalid API key	Check `Authorization: Bearer` header
`500 Internal Server Error`	Server-side issue (often auth via gRPC)	Verify API key, retry
`DEADLINE_EXCEEDED`	Query timeout — miners can't fulfill request	Use broader date range, switch to keyword search
Empty `data` array	No matching results	Broaden search terms or date range

Skills social-data

Macrocosmos SN13 API - Social Media Data Skill

Metadata

Required Environment Variables

API Endpoint

Headers

Request Format

Parameters

Response Format

curl Examples

1. Keyword Search on X

2. Fetch Tweets from a Specific User

3. Multi-Keyword AND Search

4. Reddit Search

5. User + Keyword Filter

Python Examples

Using the
`macrocosmos`
SDK

Using
`requests`
(REST)

Tips & Known Behaviors

What works reliably

What can be flaky

Best practices for LLM agents

Gravity API (Large-Scale Collection)

Create Task

Check Status

Build Dataset

Get Dataset Download

Workflow Summary

Error Reference