A powerful GraphQL-based integration for automating social media scheduling and channel management via the Buffer API.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install buffer-api
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install buffer-api using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Buffer API Integration?
The Buffer skill for Openclaw Skills provides a unified GraphQL interface to manage your entire social media presence. It allows developers and AI agents to programmatically interact with Buffer's robust ecosystem, supporting platforms like Instagram, Facebook, Twitter/X, LinkedIn, TikTok, and more. By leveraging managed authentication through Maton, it simplifies the complex process of OAuth and connection handling.
This skill is designed for high-efficiency content operations, enabling the creation of posts, management of organizations, and tracking of publishing schedules. It acts as a bridge between your AI workflows and social media channels, ensuring that cross-platform content distribution is seamless, secure, and highly customizable through platform-specific metadata.
Buffer API Integration Use Cases
- Automating social media post scheduling across multiple profiles from a single prompt.
- Creating complex content threads on Twitter/X or multi-image carousels on Instagram.
- Managing organizational workflows and team-based social media channels.
- Link-tracking and link attachment management for LinkedIn and Pinterest campaigns.
- Programmatically generating and saving content ideas to the Buffer Idea board for future refinement.
How Buffer API Integration Works
- The skill connects to the Maton API gateway using a secure MATON_API_KEY for managed authentication.
- Users establish a connection to their Buffer account through the Maton connection manager.
- GraphQL queries or mutations are sent as POST requests to the centralized Buffer endpoint.
- The skill processes platform-specific metadata (like Instagram first comments or YouTube privacy settings) to tailor posts for each network.
- Write operations trigger a confirmation step to ensure security and user intent before publishing or scheduling.
Buffer API Integration Setup
To get started with the Buffer skill in your Openclaw Skills environment, follow these steps:
- Obtain your Maton API key from maton.ai/settings.
- Configure your environment variable:
export MATON_API_KEY="YOUR_API_KEY"
- Create a connection to Buffer:
python <<'EOF'
import urllib.request, os, json
data = json.dumps({'app': 'buffer'}).encode()
req = urllib.request.Request('https://api.maton.ai/connections', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
Buffer API Integration Data Schema & Taxonomy
The skill utilizes a structured GraphQL schema to organize social media data:
| Object | Key Fields | Description |
|---|---|---|
| Account | id, email, timezone |
The primary user profile and preferences. |
| Organization | id, name, channels |
Groups of social media channels and team members. |
| Channel | id, service, postingSchedule |
Specific social profiles (e.g., a Twitter account). |
| Post | id, text, status, dueAt |
Individual content pieces, including drafts and sent posts. |
| Idea | id, title, text |
Unscheduled content drafts for future use. |
Buffer API Integration Advanced Features
- Platform-Specific Metadata: Support for unique features like Instagram Reels, Twitter Threads, and YouTube privacy settings.
- Multi-Connection Routing: Use the
Maton-Connectionheader to manage multiple Buffer accounts simultaneously. - Cursor-Based Pagination: Efficiently retrieve large volumes of historical or scheduled posts using
pageInfoandcursors. - Advanced Error Handling: Granular GraphQL error types including
LimitReachedErrorandInvalidInputErrorfor robust debugging. - AI-Assisted Flagging: Metadata support to track if content was generated with AI assistance.
SKILL.md
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