A powerful CLI-based tool for AI agents to interact with Miniflux RSS feeds, allowing for article discovery, reading, and management.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install miniflux
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 miniflux 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 Miniflux?
The Miniflux skill empowers your AI agent to become a sophisticated news and information consumer. By integrating directly with a Miniflux instance, it provides a structured way to access RSS and Atom feeds, enabling agents to stay updated on technical blogs, news outlets, and research papers. This Openclaw Skills component bridges the gap between static knowledge and real-time web content through a robust API-driven interface.
With this Openclaw Skills addition, developers can build workflows where an agent monitors specific categories, filters for keywords, and handles long-form content using built-in pagination. It transforms a standard RSS reader into a programmable data source for LLMs, complete with metadata like reading times and word counts to optimize token usage.
Miniflux Use Cases
- Monitoring industry news or technical blogs for specific keywords.
- Summarizing unread articles from a research feed to stay current.
- Automating the "mark as read" workflow based on content relevance or agent analysis.
- Retrieving full-text content of RSS entries for data analysis and knowledge base expansion.
How Miniflux Works
- The agent initializes the skill using a Miniflux URL and API key through environment variables or CLI flags.
- It queries the list command to identify new or unread articles across feeds or specific categories using filters like status or search queries.
- For interesting entries, the agent uses the get command to fetch the full text, summary, or a specific character range.
- If an article is exceptionally long, it utilizes the stats command to check word counts and then retrieves content in chunks using pagination flags.
- Finally, the agent can update the state of the article in Miniflux by marking it as read, unread, or starred for later follow-up.
Miniflux Setup
To get started with this Openclaw Skills integration, you can configure your credentials via environment variables for a seamless agent experience:
# Recommended: Set environment variables
export MINIFLUX_URL="https://miniflux.example.org"
export MINIFLUX_API_KEY="your-api-key"
# Alternatively, use CLI flags to initialize configuration
uv run scripts/miniflux-cli.py --url="https://miniflux.example.org" --api-key="xxx" list
Miniflux Data Schema & Taxonomy
The skill interacts with Miniflux objects and outputs data in several formats to suit agent processing needs.
| Attribute | Description |
|---|---|
| ID | Unique identifier for articles, feeds, and categories used for retrieval. |
| Status | Article state filter: read, unread, or removed. |
| Output Format | Display types: brief (titles), summary (preview), full (content), or json (machine-readable). |
| Content Pagination | Control over data flow using --offset and --limit flags for long-form text. |
Miniflux Advanced Features
- Intelligent pagination with offset and limit flags to handle large articles without exceeding LLM context windows.
- Detailed statistics engine that provides word counts and estimated reading times per article.
- Native JSON output support for machine processing within complex Openclaw Skills workflows.
- Multi-article batch processing for marking multiple entries as read or unread in a single command.
- Flexible feed and category management to filter information at the source.
SKILL.md
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