agentic-paper-digest-skill

What This Skill Does

The agentic-paper-digest-skill is a powerful utility designed for researchers, developers, and AI enthusiasts who need to keep pace with the rapidly evolving fields of machine learning and artificial intelligence. By tapping into arXiv and Hugging Face, the skill automates the discovery, filtering, and summarization of the latest academic and technical papers. It uses advanced Large Language Model (LLM) pipelines to ensure that only the most relevant content reaches you, saving hours of manual browsing. Whether you need a daily digest delivered in JSON format for a downstream automated workflow or a quick summary to inform your current project, this skill provides a robust architecture consisting of both a CLI for ad-hoc usage and a local API server for continuous polling and integration into your existing agentic systems.

Installation

Installation is streamlined via the OpenClaw ecosystem. You can install the skill by running the following command in your terminal: clawhub install openclaw/skills/skills/matanle51/agentic-paper-digest-skill. Once installed, use the provided bootstrap script: bash "{baseDir}/scripts/bootstrap.sh". Ensure that your environment is configured with the necessary API keys, specifically OPENAI_API_KEY or valid LiteLLM-compatible credentials. If you wish to enable advanced features like PDF text analysis, install the optional dependencies by running pip install pymupdf within your virtual environment. You can customize the project directory by exporting the PROJECT_DIR variable before running the bootstrap script.

Use Cases

• Automated Research Updates: Set up a cron job or background process to fetch the latest AI papers every morning and summarize them into a daily reading list.
• Downstream Data Integration: Feed structured JSON output from the paper digest into a knowledge graph or a Slack notification bot to keep your team informed.
• Topic-Specific Monitoring: Configure the ARXIV_CATEGORIES setting to focus exclusively on your niche, such as computer vision or natural language processing, filtering out noise from unrelated fields.
• API-First Development: Use the local API endpoint to build custom front-ends or dashboard widgets that visualize the latest trends in the research community.

Example Prompts

1. "Check for any new papers on arXiv related to Large Language Models in the last 12 hours and summarize the key findings for me."
2. "Run the paper digest pipeline for the default sources and save the output as a JSON file for my research archive."
3. "Fetch the latest Hugging Face paper releases and filter them based on the 'cs.LG' category, then provide a bulleted summary of the top three."

Tips & Limitations

To get the best results, ensure your LLM model settings (temperature and model selection) are tuned correctly; lower temperatures generally yield more concise and deterministic summaries. Note that the skill relies on external API connectivity; if arXiv or Hugging Face experience downtime, the fetch process may be delayed. The MAX_CANDIDATES_PER_SOURCE configuration is crucial—adjust this based on your token budget and processing speed requirements. Finally, always verify your OPENAI_API_KEY permissions if you encounter errors during the summarization phase, as these LLM calls are the backbone of the filtering intelligence.