rscheiwe/open-skills

Overview

This skill summarizes long text into concise, actionable bullet points and produces basic text statistics. It extracts key sentences, formats a clean bullet summary, and returns a small stats report such as word and sentence counts. The output includes both a human-friendly summary and machine-friendly stats for downstream processing.

How this skill works

The skill splits the input into sentences, computes simple scores (based on length and position), and ranks sentences to pick the top N points. It generates a bullet-point summary and a JSON object with statistics like word_count, sentence_count, and average sentence length. Optional artifacts include a Markdown summary file and a JSON stats file for archiving or sharing.

When to use it

• You need a quick, readable summary of a long article, report, or meeting transcript.
• Preparing executive highlights from verbose content for presentations or emails.
• Pre-processing long documents before feeding into downstream pipelines or search indexes.
• Extracting main ideas from user-submitted content in apps or chatbots.
• Generating condensed notes for research literature or technical docs.

Best practices

• Provide clean, well-punctuated text for more accurate sentence splitting.
• Set max_points to control summary length; tune for desired granularity.
• Use this skill for fast, extractive summaries; consider NLP models for abstractive needs.
• Combine the stats output with other tooling to track content trends over time.
• For production, integrate with dedicated NLP libraries if you need advanced semantic scoring.

Example use cases

• Summarize long blog posts into 3–5 bullet points for social sharing.
• Turn meeting transcripts into concise action-oriented highlights.
• Create quick summaries of academic abstracts or literature reviews.
• Auto-generate content previews for document management systems.
• Produce lightweight summaries for mobile push notifications.