AI Summary Reader
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About this skill
Fast Understanding for Long Text
When reading technical articles, papers, or long documents, the hard part is not having text, but extracting conclusions, methods, data, and reasoning quickly, and deciding which parts deserve a closer look. AI Summary Reader treats this as a reusable reading assistant: it takes source text and returns a structured, prioritized, follow-up-ready summary instead of generic “this article introduces…” statements.
How Summaries Are Generated
The skill is prompt-driven, with no external script dependencies, and can run in cloud agents or local LLMs. Core capabilities include:
- Multi-mode summaries: TL;DR, structured, outline, targeted extraction, comparison, and deep reading.
- Adaptive output: adjusts detail based on user intent and source length, while preserving key data, conclusions, and uncertainty.
- Reading navigation: highlights sections worth reading or skipping, then proposes follow-up questions.
The typical flow is to confirm the input source and length, match the user's intent to a summary mode, generate a formatted result, and suggest next questions such as expanding a point or extracting evidence. For texts over 5,000 words, it uses a segmented approach to reduce detail loss from aggressive compression.
Boundaries and Usage Notes
It works on pasted text, file contents, or web-page text obtained by an agent, but it does not fetch URLs itself. It is not a translation tool by default; cross-language summaries must be requested explicitly. Because compression loses detail, high-stakes decisions, fact-checking, and full stylistic reading still require consulting the original. If the input is code, raw data, or poorly structured content, it first offers a preliminary judgment and suggests a better question type rather than blindly summarizing it as a normal article.
Use Cases
- Extract the core conclusion from a technical article and decide whether it is worth implementing
- Trace the argument structure, causal chain, and key data in a long document before a report
- Compare 2-3 research articles and list their agreements and disagreements
- Focus on methods, results, and limitations in a paper while skipping irrelevant background
Best For
- Data analysts who need to extract conclusions from technical articles quickly
- Product or technical writers who need to clarify argument logic for reports
- Researchers comparing multiple literature sources in a domain
- Developers extracting methods, data, and conclusions on demand
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