01
Identify the source
Recognize document type, scope, metadata, author intent and the reading objective.
DEEP READING WORKFLOW
No-Loss Deep Reading
Read deeply without flattening the source into a shallow summary.
No-Loss Deep Reading is a structured workflow for books, reports, papers and other long-form material. It preserves context, structure, evidence, terminology and important passages while still producing a usable synthesis.
What it does
Why “no-loss”
Most summaries optimize for compression. That is useful, but aggressive compression can erase the logic connecting ideas, the evidence behind claims, important definitions, counterarguments and subtle distinctions. This workflow is designed to retain the information that matters before compressing it into a clearer mental model.
Workflow
From ambiguity to the next move.
02
Map the structure
Build a chapter / section map so later synthesis keeps the source’s architecture intact.
03
Extract without flattening
Capture arguments, evidence, examples, terminology, important passages and internal relationships.
04
Separate evidence from analysis
Clearly distinguish text-backed content, reasonable inference and AI-generated interpretation.
05
Synthesize for use
Turn the retained material into a structured report, knowledge map, takeaways and follow-up questions.
Typical outputs
Turn analysis into usable outputs
- Document / chapter structure map
- Key arguments and supporting evidence
- Definitions, concepts and terminology
- Important passages and memorable lines
- Source-backed content vs inference vs AI analysis
- Practical takeaways, questions and contradictions
Designed for
Who can use it
- Readers working through serious nonfiction
- Professionals reviewing long reports
- Students and researchers organizing dense material
- Anyone who wants compression without losing the reasoning
Principles
Reading modes & quality controls
- Supports Complete, Balanced and Executive reading modes.
- Designed for both nonfiction and fiction, with different analysis emphasis.
- Preserves source structure before producing conclusions.
- Separates what the source says from what the AI infers or analyzes.
- Treats uncertainty, missing context and ambiguous claims explicitly instead of silently filling gaps.
Public release
Browse my published AI Skills
This skill is also in China beta on Xiaping. You can also browse my public AI Skills through my Agensi and Capafy profiles.