dsh-deepread
Lets your agent read documents deeply, like articles and PDFs, creating knowledge maps. Useful for understanding complex information in a structured way.
What it does
This tool helps you understand long documents like articles, books, or web pages. Instead of a simple summary, it separates main ideas, supporting evidence, data, examples, assumptions, counterarguments, and limitations.
When to use it
- ✓When you need to critically analyze a long text.
- ✓When it's important to know where information comes from and how it connects.
- ✓When you want to test your understanding of the material.
Example of what to ask your AI
“Analyze this article and tell me the main claims, the evidence for each, and any limitations mentioned.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool reads the document and identifies different types of information, such as claims and evidence. It then organizes everything in a structured way, showing how ideas connect and what the sources are for each piece of information.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill dsh-deepreadOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/dsh-deepread/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
DeepRead
Overview
DeepRead turns long-form material into an evidence-first reading report. It separates claims, evidence, data, examples, assumptions, counterarguments, and limitations instead of producing an untraceable summary.
The workflow supports five modes: quick orientation, deep argument analysis, knowledge mapping, Feynman explanation, and whole-book synthesis. Use the host agent's available file, PDF, OCR, and web-reading tools; never invent source content that was not successfully retrieved.
When to Use
- Use when a user asks to read, summarize, or critically analyze an article, book, PDF, web page, or document collection.
- Use when important claims must remain connected to evidence and source locations.
- Use when the user wants a mind map, concept map, comparison matrix, or structured study notes.
- Use when the user wants to test understanding with plain-language explanations or recall questions.
- Use when multiple documents need to be compared without collapsing disagreements into one answer.
Choose a Mode
| Mode | Use it for | Required output |
|---|---|---|
quick |
Orientation or time-limited reading | Short summary, core claim, up to three supporting points, open questions |
deep |
Argument analysis | Claim hierarchy, reasoning chain, evidence, concepts, counterarguments, limitations |
map |
Knowledge organization | Claim-evidence-data table, labeled relationships, confidence tags, concept map |
feynman |
Understanding and retention | Plain-language explanation, knowledge gaps, corrections, recall plan |
book |
Whole-book synthesis | Chapter map, thesis development, cross-chapter links, final evaluation |
Default to deep unless the user names another mode or the time budget clearly calls for quick.
How It Works
Step 1: Establish the Reading Contract
Record:
- The source or set of sources.
- The user's reading question.
- The selected mode.
- The desired depth and output format.
- Any deadline, token budget, or chapter limit.
If the source cannot be accessed, stop and request the text or a readable file. Do not fill gaps from memory.
Step 2: Survey Before Reading Closely
Inspect the title, author, date, table of contents, headings, abstract or introduction, conclusion, figures, and tables. Convert the structure into three to seven questions the reading should answer.
For a long source, divide it on semantic boundaries such as chapters or headings. Keep a progress list and synthesize only after every selected section has been processed.
Step 3: Extract Atomic Reading Units
Each note should contain one idea and one type:
claim: a conclusion the author wants the reader to accept;reason: a premise or mechanism supporting a claim;evidence: a quotation, observation, method, or source-backed result;data: a numeric fact with unit, time, population, baseline, and source when available;example: an illustration that must not be treated as general proof;assumption: an unstated dependency of the argument;counterargument: a challenge or alternative explanation;limitation: a boundary on where the claim applies;action: a recommendation that follows from the analysis.
Keep the author's statements separate from the agent's inference and the user's interpretation.
Step 4: Build the Evidence Ledger
For every important claim, record:
| Field | Requirement |
|---|---|
| Claim | Complete proposition, not a topic label |
| Evidence | Source passage or faithful paraphrase |
| Location | Page, section, paragraph, timestamp, or URL anchor when available |
| Data context | Value, unit, timeframe, sample, baseline, source |
| Relationship | Supports, contradicts, causes, explains, depends on, exemplifies, or limits |
| Confidence | Author claim, source fact, reasoned inference, or unverified |
| Caveat | Missing evidence, alternative explanation, or applicability boundary |
Write source does not provide evidence when appropriate. Never manufacture a supporting quotation or location.
Step 5: Produce the Mode-Specific Artifact
For deep, organize the report as:
- Reading question and concise answer.
- Core thesis and subclaims.
- Argument flow with evidence.
- Key concepts and definitions.
- Strongest evidence and weakest link.
- Counterarguments and limitations.
- Practical implications.
…
FAQ
What are 'atomic reading units'?
These are the smallest pieces of information the tool identifies, such as a claim, evidence, data, or an example.
What is a 'Knowledge Map'?
It's a visual representation that shows how ideas and evidence within a document are connected to each other.
What is the 'Feynman Check'?
It's a method to ensure you've understood the material by explaining it in simple terms, as if teaching a child, and identifying where the understanding breaks down.
Details
Original description by the author
Use for evidence-first reading of articles, books, PDFs, web pages, or document sets, with knowledge maps and Feynman checks.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2886
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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