learn-from-materials

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learn-from-materials

Turn books, PDFs, slides, Word documents, web pages, and multi-file material sets into traceable knowledge bases, interactive learning pages, and Markdown derived from the same content data.

learn-from-materials is a cross-agent learning skill built on the open Agent Skills specification. Systematic study emphasizes complete reading; a quick overview extracts the core argument. Both require verifiable sources, a strict separation between material facts and model-added content, offline-first operation, and least privilege.

It works in agent environments that can read files, run local commands, and recognize SKILL.md — such as WorkBuddy, Codex, Claude Code, and GitHub Copilot CLI. Pure chat environments without file-system access or Python execution can only use parts of the prompting workflow; they cannot perform material extraction, coverage validation, or HTML rendering.

Key Features

A Methodology That Spans the Whole Material

It starts by extracting the material's central question and its goal, then organizes the argument, frameworks and action rules into one complete structure. It supports the main flow, decision branches, causal and hierarchical relations, and evidence-backed feedback loops; it does not fix a step count and does not force every material into an eight-step process. The overview is not split into groups by node count, and selecting a node shows its inputs, actions, outputs, check conditions, related learning units and sources.

The whole structure is also saved separately as methodology.json / methodology.md, keeping what the source material itself does clearly distinct from the whole-material synthesis. "Apply This Methodology" carries the entire structure by default, letting the AI locate your entry point and advance you condition by condition; a single card can still be chosen instead.

Guided content shows source hints for core ideas, key points and conclusions on mouse hover and on keyboard focus, plus an expandable "Source" panel for touch screens. These stay available after switching units. When a verified fine-grained location exists, the matching page number is displayed; otherwise the unit-level or conclusion-level range is reported as-is.

See the whole-material methodology protocol, the original three-page teaching PDF, and the systematic-study example. The example includes coverage, action-rule, content-review and original-heading records. Its short length is for demonstration and regression testing, not an extraction ceiling for real materials.

An Accumulating Method Library

After parsing new material, reusable methods are saved to the knowledge base's methods.json, and a readable patterns.md is generated from it. Each card records purpose, prerequisites, limits, steps, effect checks, a short source quotation, a stable ID and a version; when a material contains no methodology, that is stated plainly rather than invented.

Page bindings and the method prompts are derived from that same data. Several method files you designate can be indexed cumulatively, candidate methods can be found by Chinese or English keywords, and comparison records can be exported. Original methods and earlier versions are retained; a synthesized method receives a new ID and an explicit parent-method reference, and never silently overwrites or blends into what the material actually meant.

See the method library protocol and commands, the example method file, the readable method cards and the demo page bound to a method library. The earlier Chinese and English versions, the relationship maps and "Apply This Methodology" all remain available.

Language and In-Page Interaction

• Follows your language: an explicit instruction comes first, then the current request and the conversation; Chinese and English are supported for body text, the interface, Markdown and copyable prompts. Original quotations and source names are preserved. Language is never guessed from nationality or browser locale, and existing HTML is never auto-translated.

• Logical relationship maps: core framework cards remain available and switch to a collapsible framework mind map with search, zoom and fullscreen. Hover or focus a node or link for its explanation and source; independent branches and cross-links retain the original relationships. Action rules still show the whole-material methodology graph.

• Reader themes and motion: new pages use Sea Salt, Ink Wash or Bamboo Moon with a compact horizontal layout, larger diagrams, readable floating explanations and an optional motion switch. Both diagrams retain their links when motion is off.

• Apply This Methodology: enter your question, goal and constraints, choose the whole structure, a specific method or automatic selection, and copy the resulting prompt into your material conversation. The prompt asks your agent to assess applicability before giving sourced analysis and suggested actions. The page does not run an AI itself.

• Supports PDF, EPUB, MOBI/AZW/AZW3, DOCX, PPTX/PPTM, HTML, Markdown, TXT, RTF, and multi-material collections

• Two learning depths: "Quick Overview" and "Systematic Study"

• Systematic study reads to the end and audits every method candidate's disposition; card counts do not establish completeness across books, papers, industry reports, slides and other materials

• Generates a traceable .learnkb/ knowledge base, a self-contained interactive HTML page, and Markdown derived from the same content data

• Fine-grained source mapping for PDF pages, PPT slides, and EPUB chapters

• Produces core-framework, guided-content, glossary, action-rules, dynamic self-check, and local-notes pages

• Systematic study runs full coverage, summary mapping and reverse checks; quick overviews scan the complete structure and audit displayed citations. Both check source hashes

• Distinguishes material-backed facts, areas not covered by the material, model-added content, and externally verified items

• Offline by default with no automatic dependency installation; page notes, learner profiles, and wrong-answer records stay in the local browser only

Framework Maps with Many Unlinked Cards

Connected frameworks appear in compact groups. Frameworks with no recorded links stay visible in a responsive grid below, retaining their card numbers and source order. “No links recorded yet” describes this graph, not conceptual independence in the original material. No card limit, artificial root or decorative relationship is added. Search lists every match; selection traces direct links or exact edge endpoints. Long English/Chinese labels are measured, wrapped and given real wire gaps, with conflicting routes moved into clear corridors. The same renderer keeps cycles, cross-links and all nodes when expanded.

Learning Depths