pdf2skills converts unstructured PDF books and EPUB files into dynamic, executable AI skills — turning 400-page professional texts into intelligent applications with zero code.
pdf2skills is a specialized knowledge and workflow extraction tool designed for the Manus AI agent platform. Its core mission is to convert unstructured books, PDF documents, and EPUB files into executable Manus skills. Rather than performing simple text extraction, pdf2skills surfaces the implicit workflows hidden within books — making tacit expert knowledge explicit, operational, and reusable. The result is a paradigm we call "Knowledge as Skill": static human knowledge, accumulated over centuries in printed form, is activated into dynamic AI capabilities that anyone can use.
PDF & EPUB ingestion with advanced layout analysis. Filters out case studies, prefaces, and noise — retains core methodology.
AI agents perform Context-to-Skill Conversion — extracting reasoning elements, mapping logic, and generating interactions.
Produces a skills.zip package conforming to the Manus skill-creator specification. Ready to run instantly.
Designs the shortest possible workflow chain for each conversion task. Each node in the pipeline uses the smallest viable model size — down to Nano-level — to minimize cost and maximize response speed without sacrificing output quality.
Prompts are treated as code, not prose. In financial report analysis, for example, the AI does not generate free-form analysis — it extracts data points and triggers a set of 23 pre-defined analysis rule scripts, ensuring deterministic and reproducible results.
Goes beyond surface-level text extraction. The system identifies implicit workflows, pain points, and decision logic within books, then maps them into explicit, executable skill definitions with proper degrees of freedom.
For long-text processing, pdf2skills applies multi-level summarization and structured indexing within a RAG (Retrieval-Augmented Generation) framework, enabling precise retrieval across hundreds of pages.
Generated skills store source material in segmented references/ directories. During execution, the AI dynamically retrieves relevant chapters based on the current task, circumventing context window limitations.
Integrates a Skill Knowledge Graph that provides global prior knowledge, reducing AI misuse of skill components and ensuring domain-appropriate behavior across different professional contexts.
| Domain | Core Logic | Key Extraction Elements | Freedom Level |
|---|---|---|---|
| Financial Analysis | Rule-driven | Three major financial statements, 28 core indicators, ratio calculations | Low — High precision |
| Psychology & Education | Interaction-driven | Exercise steps, life dashboards, compass tools, self-assessments | High — Open exploration |
| Mystery & Gaming | Logic-driven | Suspects, witnesses, alibis, lies, interactive plot elements | Medium — Contextual |
| Growth & Marketing | Strategy-driven | Growth frameworks, funnel models, A/B testing workflows | Medium — Guided |
| Negotiation & Communication | Scenario-driven | Dialogue templates, tactical frameworks, role-play scenarios | High — Adaptive |
A complex design thinking and psychology textbook was transformed into an interactive multi-stage application. The skill includes a Life Dashboard for self-assessment, a Life Compass for value alignment, and structured exercise "levels" that turn abstract methodologies into guided, actionable steps users can follow without prior expertise.
A 400-page professional accounting textbook was converted into an automated financial analysis system. Users upload a PDF financial report, and the AI executes 23 pre-defined analysis rules derived from the book — computing 28 core financial indicators across the three major statements with deterministic accuracy.
Books on mystery writing techniques were processed to extract trick design methodologies. The resulting skill generates logically consistent mystery scenarios — complete with 5 suspects, 2–3 witnesses, individual backstories, alibis, and embedded lies — all maintaining narrative coherence for interactive storytelling.
Growth strategy books were converted into executable marketing workflows. The skill provides structured funnel analysis, hypothesis generation frameworks, and step-by-step experiment design templates — transforming theoretical growth principles into operational playbooks.
Professional negotiation methodology was extracted and structured into role-play scenarios with tactical frameworks. The skill guides users through progressively complex negotiation situations, applying book-derived techniques in realistic dialogue simulations.
Specialized data mining textbooks were converted into guided analytical workflows. The skill automates the extraction and analysis pipeline for financial datasets, applying domain-specific data processing rules and visualization strategies from the source material.
Memect brings deep expertise in natural language processing, knowledge graph construction, and large language model deployment for enterprise applications. The pdf2skills project is a direct product of this accumulated experience — specifically, the team's work on industrializing knowledge extraction at scale.
The Memect Prompt Engineering Lab, led by researchers including Zhang Yibo and Zhao Xinhan, has developed and refined the core prompt engineering methodologies that power pdf2skills. These methodologies have been validated through extensive B2B deployments across financial services, education technology, and content production industries.
pdf2skills embodies Memect's conviction that the future of enterprise AI lies not in general-purpose chatbots, but in domain-specific, skill-based agents that encapsulate genuine professional expertise. By converting the world's professional literature into executable skill libraries, we aim to make expert knowledge universally accessible — regardless of the user's background or training.