book-to-skill vs Proto-Mind: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of book-to-skill and Proto-Mind — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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book-to-skill
virgiliojr94
Convert technical books, docs, and PDFs into a unified agent skill your AI coding assistant can reference in Claude Code, Copilot CLI, or Amp.
Key features
- Multi-Format Ingest: Accepts PDF, EPUB, DOCX, Markdown, HTML, RTF, and MOBI as source material.
- Folder & Multi-Source Support: Bundle a directory of mixed documents into one unified skill rather than one file per skill.
- Agent Skills Standard Output: Produces skills that conform to the Agent Skills Open Standard so any compliant agent can load them.
- Assistant Compatibility: Works with Claude Code, GitHub Copilot CLI, and Amp out of the box.
- Token-Efficient Retrieval: Reports 24×–51× fewer tokens than dumping the source text into context.
- MIT-Licensed CLI: Ships as an installable command-line tool with open-source license and GitHub releases.
Best for
- Personal Study Reference: Convert a technical book you're reading into a skill your coding agent can quiz you on or cite while you code.
- Domain-Knowledge Onboarding: Package a company's PDF handbook or spec collection so new-hire agents can answer questions without human bandwidth.
- Framework Documentation: Turn a language or framework's PDF/HTML docs into a locally referenceable skill for offline agent use.
- Research Collection: Bundle a folder of papers into one skill so an agent can cross-reference them during writing sessions.
- Legacy System Playbook: Ingest older manuals or runbooks (RTF, DOCX) so agents helping with maintenance have grounded answers.
Proto-Mind
VIRENCORE
A native macOS floating workspace that keeps AI conversations, project memory, files and live voice together on your Mac.
Key features
- Floating Cube Workspace: Hover the cube to reveal the workspace and click to pin it, or move away to hide it while tasks keep running in the background.
- Per-Conversation Model Routing: Each chat picks its own model and account — ChatGPT with Codex access, supported model APIs, or a local Ollama model.
- Editable Project Memory: Notes, decisions and preferences stay attached to a project and carry into later conversations, and you can review, change or remove any of them.
- Live Voice Control: Speak to open a project, steer a running task or send new work, and add a correction while the task is still going.
- Detachable Companion Windows: Pull out and resize a browser, a file or a second conversation so reference material sits beside the work.
- Explicit Mac Access: Codex can work with files and run commands only after you turn Mac access on; screen control additionally requires Codex Desktop's signed Computer Use helper.
- Local Data Storage: Conversation history and saved memory live on your Mac, and cloud processing happens only when you choose a cloud model or voice.
- Open Source Beta: The macOS installer and the Apache 2.0 source are both published, so the workspace can be inspected and built from source.
Best for
- Long-Running Project Work: Keep a website or client project's decisions in project memory so each session resumes instead of re-explaining the brief.
- Brief to Deliverable: Have the agent read a client brief and save a proposal document, then open it in a companion window next to the conversation.
- Parallel Task Execution: Start several tasks across different models at once and check back on them without blocking the conversation you are in.
- Hands-Free Steering: Dictate a correction or open a project by voice while your hands are busy elsewhere on the Mac.
- Privacy-Sensitive Drafting: Run a local Ollama model so conversation content never leaves the machine.
- Model Comparison: Put the same question to a Codex route and a local model in adjacent windows to compare the answers side by side.