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interactive learning
A personalized learning coach that diagnoses blind spots and builds a custom curriculum based on deep source analysis.
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interactive learning
Example session with this skill installed
/interactive-learning teach me about the core concepts in the provided book "Thinking, Fast and Slow" using the PDF I just uploaded.
- Read your context and instructions
- Compiled the interactive learning
I have confirmed the source material for "Thinking, Fast and Slow". I have created a topic directory at ~/Documents/interactive-learning/thinking-fast-and-slow/. We will now begin the diagnosis phase to identify your specific blind spots before I build the learning plan.
Diagnostic Phase: Question 1 of 4
Think about a time you made a "gut feeling" decision that turned out to be statistically unlikely. If you had to explain to someone why your brain chose the wrong option despite knowing the math, how would you describe the relationship between your intuition and your logical thinking?
Next steps
- Answer the question above to help me map your current understanding.
- I will then ask three follow-up questions to identify your specific misconceptions.
- Once the diagnosis is complete, I will launch the agents to build the
skeleton.mdwith line-by-line citations.
Connects securely to your tools. The creator never sees your data.
About this skill
Traditional learning often fails because it follows a linear path regardless of what the student already knows. This skill solves that by identifying hidden knowledge gaps before teaching a single concept. It transforms static documents into a dynamic, personalized curriculum that focuses strictly on your blind spots.
What it does
- Blind-spot diagnosis probes for misconceptions using targeted questions rather than simple quizzes.
- Deep-source analysis decomposes reference materials into a granular skeleton with line-by-line citations.
- Adaptive sequencing reorders the learning path to prioritize P0 knowledge gaps over the book's table of contents.
- Socratic instruction uses questioning to lead you to insights rather than lecturing.
- Feynman verification requires you to explain concepts in your own words before unlocking new content.
How it works
- Material Ingest: Provide a topic or upload source files like
.md,.txt, or.pdffor analysis. - Diagnosis: Complete a four-question assessment to map out your current understanding and errors.
- Skeleton Building: The skill runs parallel agents to index the source material with precise line-number evidence.
- Unit Delivery: Engage with one unit at a time, providing feedback and passing Feynman checks to progress.
Frameworks & tools
This skill utilizes standard file system operations for directory management and includes specific support for convert_docx_to_md.sh and web-based source reconstruction for image-heavy PDFs.
Why this beats prompting it yourself
Generic prompts produce generic summaries that gloss over what you don't understand. This skill enforces a strict "diagnose before teaching" rule and uses parallel processing to ensure every claim is backed by a specific line citation from your source text.
Use cases
- Deep-reading technical books to master complex conceptual systems without skimming.
- Onboarding to new codebases by converting documentation into a structured learning path.
- Mastering counterintuitive subjects where your existing mental models might be holding you back.
- Systematic research into specific documents where precise citations are required for every argument.
Known limitations
Scanned or image-based PDFs require web search reconstruction rather than verbatim extraction. Manual feedback is required to advance between units; it will not auto-generate the next lesson.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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