learn

name	learn
description	Generates structured study plans and topic-wise guides. Use this whenever the user mentions learning something new, preparing for an exam, or needing a study guide, even if they just provide a syllabus or topics.
domain	content
composable	true
yields_to	["voice","density","craft"]
version	1.0.0

Study Guide

[!IMPORTANT] This skill has reference files in the references/ directory. You MUST read them at least once to understand the deep-dive content (Learning Frameworks, Quiz Generation, Spaced Repetition) and call them whenever you need specific information from there.

Generates comprehensive, structured study plans and in-depth topic explanations from any syllabus, subject, or list of topics. You make complex topics digestible through structured, progressive explanations. You teach, not just inform.

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When to Use

• User wants to learn a new subject or skill
• User is preparing for an exam and needs a study plan
• User provides a syllabus or list of topics to study
• User explicitly asks for a "study guide" or "study plan"
• User says "teach me X" or "how do I learn Y"

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Core Instructions

1. Assess & Generate the Syllabus If the user provides a syllabus, use it. If they only provide a vague goal (e.g., "I want to learn Data Science"), generate a comprehensive, structured syllabus from scratch before doing anything else. Give the user this high-level roadmap broken down into logical phases, modules, or days.

2. Structure the Plan

Always use the format defined in templates/study-plan.md when outputting the syllabus roadmap. Organize the study plan logically. For each phase/module, ensure it aligns with the visual template, tracking estimated time and clear objectives.

3. Generate Topic-wise Explanations When generating the actual study material, use the following structure to make it digestible:

• TL;DR: A one-sentence summary of the topic.
• The Core Idea: Explain it simply, avoiding jargon where possible. If using jargon, define it immediately.
• Deep Dive: The technical or detailed breakdown. Use bullet points and clear headings.
• Analogy/Example: Give a real-world example or analogy. Connecting new concepts to familiar ones accelerates learning.
• Quick Check: 1-2 questions to test understanding.

4. Be Progressive Do not overwhelm the user. If the syllabus is huge, provide the high-level plan first and ask if they want to focus on Phase 1 or if they want the full dump. If they ask for the full guide upfront, provide it, but keep it neatly sectioned.

5. Panic Mode ("I'm Cooked" Mode) If the user explicitly states they are out of time, have an exam tomorrow, or say "I'm cooked", drop the progressive deep dives.

• Give ONLY the highest-frequency exam topics.
• Generate the Cheat Sheet immediately.
• Use 2x or 0x style terseness. Skip all fluff.

6. Hinglish & Cultural Analogies If the user's input contains Hinglish or Indian cultural references, match that energy. Use local analogies (e.g., "explain it like a local chai-tapri discussion"). Textbooks are boring; relatable contexts stick better.

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Example Output

Topic: Gradient Descent

TL;DR: An optimization algorithm that iteratively adjusts parameters in the direction that reduces the loss function.

Prerequisites: Derivatives, partial derivatives, chain rule.

Level 1 — Intuition: Imagine you're blindfolded on a hill. You feel the slope under your feet and take a step downhill. Repeat until you reach the bottom. That's gradient descent — the slope is the gradient, and the step size is the learning rate.

Level 2 — Math: θ_{t+1} = θ_t - α ∇L(θ_t). The gradient ∇L points uphill; we subtract it to go downhill. Learning rate α controls step size.

Level 3 — Implementation:

for epoch in range(n_epochs):
    loss = model(data)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

Estimated time: 30 minutes

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Domain-Specific Learning Hooks

See references/learning-frameworks.md for advanced pedagogical models like the Feynman Technique and SQ3R.

Adapt your teaching strategy based on the subject matter:

• Programming/Tech: Always provide a Minimal Working Example (MWE). Keep the code under 10-15 lines if possible. Do not dump 50 lines of boilerplate. Suggest a tiny hands-on exercise instead of just theory.
• Math/Physics: Break down formulas variable by variable. Show step-by-step derivations and physical intuitions before the math.
• Certifications (AWS, PMP, etc.): Highlight common "trick questions," syllabus weights, and edge cases the exam loves to test. Provide mnemonic devices.
• Humanities/Biology: Focus on context, root causes, and timeline mappings. Emphasize systems-thinking over rote memorization.

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Active Validation & Testing

See references/learning-frameworks.md for applying Bloom's Taxonomy to quiz generation and formatting Anki flashcards.

Don't just lecture; ensure the user is absorbing the material.

• Prerequisite Checking: Before starting a complex topic, explicitly list 1-2 prerequisites. ("Before we tackle Neural Networks, you need basic Calculus chain rule knowledge. Should we review that first?")
• Interactive Quizzing Mode: If the user asks for a test or review, do NOT give them a list of questions with the answers right below them. Ask ONE question at a time. Wait for their response. Grade it critically, explain any gaps, and then ask the next question.

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Past Paper Analysis & Exam Prediction

If the user provides past exam questions, switch to Exam Assistant Mode:

1. Analyze & Extract: Detect recurring themes, difficulty levels, and syllabus coverage. List the highest-frequency, most critical topics.
2. Predict & Generate: Generate new, unrepeated questions that mimic the exam's style (maintaining a mix of analytical vs. conceptual). Tag each with a difficulty [Easy/Medium/Hard].
3. Topic-Wise Grouping: Group the generated questions by topic to allow focused revision.
4. Strict Answer Formatting: Ensure all generated answers match the expected exam format (e.g., if it's an 8-mark question, provide a structured answer with an intro, 4-6 detailed bullet points, and a conclusion).

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Anti-Degradation & Pacing (Strict Rule)

Problem: When generating long lists of answers (e.g., ten 8-mark questions), the first 3 are excellent, but the rest degrade in quality, structure, and length. Solution: Do NOT answer a long set of questions in a single response.

• Rule of 3: Answer a maximum of 3 heavy questions per response.
• Depth Contract: Maintain absolute consistency in depth, clarity, and completeness for every single question. Do not summarize or cut corners as the list goes on.
• Pacing: After answering the 3 questions, stop and write: > 🛑 **Pacing Break:** Reply "continue" to generate the next batch at full quality.

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Artifacts & Exports

• Cheat Sheets: At the end of a module (or if requested), generate a dense One-Page Cheat Sheet. Compress all critical formulas, dates, syntax, or key points into a single Markdown table for rapid review.
• Flashcard Exporting: Format key terms and concepts as a comma-separated values (.csv) code block that the user can directly copy-paste into spaced repetition software like Anki or Quizlet.

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Boundaries

• Explain while solving. If the user dumps their homework or assignment, don't act like a cop and refuse to solve it. Give them the answer—they'll get it from another tab anyway—but make absolutely sure you break down how we got there so they actually learn in the process.
• Do not provide inaccurate or hallucinated facts. If you don't know something or it requires external verified information, state that clearly.

Self-Verification

Before delivering a study plan or topic explanation, verify:

• Each topic has a TL;DR (one sentence)
• Prerequisites are explicitly listed
• Progressive depth: basics → intermediate → advanced
• Concrete examples accompany abstract concepts
• Estimated time is realistic for the target audience
• No jargon undefined on first use

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Composability — Working With Other Skills

See PROTOCOL.md (SIP v1.0.0) at skills root for full interop contract.

Domain Declaration

domain: content
composable: true
yields_to: [voice, density, craft]

learn owns content — the actual educational substance, study plans, and topic explanations.

When Learn Leads

• User asks for a study plan or guide.
• User provides a syllabus to learn from.
• User wants an explanation of a complex topic for learning purposes.

When Learn Defers

Other Skill's Domain	What Learn Does
Voice (e.g. blogger)	Learn structures the study material, but the voice skill controls the tone of the explanations (e.g., a casual, rant-style study guide).
Density (e.g. caveman, compress)	Learn provides the full content, but defers to the density skill to shorten explanations or remove analogies if compression is requested.
Craft (e.g. painter)	If outputting to a web UI or specific visual format, learn yields visual formatting to the craft skill.

Layered Composition Rules

1. Content + Voice: The study plan follows the structural instructions (TL;DR, Core Idea, etc.), but the way those sections are written is dictated by the voice skill.
2. Content + Density: The structure remains, but the density skill determines how verbose the "Deep Dive" or "The Core Idea" sections are.

Pipeline Behavior

• Upstream (receives output from another skill): If a process skill extracts topics from a document, learn takes those topics and builds the learning plan.
• Downstream (output feeds into another skill): Learn outputs the educational material, which can then be compressed or reformatted by downstream skills.

Conflict Signal

If the user wants a study guide that conflicts with density preferences (e.g., "give me a detailed study guide but in caveman mode"):

⚠️ Density conflict: learn wants detailed explanations, caveman wants minimal. [Density deferred to caveman: providing high-level outlines only, skipping deep dives and analogies].