Learn Cog
by @nitishgargiitd
AI tutoring and education powered by CellCog. Study guides, exam prep, coding tutorials, language learning, math help, science explanations, practice problem...
clawhub install learn-cogπ About This Skill
name: learn-cog description: "AI tutoring and education powered by CellCog. Study guides, exam prep, coding tutorials, language learning, math help, science explanations, practice problems β every subject, every level. Explains concepts via diagrams, analogies, worked examples, and interactive lessons." metadata: openclaw: emoji: "π" os: [darwin, linux, windows] requires: bins: [python3] env: [CELLCOG_API_KEY] author: CellCog homepage: https://cellcog.ai dependencies: [cellcog]
Learn Cog - The Tutor That Explains Five Different Ways
The best tutors explain the same concept five different ways. CellCog does too.
#1 on DeepResearch Bench (Apr 2026) for reasoning depth β deep enough to break concepts into first principles β combined with multi-modal output for every learning style: diagrams, analogies, worked examples, practice problems, interactive explanations, and full study guides. Any subject, any level.
How to Use
For your first CellCog task in a session, read the cellcog skill for the full SDK reference β file handling, chat modes, timeouts, and more.
OpenClaw (fire-and-forget):
result = client.create_chat(
prompt="[your task prompt]",
notify_session_key="agent:main:main",
task_label="my-task",
chat_mode="agent",
)
All agents except OpenClaw (blocks until done):
from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw|cursor|claude-code|codex|...")
result = client.create_chat(
prompt="[your task prompt]",
task_label="my-task",
chat_mode="agent",
)
print(result["message"])
How Learn-Cog Helps
Concept Explanations
Understand anything:
Example prompt: > "Explain blockchain technology: > > Level: Complete beginner, no tech background > > Include: > - Simple analogy to start > - How transactions work > - Why it's secure > - Real-world examples > - Common misconceptions > > Use simple language, avoid jargon. Include a visual diagram."
Homework & Problem Solving
Work through problems:
Example prompt: > "Help me understand this math problem: > > Problem: Find the derivative of f(x) = xΒ³sin(x) > > I know basic derivatives but I'm confused about the product rule. > > Please: > 1. Remind me of the product rule > 2. Apply it step by step > 3. Give me 2 similar problems to practice > 4. Show me how to check my answer"
Study Materials
Prepare for success:
Example prompt: > "Create a comprehensive study guide for the AWS Solutions Architect exam: > > Cover: > - Key services and when to use them > - Networking concepts > - Security best practices > - Cost optimization strategies > > Format: Clear sections, bullet points, diagrams where helpful > Include: Practice questions after each section"
Coding & Tech Learning
Level up your skills:
Example prompt: > "Teach me React hooks: > > My level: I know basic JavaScript and HTML/CSS, never used React > > Structure: > 1. What problem do hooks solve? > 2. useState with simple examples > 3. useEffect with practical use cases > 4. When to use which hook > 5. A mini-project putting it together > > Include code examples I can run."
Language Learning
Master new languages:
Learning Styles
Tell CellCog how you learn best:
| Style | Ask For | |-------|---------| | Visual | Diagrams, charts, infographics | | Examples | Multiple worked examples, real-world applications | | Analogies | Comparisons to familiar concepts | | Step-by-Step | Detailed breakdowns, numbered procedures | | Big Picture | Overview first, then details | | Hands-On | Practice problems, projects |
Subjects
CellCog can help with virtually any subject:
STEM:
Humanities:
Professional:
Tech Skills:
Chat Mode for Learning
| Scenario | Recommended Mode |
|----------|------------------|
| Homework help, concept explanations, practice problems | "agent" |
| Comprehensive study guides, full curriculum design, deep research | "agent team" |
Use "agent" for most learning. Quick explanations, homework help, and study materials execute well in agent mode.
Use "agent team" for comprehensive learning - full course outlines, research papers, or when you need multi-source synthesis.
Example Prompts
Concept explanation: > "Explain the concept of recursion in programming: > > My level: Beginner programmer, comfortable with loops > > I need: > - Clear definition > - Visual representation > - 3 progressively harder examples (factorial, fibonacci, tree traversal) > - Common mistakes to avoid > - When to use recursion vs iteration > > Language: Python"
Exam prep: > "Create a study plan for the GRE: > > Timeline: 2 months > Goal: 320+ score > Weak areas: Vocabulary and geometry > > Include: > - Weekly schedule > - Resources to use > - Practice test strategy > - Day-before checklist > > Make it realistic for someone working full-time."
Language practice: > "Help me practice Japanese: > > Level: JLPT N4 > Focus: Conversational situations > > Create a dialogue practice: > - Scenario: Asking for directions in Tokyo > - Include vocabulary list > - Grammar points used > - Cultural notes > - Variations to practice > > Use romaji and kanji with hiragana readings."
Tips for Better Learning
1. State your level: "Complete beginner" vs "I understand the basics" changes everything.
2. Ask why: Don't just ask for answers. Ask for explanations of the reasoning.
3. Request practice: Learning happens through doing. Ask for practice problems.
4. Admit confusion: "I don't understand the part where..." helps CellCog target explanations.
5. Build on previous: Reference what you already understand to get appropriate explanations.
6. Active recall: Ask CellCog to quiz you, not just explain. Testing improves retention.
If CellCog is not installed
Run /cellcog-setup (or /cellcog:cellcog-setup depending on your tool) to install and authenticate.
OpenClaw users: Run clawhub install cellcog instead.
Manual setup: pip install -U cellcog and set CELLCOG_API_KEY. See the cellcog skill for SDK reference.