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SKILL.md
npx skills add learnprompt/andrej-karpathy-skills --skill karpathy-understanding-first
Apply the Karpathy understanding-first principle — verify what AI built, own your understanding, map the jagged capability landscape of LLMs. Use this skill when the user has used AI to build something and needs to verify it, wants to audit their own understanding, needs to know where LLMs are reliable vs unreliable, or says "verify this", "do I actually understand this", "what can I trust the AI on", "jagged capabilities", "understanding not outsourcing". Based on 46k-like quote post.
npx skills add learnprompt/andrej-karpathy-skills --skill karpathy-understanding-first
Source: https://x.com/karpathy/status/2049907410303865030 "You can outsource your thinking but you cannot outsource your understanding." — 46k likes Also: Jagged Capabilities Modeling (Sequoia conversation)
Outsourcing is fine. Atrophy is not.
Use AI to go 10x faster. But every time you skip understanding something, you're borrowing from your own future. Understanding compounds. Blindly shipping agent output does not.
Karpathy's "jagged capabilities" insight: LLMs are wildly good at some things (verifiable, high-data domains) and silently wrong at others (off-distribution, novel combinations). You need a mental map of which is which.
After every agent-assisted work session, run this:
LLMs are NOT uniformly capable. Use this mental model:
These are well-defined, verifiable, data-rich domains:
Before trusting an AI output on something important:
Karpathy tracks which skills are atrophying (taken over by AI) and which are growing. Monthly check:
Before shipping any agent-produced output:
When AI writes something you don't understand — learn from it, don't just ship it:
属于工作流：反偏见决策（第2步）+ 工作流：月度体检（第2步）
| 位置 | 上游 | 下游 |
|---|---|---|
| C第2步 | karpathy-llm-simulator（辩论后） | karpathy-system-prompt-learning（沉淀规则） |
| D第2步 | karpathy-meta-reflection（审计后） | karpathy-practice-environments（建练习环境） |
反偏见决策链路：llm-simulator → understanding-first → system-prompt-learning 月度体检链路：meta-reflection → understanding-first → practice-environments → education-first
I need to evaluate how reliable your output is for this specific task:
Task type: [DESCRIBE WHAT YOU ASKED THE AI]
Output: [PASTE THE OUTPUT]
Help me assess:
1. How common is this exact pattern in training data? (ubiquitous / common / rare / novel)
2. What's the easiest way to verify this without running it?
3. What's the most likely error — hallucinated API? Wrong logic? Outdated syntax?
4. What search query would I use to double-check the key claim?
5. Should I trust this output? (YES/VERIFY_FIRST/DEFINITELY_CHECK)Run a skills audit for [MONTH/YEAR]:
Things I've been having AI do for me recently:
[LIST 5-10 TASKS]
For each:
1. Is my ability to do this WITHOUT AI declining? (YES / NO / UNSURE)
2. Is that OK? (Critical skill I must maintain / Commodity I'm fine outsourcing)
3. Action: KEEP_DELEGATING / PRACTICE_MANUALLY / LEARN_DEEPLY
Final question: What's one thing I should deliberately do WITHOUT AI this month to stay sharp?Understanding verification:
- [ ] I can explain what this does in plain English without looking at it
- [ ] I've traced through the logic manually for at least one example
- [ ] I know what the failure mode looks like
- [ ] I've run it / tested it myself (not just read the output)
- [ ] If it's wrong, I'll know how to debug it
- [ ] I'm not shipping something I'd be embarrassed to explain in a code review
If any box is unchecked: understand it before shipping.You wrote this code/text and I don't fully understand it:
[PASTE OUTPUT]
Teach me:
1. The underlying concept (assume I know [MY_LEVEL] — beginner/intermediate/expert)
2. Walk through the logic step by step
3. What would happen if I changed [SPECIFIC PART]?
4. Point me to the canonical resource where I can learn this properly
5. Give me a simpler version that demonstrates the core idea in isolation
I'm going to use this as a learning moment, not just copy-paste.After completing this task, append an Understanding Report: 1) Key assumptions made (list each), 2) What was verified (with evidence), 3) What remains inferred/unverified (with suggested verification method), 4) Capability boundary notes (where LLM might be off-distribution for this task), 5) Items I must personally inspect or understand before relying on this output.