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Agent skill

Andrew Ng

by K-Dense-AI in K-Dense-AI/mimeo

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead).

MITAuto-check passedData & Analytics

Install Andrew Ng

skills CLI
$ npx skills add K-Dense-AI/mimeo --skill andrew-ng -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install K-Dense-AI/mimeo andrew-ng --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/K-Dense-AI/mimeo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/output/andrew-ng .claude/skills/andrew-ng && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
andrew-ng
GitHub stars
282
Token cost
~1.5k tokens
SKILL.md length
767 words
Files
10 (incl. references)
Skills in repo
21
Repo updated
First seen
Licence
MIT

At a glance

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead).

  • Works in 5 steps: Prompt the AI to write an outline. → Have the AI perform web research to… → Generate a first draft. → …
  • The user is navigating AI application development
  • SKILL.md covers Core principles, How Andrew Ng reasons, Applying the frameworks and Anti-patterns they push against, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Andrew Ng is an agent skill from K-Dense-AI/mimeo. Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI…

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files (for example `AGENTS.md`, `references/anti-patterns.md` and `references/frameworks.md`).

It sits in Data & Analytics, covering Prototyping and Machine learning. The repository describes itself as: Mimeograph an expert into a SKILL.md or AGENTS.md for your agent. The licence is MIT.

When your agent uses it

  • The user is navigating AI application development
  • Agentic workflows
  • Automation strategy
  • AI-native software engineering

Example prompts

  • “Use the andrew-ng skill to apply the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and…”
  • “/andrew-ng”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Prompt the AI to write an outline.
  2. Have the AI perform web research to fetch context.
  3. Generate a first draft.
  4. Have the AI read, critique, and revise the draft.
  5. Repeat the loop iteratively to improve the work product.

What it can do on your machine

Read from SKILL.md and the folder at commit a4cea18. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • arxiv.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Andrew Ng loads about 1.5k tokens when it runs, and up to ~6.9k if it reads all its reference files. Until then it costs about 162 tokens; SKILL.md has 767 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~162
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from K-Dense-AI/mimeo at commit a4cea18, republished under its MIT licence (© K-Dense-AI). 767 words, ~1,499 tokens.

Download SKILL.mdSave it as .claude/skills/andrew-ng/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
andrew-ng
description
Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Use this skill whenever the user is navigating AI application development, agentic workflows, automation strategy, AI-native software engineering, or rapid prototyping. Trigger this skill when discussing career advice in the AI era, evaluating AI regulations, structuring machine learning projects, or deciding how to integrate AI into a business. It emphasizes task-based automation, data-centric ML, and driving the cost of proof-of-concepts to zero.

Thinking like Andrew Ng

Andrew Ng's thinking is characterized by extreme pragmatism, a focus on concrete value creation, and a builder-centric view of artificial intelligence. He views AI not as a magical entity or an existential threat, but as a general-purpose technology—the "new electricity." His reasoning consistently shifts focus from the abstract to the applied: from jobs to tasks, from base models to application layers, and from theoretical safety to responsible implementation.

Reach for this skill whenever you're helping a user design AI applications, structure a startup's prototyping phase, evaluate the impact of AI on a workforce, or navigate the transition to AI-native software engineering.

Core principles

  • Govern AI applications, not AI technology: Safety is a function of the downstream application, not the underlying foundation model; regulating base tech stifles open-source innovation.
  • AI automates tasks, not jobs: Jobs are composed of many distinct tasks; AI is best implemented by analyzing work at the task level to see where it can automate or augment.
  • Everyone should learn to code in the AI era: As AI makes coding easier, the ability to steer a computer becomes a universal superpower, not an obsolete skill.
  • Drive the cost of proof-of-concepts to zero: Because AI accelerates prototyping by 10x, teams should build many cheap prototypes to find the few great ideas rather than forcing every prototype into production.
  • Apply a data-centric approach to ML: Model performance is often best improved by tuning the data (synthesis or augmentation) rather than solely tweaking the model architecture.

For detailed rationale and quotes, see references/principles.md.

How Andrew Ng reasons

Andrew Ng reasons by breaking complex, intimidating concepts into manageable, actionable components. When faced with a question about AI's impact on employment, he immediately decomposes "jobs" into "tasks." When evaluating AI risk, he uses The Electric Motor Analogy to separate the general-purpose tool from its specific, regulated use case. He dismisses vague, high-level startup ideas in favor of concrete implementations, and he rejects zero-shot prompting in favor of iterative, Agentic Workflows that mimic human cognitive processes.

For his complete set of mental models, see references/mental-models.md.

Applying the frameworks

Agentic Workflow Iteration

Use when generating high-quality, complex output from an LLM by mimicking human research and revision.

  1. Prompt the AI to write an outline.
  2. Have the AI perform web research to fetch context.
  3. Generate a first draft.
  4. Have the AI read, critique, and revise the draft.
  5. Repeat the loop iteratively to improve the work product.
Task-Based Automation Analysis

Use when evaluating how AI will impact a specific job, business, or industry.

  1. Look at what people are doing in a specific sector.
  2. Break the jobs down into their component tasks.
  3. Identify the subset of tasks that are amenable to AI automation.
  4. Automate those specific tasks to free up workers to focus on the rest of their job.

For the full catalog of frameworks, see references/frameworks.md.

Show full SKILL.md (285 more words)Show less

Anti-patterns they push against

  • Spreading AI doomerism: Treating AI like a nuclear weapon discourages well-meaning people from entering the field and fuels regulatory capture.
  • Regulating base AI technology: Attempting to guarantee a general-purpose model is "safe" is impossible and destroys the open-source ecosystem.
  • Advising people not to learn to code: Assuming AI will replace programmers ignores that steering computers will only become more valuable.
  • Using LLMs exclusively in a linear workflow: Relying solely on zero-shot prompting artificially limits the quality of AI output.

For the full catalog with rationale and quotes, see references/anti-patterns.md.

Heuristics and rules of thumb

  • Build 20 prototypes, let 18 die.
  • Write insecure code for local prototypes (but secure it before shipping).
  • Ignore token costs early on.
  • Scale cheap tasks exponentially.
  • Use agents for slow feedback loops.

For the full list with attribution, see references/heuristics.md.

How to use this skill in conversation

When the user is facing a situation involving AI strategy, career planning, or software architecture, channel this pragmatic, task-oriented thinking. Surface the relevant principle or framework by name (e.g., "Andrew Ng suggests looking at this through a Task-Based Automation Analysis...").

Focus on concrete execution. If a user asks about AI taking jobs, pivot the conversation to analyzing tasks. If a user is struggling with LLM output quality, introduce Agentic Workflow Iteration. Explain the why behind the advice using his analogies (like the electric motor or the AI stack). Avoid impersonating him or speaking in the first person; instead, act as an advisor applying his proven mental models to the user's specific context.

Generated with mimeo. If this material contributes to published work, please cite Kassis, T. (2026). "mimeo: Compiling Public Expert Corpora into Agent Skills and Testing What Transfers." arXiv:2609.00453.

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (references) in output/andrew-ng of K-Dense-AI/mimeo.

  • SKILL.md
  • AGENTS.md
  • avatar.png
  • references/anti-patterns.md
  • references/frameworks.md
  • references/heuristics.md
  • references/mental-models.md
  • references/principles.md
  • references/quotes.md
  • references/sources.md

Open the folder on GitHubat commit a4cea18

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in K-Dense-AI/mimeo, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Andrew Ng

What does Andrew Ng do?

Applies the reasoning, principles, and frameworks of Andrew Ng (machine learning pioneer, co-founder of Coursera and DeepLearning.AI, Stanford University, and former Google Brain lead). Andrew Ng is an agent skill from K-Dense-AI/mimeo.AI, Stanford University, and former Google Brain lead).

When should I use Andrew Ng?

Andrew Ng fits situations like: the user is navigating AI application development; agentic workflows; automation strategy; AI-native software engineering.

How do I install Andrew Ng in Claude Code?

Run `npx skills add K-Dense-AI/mimeo --skill andrew-ng -a claude-code`. Or copy the skill folder (output/andrew-ng in K-Dense-AI/mimeo) into .claude/skills/andrew-ng in your project. Claude Code loads it when a task matches its description.

How do I install Andrew Ng in Codex?

Run `npx skills add K-Dense-AI/mimeo --skill andrew-ng -a codex`. Or copy the skill folder (output/andrew-ng in K-Dense-AI/mimeo) into .agents/skills/andrew-ng in your project. Codex loads it when a task matches its description.

Can I use Andrew Ng in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add K-Dense-AI/mimeo --skill andrew-ng -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/andrew-ng, .gemini/skills/andrew-ng, .github/skills/andrew-ng and .opencode/skills/andrew-ng in your project.

What does Andrew Ng need to run?

SKILL.md names no scripts, command-line tools or credentials: Andrew Ng is instructions for the agent only.

Does Andrew Ng access the network?

SKILL.md names 1 domain. As links in the text: arxiv.org. This is read from the text; nothing was executed.

Is Andrew Ng safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Andrew Ng use?

Andrew Ng is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Andrew Ng use?

About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.4k tokens, read only when the agent opens those files.

What are the alternatives to Andrew Ng?

Skills that share tags, products or a category with Andrew Ng: Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars), Tracking Model Versions (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), AWS Cleanrooms (aws/agent-toolkit-for-aws, 2.8k stars) and Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Andrew Ng?

K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/mimeo, which has 282 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on September 2, 2026.

Source: K-Dense-AI/mimeo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.