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Karpathy Supply Chain Hygiene

Community
LearnPrompt
karpathy-supply-chain-hygiene

Apply rigorous supply chain security hygiene to software projects — audit dependencies, detect risks, minimize attack surface. Use this skill when the user wants to audit project dependencies, is concerned about supply chain attacks, wants to set up secure dependency management, needs to review a requirements.txt or package.json, or says "dependency audit", "supply chain risk", "check packages", "secure dependencies", "pin versions", "check pypi". Based on Karpathy 28k-like litellm supply chain post.

Overview

PublisherLearnPrompt
Repositoryandrej-karpathy-skills
Skill namekarpathy-supply-chain-hygiene
Stars
118
Forks
18
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by LearnPrompt on GitHub. Read the source before you install it.

Installation

Install the Karpathy Supply Chain Hygiene AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LearnPrompt/andrej-karpathy-skills/tree/main/karpathy-supply-chain-hygiene
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LearnPrompt/andrej-karpathy-skills.git /tmp/andrej-karpathy-skills
mkdir -p .claude/skills
cp -r /tmp/andrej-karpathy-skills/karpathy-supply-chain-hygiene .claude/skills/karpathy-supply-chain-hygiene
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Karpathy Supply Chain Hygiene in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Karpathy Supply Chain Hygiene on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Karpathy Supply Chain Hygiene is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Skill 11: Supply Chain & Security Hygiene(供应链安全卫生)

Source: https://x.com/karpathy/status/2036487306585268612 "Software horror: litellm PyPI supply chain attack" — ~28k likes

Core Principle

Every dependency is a door you didn't build, maintained by someone you don't know.

The litellm incident Karpathy flagged: a widely-used LLM library got compromised via its transitive dependencies. If your project pulled litellm, you were exposed — and you probably didn't even know litellm was in your stack until it was too late.

Karpathy's response: minimize deps, prefer simple implementations, audit everything.

The Full Dependency Audit

Run this for any project before deployment or after any dependency update:

Perform a full supply chain security audit for this project.

Dependency file contents:
[PASTE requirements.txt / package.json / Gemfile / go.mod]

For each direct dependency, analyze:
1. Maintainer health: single maintainer? Last commit? GitHub stars?
2. Transitive depth: how many packages does it pull in?
3. Install-time code execution: does it run arbitrary code on install?
4. Network calls on import: does importing trigger outbound connections?
5. Version pinning: are we using exact versions or ranges?

Risk assessment per package:
- HIGH RISK: (list criteria)
- MEDIUM RISK: (list criteria)  
- LOW RISK: (list criteria)

Priority actions: [what to fix first]

The 5-Minute Pre-Install Check

Before adding any new package:

Security pre-check for: [PACKAGE NAME] v[VERSION]

1. PyPI/npm stats: weekly downloads? Known? Established?
2. GitHub: https://github.com/[owner/repo] — stars, last commit, open issues?
3. Transitive deps: run `pip show [package]` or `npm ls [package]` — how deep does it go?
4. setup.py / postinstall: does it run code on install?
5. Import-time behavior: what does `import [package]` actually do?

Risk verdict: SAFE / VERIFY_FURTHER / AVOID
Alternative: if risky, what can I use instead or implement myself?

Requirements File Hardening

Transform a loose requirements file to a pinned, audited one:

Harden this requirements.txt / package.json for production security.

Current file:
[PASTE FILE]

Output a new version that:
1. Pins all packages to exact versions (== not ~=)
2. Adds a comment for each package: what it's used for, why we need it
3. Flags any packages that could be eliminated with minimal effort
4. Groups by category: core / dev / testing
5. Adds a header comment with: last audited date, total dep count, audit command to run

Also: what command should I run to check for known vulnerabilities?

The Minimal Dependency Philosophy

For each feature you're about to add via a package:

I'm about to add [PACKAGE] to solve [PROBLEM].

Before I do: help me evaluate if I should implement this myself instead.

The function I need: [SPECIFIC FUNCTION/CAPABILITY]

Can this be implemented with:
- Python stdlib only (in < 50 lines)?  YES/NO
- One simple function I can paste inline?  YES/NO

If yes to either: write the minimal implementation I need.
If no: confirm the package is the right choice and suggest the safest way to import it.

Containerization + Isolation Template

For any project that pulls external dependencies:

dockerfile
# Dockerfile template — minimal, audited, isolated
FROM python:3.11-slim  # pin exact base image

# Create non-root user
RUN useradd -m -u 1000 appuser

WORKDIR /app

# Copy ONLY requirements first (layer caching)
COPY requirements.txt .

# Install with hash verification
RUN pip install --no-cache-dir --require-hashes -r requirements.txt

# Copy application code
COPY --chown=appuser:appuser . .

USER appuser

CMD ["python", "app.py"]
# requirements.txt with hashes (generate with pip-compile --generate-hashes)
requests==2.31.0 \
    --hash=sha256:58cd2187423839823... \
    --hash=sha256:942c5a758f98d79...

Incident Response Checklist

If you discover a supply chain compromise:

markdown
IMMEDIATE (within 1 hour):
- [ ] Identify: which version of which package is compromised?
- [ ] Check: is this version in our requirements.txt / lock file?
- [ ] Check: is this version installed in any running service?
- [ ] Isolate: take affected services offline if credentials could be exposed

SHORT-TERM (within 24 hours):
- [ ] Rotate: any credentials, tokens, API keys that ran in the affected environment
- [ ] Audit: what data could have been accessed/exfiltrated?
- [ ] Update: pin to a safe version or remove the package
- [ ] Scan: check all other packages for similar issues

LONG-TERM:
- [ ] Add automated scanning (pip-audit, npm audit, Dependabot)
- [ ] Implement hash pinning for critical projects
- [ ] Consider: can we eliminate this dependency entirely?

Scanning Commands Reference

bash
# Python
pip-audit                           # check for known vulns
pip freeze | pip-audit --stdin      # audit installed packages
safety check                        # alternative scanner

# Node.js  
npm audit
npm audit fix                       # auto-fix where safe

# Check transitive deps
pip show [package]                  # see direct deps
pip-tree [package]                  # see full tree

# Generate hashed requirements
pip-compile --generate-hashes requirements.in

Workflow

属于工作流:想法到上线(第4步)

位置上游下游
第4步(安全检查)karpathy-minimalism(瘦身后)karpathy-vibe-to-agentic(最终收尾)

完整链路:idea-files → agentic-engineering → minimalism → supply-chain-hygiene → vibe-to-agentic

Prompt Contract

text
Audit this dependency before I install it: <PACKAGE_NAME> v<VERSION>. Produce: 1) Transitive dependency count and tree, 2) Maintainer activity (last commit, bus factor), 3) Known CVEs or security advisories, 4) Any postinstall hooks or network calls on import, 5) Safer minimal alternative (or "implement yourself in N lines"), 6) Pinned install command if approved. Risk score: LOW/MEDIUM/HIGH. Recommendation: USE/AVOID/IMPLEMENT_YOURSELF.

Verification Checklist

  • 所有依赖都有 pinned version(==,不是 >=)
  • 已检查 transitive dependency 数量
  • 无 postinstall hook 或已审查其内容
  • 单一维护者的包已标记风险
  • 对高风险依赖提供了自实现替代方案
  • lockfile(requirements.txt / package-lock.json)已提交到 repo

Frequently asked questions

What does the Karpathy Supply Chain Hygiene AI skill do?

Apply rigorous supply chain security hygiene to software projects — audit dependencies, detect risks, minimize attack surface. Use this skill when the user wants to audit project dependencies, is concerned about supply chain attacks, wants to set up secure dependency management, needs to review a requirements.txt or package.json, or says "dependency audit", "supply chain risk", "check packages", "secure dependencies", "pin versions", "check pypi". Based on Karpathy 28k-like litellm supply chain post.

Why use Karpathy Supply Chain Hygiene on TypingMind?

Because you install it once and use it with any model. Karpathy Supply Chain Hygiene is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Karpathy Supply Chain Hygiene in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LearnPrompt/andrej-karpathy-skills/tree/main/karpathy-supply-chain-hygiene. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Karpathy Supply Chain Hygiene?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Karpathy Supply Chain Hygiene?

As many as you like. As long as a model supports skills, you can use Karpathy Supply Chain Hygiene with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Karpathy Supply Chain Hygiene AI skill free?

Yes. It is published on GitHub by LearnPrompt under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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