minimalist – Writing & Content skill for AI agents
Use when the user asks to write code efficiently, avoid over-engineering, reduce dependencies, or prevent unnecessary abstractions. Enforces a strict efficiency ladder: YAGNI, reuse, stdlib, native platform, existing deps — before writing any new code.
Install the minimalist skill with one command
Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/minimalist-ffdd5.tgz | tar xz -C ~/.claude/skills
Call it in Claude Code: /minimalist – then describe the task you want done.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/minimalist-ffdd5.tgz | tar xz -C ~/.codex/skills
Call it in Codex: $minimalist – then describe the task you want done.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/minimalist-ffdd5.tgz | tar xz -C ~/.hermes/skills
Call it in Hermes Agent: /minimalist – then describe the task you want done.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/minimalist-ffdd5.tgz | tar xz -C ~/.deepcode/skills
Call it in DeepSeek: /minimalist – then describe the task you want done.
SKILL.md content
Minimalist
You are highly efficient. The best code is the code never written.
Overview
Use this skill whenever the goal is to solve a problem with the least code possible. It prevents common AI failure modes: inventing helper classes for single-use logic, installing packages for one-line operations, and producing boilerplate that the user will never need.
The Efficiency Ladder
Before writing any new code, stop at the first rung that holds:
1. YAGNI — Does this need to be built at all? If the user hasn't asked for it, don't build it.
2. Reuse — Does it already exist in this codebase? Find the helper, util, or pattern and reuse it.
3. Standard Library — Does the standard library already do this? Use it directly.
4. Native Platform — Does a native platform feature cover it? Use it.
5. Existing Dependency — Does an already-installed dependency solve it? Use it.
6. One-Liner — Can this be one line? Make it one line.
7. Minimum Code — Only then, write the minimum code that works.
Rules of Engagement
- No unrequested abstractions: Do not invent interfaces, base classes, or generics for future-proofing unless the user explicitly asks.
- No unnecessary dependencies: If the standard library can do it cleanly, do not install a package.
- No boilerplate: Deletion over addition. Boring over clever. Fewest files possible.
- Question complex requests: Ask "Do you actually need X, or does Y cover it?" before building X.
- Shortest working diff wins: But only once you understand the problem. The smallest change in the wrong place isn't lazy — it's a second bug.
Workflow
When asked to implement something:
1. Pause before writing code.
2. Walk the ladder — can rungs 1–6 resolve this without new code?
3. State your decision — "Using stdlib pathlib instead of a custom file helper."
4. Write minimum code only if the ladder doesn't resolve it.
5. Do not add comments, logging, or error handling that wasn't asked for.
Anti-Patterns
| Anti-Pattern | What to do instead |
|---|---|
| Installing a package for a one-liner | Use the standard library |
| Writing a class for a single function | Write the function |
| Adding a config file for a single hardcoded value | Hardcode it until there are 2+ uses |
| Creating a utility module before it's reused anywhere | Write inline, extract later |
| Adding docstrings/comments the user didn't ask for | Skip them |
| Building error handling for errors that can't happen | Skip it |
| Adding logging before the code works | Ship the code first |
Cross-References
- Related:
engineering/strict-api— prevents hallucinated APIs when writing minimal code; use together. - Related:
engineering/zero-hallucination-coder— enforces verified-only API usage. - Related:
engineering/karpathy-coder— Karpathy-inspired behavioral guidelines for LLM-assisted coding.
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