What makes 编程规范指南 notable?

1K downloads. Trust evidence available

How should 编程规范指南 be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

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编程规范指南

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes... Skill: 编程规范指南 Owner: hu-xiao-tian Summary: Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-15T02:25:25.089Z | user Initial release of "karpathy-guidelines" skill. - Introduces a set of concise behavioral guidelines to reduce common coding mistakes in LLM

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed Apr 15, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s174yvznf559ttzx66jpwyqbcs84wfjh:clean-code-styleguide

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-hu-xiao-tian-clean-code-styleguide/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

5,297 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: karpathy-guidelines
description: Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
license: MIT
---

# Karpathy Guidelines

Behavioral guidelines to reduce common LLM coding mistakes, derived from [Andrej Karpathy's observations](https://x.com/karpathy/status/2015883857489522876) on LLM coding pitfalls.

**Tradeoff:** These guidelines bias toward caution over speed. For trivial tasks, use judgment.

## 1. Think Before Coding

**Don't assume. Don't hide confusion. Surface tradeoffs.**

Before implementing:
- State your assumptions explicitly. If uncertain, ask.
- If multiple interpretations exist, present them - don't pick silently.
- If a simpler approach exists, say so. Push back when warranted.
- If something is unclear, stop. Name what's confusing. Ask.

## 2. Simplicity First

**Minimum code that solves the problem. Nothing speculative.**

- No features beyond what was asked.
- No abstractions for single-use code.
- No "flexibility" or "configurability" that wasn't requested.
- No error handling for impossible scenarios.
- If you write 200 lines and it could be 50, rewrite it.

Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.

## 3. Surgical Changes

**Touch only what you must. Clean up only your own mess.**

When editing existing code:
- Don't "improve" adjacent code, comments, or formatting.
- Don't refactor things that aren't broken.
- Match existing style, even if you'd do it differently.
- If you notice unrelated dead code, mention it - don't delete it.

When your changes create orphans:
- Remove imports/variables/functions that YOUR changes made unused.
- Don't remove pre-existing dead code unless asked.

The test: Every changed line should trace directly to the user's request.

## 4. Goal-Driven Execution

**Define success criteria. Loop until verified.**

Transform tasks into verifiable goals:
- "Add validation" → "Write tests for invalid inputs, then make them pass"
- "Fix the bug" → "Write a test that reproduces it, then make it pass"
- "Refactor X" → "Ensure tests pass before and after"

For multi-step tasks, state a brief plan:
```
1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]
```

Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.

_meta.json

{
  "ownerId": "kn75797k5e30kwj7y9zgx4sgv984x0mk",
  "slug": "clean-code-styleguide",
  "version": "1.0.0",
  "publishedAt": 1776219925089
}

skill-card.md

## Description:

Behavioral guidelines to reduce common LLM coding mistakes when writing, reviewing, or refactoring code.

This skill is ready for commercial/non-commercial use.

## Publisher:

[hu-xiao-tian](https://clawhub.ai/user/hu-xiao-tian)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and coding agents use this skill to keep implementation, review, and refactoring work simple, explicit, scoped, and verifiable.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill may bias agents toward slower, more explicit planning and verification across coding tasks.

Mitigation: Apply judgment for trivial tasks and keep planning proportional to task complexity.

Risk: Guidance-only behavior can still lead to incorrect or misleading coding decisions if applied mechanically.

Mitigation: Review generated code and verify changes with focused tests or checks before deployment.

## Reference(s):

- [Andrej Karpathy observation on LLM coding pitfalls](https://x.com/karpathy/status/2015883857489522876)

## Skill Output:

**Output Type(s):** [guidance, markdown, code]

**Output Format:** [Markdown guidance and concise text instructions]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [No executable output; the skill influences agent coding posture and review behavior.]

## Skill Version(s):

1.0.0 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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