1k-code-quality

Code quality standards for OneKey. Use when fixing lint warnings, running pre-commit tasks, handling unused variables, writing comments, or ensuring code quality. All comments must be in English. Triggers on lint, linting, eslint, oxlint, tsc, type check, unused variable, comment, documentation, spellcheck, code quality, pre-commit, yarn lint.

16 stars

Best use case

1k-code-quality is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Code quality standards for OneKey. Use when fixing lint warnings, running pre-commit tasks, handling unused variables, writing comments, or ensuring code quality. All comments must be in English. Triggers on lint, linting, eslint, oxlint, tsc, type check, unused variable, comment, documentation, spellcheck, code quality, pre-commit, yarn lint.

Teams using 1k-code-quality should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/1k-code-quality/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/communication/1k-code-quality/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/1k-code-quality/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How 1k-code-quality Compares

Feature / Agent1k-code-qualityStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Code quality standards for OneKey. Use when fixing lint warnings, running pre-commit tasks, handling unused variables, writing comments, or ensuring code quality. All comments must be in English. Triggers on lint, linting, eslint, oxlint, tsc, type check, unused variable, comment, documentation, spellcheck, code quality, pre-commit, yarn lint.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Code Quality

Linting, documentation, and general code quality standards for OneKey.

## Lint Commands

```bash
# Pre-commit (fast, only staged files)
yarn lint:staged
yarn tsc:staged

# CI only (full project check)
yarn lint        # Comprehensive: TypeScript, ESLint, folder structure, i18n
yarn lint:only   # Quick: oxlint only
yarn tsc:only    # Full type check
```

**Note:** `yarn lint` is for CI only. For pre-commit, always use `yarn lint:staged`.

## Pre-Commit Workflow

For fast pre-commit validation:
```bash
# Lint only modified files (recommended)
yarn lint:staged

# Or with type check
yarn lint:staged && yarn tsc:staged
```

## Common Lint Fixes

```typescript
// Unused variable - prefix with underscore
const { used, unused } = obj;     // ❌ Error: 'unused' is defined but never used
const { used, unused: _unused } = obj;  // ✅ OK

// Unused parameter - prefix with underscore
function foo(used: string, unused: number) {}     // ❌ Error
function foo(used: string, _unused: number) {}    // ✅ OK

// Floating promise - add void or await
someAsyncFunction();        // ❌ Error: Promises must be awaited
void someAsyncFunction();   // ✅ OK (fire-and-forget)
await someAsyncFunction();  // ✅ OK (wait for result)
```

## Language Requirements

**All comments must be written in English:**

```typescript
// ✅ GOOD: English comment
// Calculate the total balance including pending transactions

// ❌ BAD: Chinese comment
// 计算总余额,包括待处理的交易

// ✅ GOOD: JSDoc in English
/**
 * Fetches user balance from the blockchain.
 * @param address - The wallet address to query
 * @returns The balance in native token units
 */
async function fetchBalance(address: string): Promise<bigint> {
  // ...
}
```

## When to Comment

```typescript
// ✅ GOOD: Explain non-obvious logic
// Use 1.5x gas limit to account for estimation variance on this chain
const gasLimit = estimatedGas * 1.5n;

// ✅ GOOD: Explain business logic
// Premium users get 50% discount on transaction fees
const fee = isPremium ? baseFee * 0.5 : baseFee;

// ❌ BAD: Obvious comment
// Set the value to 5
const value = 5;
```

## Development Principles

### Single Responsibility
Each function should perform a single, atomic task:

```typescript
// ✅ GOOD: Single responsibility
async function fetchUserBalance(userId: string): Promise<Balance> {
  const user = await getUser(userId);
  return await getBalanceForAddress(user.address);
}

// ❌ BAD: Multiple responsibilities
async function fetchUserBalanceAndUpdateUI(userId: string) {
  const user = await getUser(userId);
  const balance = await getBalanceForAddress(user.address);
  setBalanceState(balance);
  showNotification('Balance updated');
  logAnalytics('balance_fetched');
}
```

### Avoid Over-Abstraction
Don't create helpers for one-time operations:

```typescript
// ❌ BAD: Over-abstracted
const createUserFetcher = (config: Config) => {
  return (userId: string) => {
    return fetchWithConfig(config, `/users/${userId}`);
  };
};
const fetchUser = createUserFetcher(defaultConfig);
const user = await fetchUser(userId);

// ✅ GOOD: Simple and direct
const user = await fetch(`/api/users/${userId}`).then(r => r.json());
```

## Detailed Guides

### Code Quality Standards
See [code-quality.md](references/rules/code-quality.md) for comprehensive guidelines:
- Linting commands and pre-commit workflow
- Comment and documentation standards
- Language requirements (English only)
- Single responsibility principle
- Avoiding over-abstraction
- Consistent naming conventions
- Code quality checklist

### Fixing Lint Warnings
See [fix-lint.md](references/rules/fix-lint.md) for complete lint fix workflow:
- Analyzing lint warnings
- Categorizing lint errors
- Common fix patterns by category
- Spellcheck fixes
- Unused variable/parameter handling
- Automated fix strategies
- Testing after fixes

## Spellcheck

If a technical term triggers spellcheck errors:
```bash
# Check if word exists
grep -i "yourword" development/spellCheckerSkipWords.txt

# Add if not present (ask team lead first)
echo "yourword" >> development/spellCheckerSkipWords.txt
```

## Checklist

### Pre-commit
- [ ] `yarn lint:staged` passes
- [ ] `yarn tsc:staged` passes

### Code Quality
- [ ] All comments are in English
- [ ] No commented-out code committed
- [ ] Functions have single responsibility
- [ ] No unnecessary abstractions
- [ ] Consistent naming conventions

## Related Skills

- `/1k-sentry-analysis` - Sentry error analysis and fixes
- `/1k-test-version` - Test version creation workflow
- `/1k-coding-patterns` - General coding patterns

Related Skills

data-quality-frameworks

16
from diegosouzapw/awesome-omni-skill

Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

51-execute-quality-150

16
from diegosouzapw/awesome-omni-skill

[51] EXECUTE. Commitment to maximum quality work with 150% coverage. Use when you need the highest quality output for critical tasks, complex problems, important decisions, or when standard work isn't enough. Triggers on "maximum quality", "150% mode", "full quality", "critical task", or when you explicitly want AI to work at its best.

ai-content-quality-checker

16
from diegosouzapw/awesome-omni-skill

AI生成コンテンツの総合品質チェックスキル。読みやすさ、正確性、関連性、独自性、SEO、アクセシビリティ、エンゲージメント、文法・スタイルを多角的に評価。

Verification & Quality Assurance

16
from diegosouzapw/awesome-omni-skill

Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.

quality-nonconformance

16
from diegosouzapw/awesome-omni-skill

Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing.

Data Quality Monitoring

16
from diegosouzapw/awesome-omni-skill

Data Quality (DQ) Monitoring is the continuous process of validating data against predefined rules and expectations. In a modern data stack, monitoring must happen at every stage: **Ingestion**, **Tra

Data Quality Checks

16
from diegosouzapw/awesome-omni-skill

Data Quality Checks are automated tests that validate data against predefined rules and expectations. They act as the "unit tests" for data, catching issues before they propagate downstream to analyti

analyzing-response-quality

16
from diegosouzapw/awesome-omni-skill

Expert at analyzing the quality of Claude's responses and outputs. Use when evaluating response completeness, accuracy, clarity, or effectiveness. Auto-invokes during self-reflection or when quality assessment is needed.

agentic-quality-engineering

16
from diegosouzapw/awesome-omni-skill

AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles.

bgo

10
from diegosouzapw/awesome-omni-skill

Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.

Coding & Development

mcp-create-declarative-agent

16
from diegosouzapw/awesome-omni-skill

Skill converted from mcp-create-declarative-agent.prompt.md

MCP Architecture Expert

16
from diegosouzapw/awesome-omni-skill

Design and implement Model Context Protocol servers for standardized AI-to-data integration with resources, tools, prompts, and security best practices