boundary-bbcr-fallback
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Setup code linting and quality tools
$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
/setup-lintingContext preview
What this command does when you run it.
Setup code linting and quality tools
Setup code linting and quality tools
Follow this systematic approach to setup linting: **$ARGUMENTS**
1. **Project Analysis**
2. **Tool Selection by Language**
**JavaScript/TypeScript:**
npm install -D eslint @typescript-eslint/parser @typescript-eslint/eslint-plugin npm install -D prettier eslint-config-prettier eslint-plugin-prettier
**Python:**
pip install flake8 black isort mypy pylint
**Java:**
# Add to pom.xml or build.gradle # Checkstyle, SpotBugs, PMD
3. **Configuration Setup**
**ESLint (.eslintrc.json):**
{
"extends": [
"eslint:recommended",
"@typescript-eslint/recommended",
"prettier"
],
"parser": "@typescript-eslint/parser",
"plugins": ["@typescript-eslint"],
"rules": {
"no-console": "warn",
"no-unused-vars": "error",
"@typescript-eslint/no-explicit-any": "warn"
}
}4. **IDE Integration**
5. **CI/CD Integration**
- name: Lint code
run: npm run lint6. **Package.json Scripts**
{
"scripts": {
"lint": "eslint src --ext .ts,.tsx",
"lint:fix": "eslint src --ext .ts,.tsx --fix",
"format": "prettier --write src"
}
}Remember to customize rules based on team preferences and gradually enforce stricter standards.
A comprehensive development toolkit designed following Anthropic's Claude Code Best Practices for AI-assisted software development.
Repo: qdhenry/Claude-Command-Suite
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
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Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles:…