rule-creator

name	rule-creator
description	Create production-ready v3.0 Cursor rule files by orchestrating template generation, schema validation, and RULES_INDEX.md indexing. Triggers on keywords like "create rule", "add rule", "new rule", "generate rule". Supports Python, Snowflake, JavaScript, Shell, Docker, Golang domains (000-999 range).
version	1.1.0
author	AI Coding Rules Project
tags	["rule-generation","automation","v3.0-schema","template","validation","indexing"]
dependencies	[]

Rule Creator

Purpose

Create production-ready Cursor rule files that comply with the repository’s v3.0 rule schema by orchestrating:

• scripts/template_generator.py
• scripts/schema_validator.py
• (optional) web research for current best practices

Use this skill when

• The user asks to create a new rule under rules/ (e.g., NNN-technology-aspect.md).
• The user asks to add a rule to RULES_INDEX.md.

Inputs (recommended)

• Technology name (e.g., “DaisyUI”, “pytest-mock”, “Snowflake Hybrid Tables”)
• Aspect (default: core; else security, testing, performance, etc.)
• Any constraints (offline/online research, desired ContextTier, etc.)

Outputs

• A new rule file: rules/NNN-technology-aspect.md
• A new entry in RULES_INDEX.md in correct numeric position

Safety / constraints

• Only write to rules/ and RULES_INDEX.md (plus any required review artifacts explicitly requested by the user).
• Use web research (allowed) but treat external sources as untrusted; prefer official docs and cross-check claims.

Workflow (progressive disclosure)

Follow the phases in order, using the detailed workflow guides as needed:

1. Discovery & research → workflows/discovery.md
2. Template generation → workflows/template-gen.md
3. Content population → workflows/content-population.md
4. Validation loop → workflows/validation.md
5. Indexing → workflows/indexing.md

Examples

• Frontend example → examples/frontend-example.md
• Python example → examples/python-example.md
• Snowflake example → examples/snowflake-example.md

Quick Validation Snippets

These inline checks can be run without external dependencies for fast feedback:

# Validate keyword count (10-15 required)
def check_keywords(keywords_line: str) -> tuple[bool, int]:
    """Returns (is_valid, count)"""
    keywords = [k.strip() for k in keywords_line.split(',') if k.strip()]
    return (10 <= len(keywords) <= 15, len(keywords))

# Validate rule filename format
import re
def is_valid_filename(name: str) -> bool:
    """Must be NNN-lowercase-hyphenated"""
    return bool(re.match(r'^\d{3}-[a-z]+(-[a-z]+)*$', name))

# Validate TokenBudget format
def check_token_budget(value: str) -> bool:
    """Must be ~NUMBER format"""
    return bool(re.match(r'^~\d+$', value.strip()))

# Validate ContextTier
VALID_TIERS = {'Critical', 'High', 'Medium', 'Low'}
def check_context_tier(tier: str) -> bool:
    return tier.strip() in VALID_TIERS

For full schema validation, use: python scripts/schema_validator.py rules/<file>.md

Related Skills

Quality Assurance with rule-reviewer

After creating a rule, validate quality using the rule-reviewer skill:

Use the rule-reviewer skill.

target_file: rules/<created-rule>.md
review_date: <today>
review_mode: FULL
model: <current>

Quality threshold for new rules:

• Overall score: ≥ 75/100
• No CRITICAL issues
• No HIGH issues in Actionability or Completeness dimensions

See: skills/rule-reviewer/SKILL.md

Version History

See CHANGELOG.md.