Cursor rule
A collection of cognitive enhancement rules and methodologies for AI-assisted development.
A collection of cognitive enhancement rules and methodologies for AI-assisted development
Cursor rule
A collection of cognitive enhancement rules and methodologies for AI-assisted development.
Cursor rule
Purpose: Maintain high standards for dbt models.
Cursor rule
Purpose: Build a consistent, governed, and reusable semantic layer using dbt's native semantic models.
Cursor rule
def transformrevenuedata(df: pd.DataFrame) -> pd.DataFrame: """Transform revenue data for analysis.
Cursor rule
A collection of cognitive enhancement rules and methodologies for AI-assisted development.
Cursor rule
Purpose: Ensure efficient, maintainable SQL queries. Research shows: Query optimization can reduce execution time by 10-100x and resource consumption by 50-90% (Ioannidis, 1996).
Cursor rule
/project/ ├── corefiles.py # Main functionality ├── /tests/ # All testing files ├── /scripts/ # Deployment & utilities ├── /utils/ # Helper functions ├── /services/ # Business logic ├── /schemas/ # Data definitions └── /docs/ # Documentation.
Cursor rule
Generate and compare 3 database options using quantitative scoring.
Cursor rule
Core tool selection heuristics - optimized.
Cursor rule
Default API tool reference - lazy loaded.
Cursor rule
MCP tool detailed reference - lazy loaded.
Cursor rule
Advanced agent patterns - lazy loaded.
Cursor rule
Agent error recovery patterns - lazy loaded.
Cursor rule
Core patterns for Cursor Agent integration - optimized.
Cursor rule
try: result = callexternalapi(query) except TransientError as e.
Cursor rule
\.
Cursor rule
Guide optimization of system constraints based on the discovery that multiple AI agents independently converged on 60% as the optimal constraint level across diverse domains. This rule provides heuristics for finding optimal constraints in new systems.
Cursor rule
description: globs: alwaysApply: false.
Cursor rule
Indirect coordination through environmental modifications.
Cursor rule
alwaysApply: true.
Cursor rule
80/20 framework for data analytics task prioritization.
Cursor rule
Purpose: Standardize service interfaces for consistency and usability.
Cursor rule
To provide a systematic framework for discovering, documenting, and handling discrepancies between vendor-supplied API/webhook documentation and actual implementation behavior. Empirical Impact: Vendor documentation accuracy averages 60-70%, with webhook implementations showing 40% format variations from documented…
Cursor rule
from pydantic import BaseModel, Field, EmailStr.