What is the 'Iron Law' in Planning Patterns?

The Iron Law dictates that every step must be a specific, actionable task that takes 2-5 minutes, eliminating vague instructions like 'Add validation' in favor of explicit test-and-implement cycles.

Does it handle risk management?

Yes, it includes a risk assessment table that calculates scores based on probability and impact, ensuring high-risk items are addressed early in the implementation plan.

How does this skill support Test-Driven Development (TDD)?

It automatically structures plans into five-step TDD cycles: write a failing test, verify the failure, implement minimal code, verify the pass, and commit the changes.

Can I use this for existing codebases?

Yes, the skill is designed to understand existing patterns and provide exact file paths and line numbers for modifications, assuming the engineer has zero initial context.

Planning Patterns for Claude Code

Name: Planning Patterns for Claude Code
Author: romiluz13

byromiluz13

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Productividad y Flujo de Trabajo

Generates granular, TDD-driven implementation plans with explicit, executable steps for Claude Code workflows.

Acerca de

Planning Patterns transforms vague development requests into hyper-specific, executable roadmaps using a strict Test-Driven Development (TDD) approach. It enforces an 'Iron Law' of specificity, ensuring every task is bite-sized (2-5 minutes), includes exact file paths, and contains specific test commands with expected outputs. This skill is essential for complex refactors or new feature implementations where architectural consistency and error reduction are paramount, providing a foolproof guide that Claude can follow with zero ambiguity or context-drifting.

Características Principales

Comprehensive risk assessment and mitigation tables
Exact file path and line-number targeting for code modifications
18 GitHub stars
Standardized TDD workflow cycles (Fail-Pass-Refactor-Commit)
Detailed functionality flow mapping for users and systems
Strict 'Iron Law' enforcement (zero vague steps)

Casos de Uso

Standardizing implementation patterns across large or unfamiliar codebases
Ensuring architectural integrity during autonomous AI-driven code generation
Breaking down complex feature requests into manageable, testable units

Acerca de

Características Principales

Comprehensive risk assessment and mitigation tables
Exact file path and line-number targeting for code modifications
18 GitHub stars
Standardized TDD workflow cycles (Fail-Pass-Refactor-Commit)
Detailed functionality flow mapping for users and systems
Strict 'Iron Law' enforcement (zero vague steps)

Casos de Uso

Standardizing implementation patterns across large or unfamiliar codebases
Ensuring architectural integrity during autonomous AI-driven code generation
Breaking down complex feature requests into manageable, testable units