writing-plans

Generates detailed multi-step implementation plans with TDD tasks from feature specifications.

Updated Mar 31, 2026
One-click install
npx skills add https://github.com/SESELOVSKYDarian/Vase --skill writing-plans-seselovskydarian
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: writing-plans
Source: https://github.com/SESELOVSKYDarian/Vase/tree/main/.agents/skills/writing-plans
Command: npx skills add https://github.com/SESELOVSKYDarian/Vase --skill writing-plans-seselovskydarian

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Turning a feature spec into code without a plan leads to missed requirements, vague tasks, and inconsistent execution. This Skill converts a specification into a complete, bite-sized implementation plan that an engineer with zero codebase context can follow task by task. ## Core Features & Use Cases - Bite-Sized TDD Tasks: Breaks work into 2-5 minute steps following a write-failing-test, implement, verify, commit cycle. - No-Placeholder Enforcement: Requires exact file paths, complete code blocks, and exact commands with expected output in every step. - Self-Review Checklist: Validates spec coverage, scans for placeholder content, and checks type consistency across tasks before handoff. - Use Case: After brainstorming a new API endpoint spec, use this Skill to produce a plan document saved to docs/superpowers/plans/ with checkbox-tracked tasks ready for subagent-driven or inline execution. ## Quick Start Ask the AI to use the writing-plans skill to create an implementation plan from your feature specification document.

Frequently Asked Questions about writing-plans

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write an implementation plan from a feature spec?

Provide the spec and invoke the writing-plans skill before touching code. It maps the file structure, decomposes work into bite-sized TDD tasks with exact paths and code, and saves the plan to docs/superpowers/plans/ with a dated filename.

What makes a good implementation plan for AI coding agents?

A good plan assumes zero codebase context: exact file paths, complete code in every step, exact commands with expected output, and no placeholders like TODO or TBD. Each task should be self-contained and independently testable.

How granular should tasks be in an implementation plan?

Each step should be one action taking 2-5 minutes: write the failing test, run it to confirm failure, implement minimal code, run tests to confirm pass, then commit. This granularity keeps execution reliable and reviewable.

When should a spec be split into multiple implementation plans?

Split when the spec covers multiple independent subsystems. Each plan should produce working, testable software on its own, so one plan per subsystem keeps scope focused and execution verifiable.

What are the limitations of plan-driven development?

Plans assume the spec is complete and correct; gaps in the spec propagate into the plan. The self-review step catches coverage and consistency issues, but ambiguous requirements still need clarification during brainstorming first.