What is a 'bead' in this planning pipeline?

A bead is a granular, executable work item that represents a specific unit of work. This skill decomposes large features into these individual beads to make implementation more manageable and traceable.

Does this skill help prevent merge conflicts?

Yes. It mandates a worktree setup and generates parallel execution tracks with non-overlapping file scopes, ensuring that different parts of a feature can be developed simultaneously without clashing.

How does the skill handle technical uncertainty?

It uses a risk classification system. High-risk items trigger the creation of 'spikes'—time-boxed research tasks or prototype code used to verify an approach before it is added to the final implementation plan.

How does the 'Oracle' component work within this skill?

The Oracle acts as a synthesis engine that analyzes discovery reports and gap analyses to recommend specific technical approaches and identify potential architectural risks.

Feature Planning Pipeline

Name: Feature Planning Pipeline
Author: draphonix

bydraphonix

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Productivity & Workflow

Architects comprehensive software implementation plans through systematic codebase discovery, risk-based verification, and task decomposition.

About

The planning skill transforms high-level feature requests into executable technical roadmaps within the Claude Code environment. It employs a multi-phase pipeline that includes deep codebase discovery to identify existing patterns, risk assessment to isolate technical uncertainties, and the creation of 'spikes' for rapid verification. By decomposing complex requirements into granular 'beads' and organizing them into parallel execution tracks, this skill ensures that large-scale features are implemented with architectural integrity, minimal merge conflicts, and clear documentation of technical constraints.

Key Features

Parallel codebase discovery and architecture mapping
Automated dependency validation and cycle detection
Granular task decomposition using the file-beads methodology
Multi-track execution planning for parallel agent workflows
49 GitHub stars
Risk-based verification via automated technical spikes

Use Cases

Validating technical feasibility of novel integrations before implementation
Designing complex new features within large, unfamiliar codebases
Creating multi-track execution plans for team-based or multi-agent development

About

Key Features

Parallel codebase discovery and architecture mapping
Automated dependency validation and cycle detection
Granular task decomposition using the file-beads methodology
Multi-track execution planning for parallel agent workflows
49 GitHub stars
Risk-based verification via automated technical spikes

Use Cases

Validating technical feasibility of novel integrations before implementation
Designing complex new features within large, unfamiliar codebases
Creating multi-track execution plans for team-based or multi-agent development