shard

Split large documents into atomic shards for efficient context management.

1|Updated Feb 19, 2026
One-click install
npx skills add https://github.com/alessioroberto82/claude-plugin-bmad --skill shard
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: shard
Source: https://github.com/alessioroberto82/claude-plugin-bmad/tree/main/plugin/skills/shard
Command: npx skills add https://github.com/alessioroberto82/claude-plugin-bmad --skill shard

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill tackles the issue of large documents consuming excessive tokens, making AI interactions slow and expensive, by breaking them down into smaller, manageable pieces.

Core Features & Use Cases

  • Context Sharding: Divides large documents (like PRDs or architecture docs) into atomic files (shards).
  • Token Reduction: Dramatically cuts token usage (up to 90%) by loading only relevant shards per task.
  • Use Case: When an AI role needs to work on a specific user story from a massive Product Requirements Document, this Skill ensures only that story's shard is loaded, not the entire document.

Quick Start

Use the shard skill to break down the document located at ~/.claude/circle/projects/myproject/output/prioritize/PRD-final.md into smaller shards.

Frequently Asked Questions about shard

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

FAQPage Schema
How do I reduce token consumption when processing large Product Requirements Documents in AI interactions?

Context sharding splits large documents like architecture specifications into atomic files. This technique manages context by generating distinct shards for requirements, architecture decisions, and user stories following a defined naming convention.

How do I split an architecture specification into smaller files for AI context management?

Context sharding splits large documents like architecture specifications into atomic files. This technique manages context by generating distinct shards for requirements, architecture decisions, and user stories following a defined naming convention.

How do I extract a specific user story from a massive PRD without loading the entire document?

To extract a user story without loading the entire PRD, run the shard skill against your document path. It automatically detects input documents from specified project output directories and breaks them down into manageable pieces.

Can I automatically detect and process markdown documents from my project output directories?

Yes, automatic document detection is supported from specified project output directories. You simply point the sharding process to a path like your project's output folder, and it generates distinct shards automatically.

What is the best way to save tokens when feeding large architecture docs to AI roles?

The best way to save tokens with large architecture docs is context sharding, which divides them into atomic files. This enables AI roles to load only relevant sections, achieving up to 90% token reduction.

Does sharding large documents impact the naming convention of generated requirements and user stories?

Sharding large documents does not randomize outputs; it generates distinct shards for requirements, architecture decisions, and user stories by following a defined naming convention, ensuring organized and traceable file management.