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progressive-disclosure

@gptme/gptme-contrib
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Template and guide for restructuring large documentation files into token-efficient directory structures. Reduces context bloat by 40-60% while maintaining accessibility.

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1Download skill
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3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name progressive-disclosure
description Template and guide for restructuring large documentation files into token-efficient directory structures. Reduces context bloat by 40-60% while maintaining accessibility.

Progressive Disclosure for Documentation

A pattern for restructuring large documentation files into slim indexes with on-demand subdirectories, reducing always-included context by 40-60%.

Problem

Large documentation files (500+ lines, 5k+ tokens) that are always included in context:

  • Waste context budget on rarely-needed content
  • Slow down response times
  • Crowd out more relevant information
  • Create cognitive overload

Solution

Split monolithic docs into:

  1. Slim index (~20% of original) - Always included, provides overview + navigation
  2. Detail directories - On-demand loading when specific topics needed

Structure

Before:
  TOOLS.md (~11k tokens always included)
  ├── Section 1 (rarely needed)
  ├── Section 2 (rarely needed)
  ├── ... (15+ sections)
  └── Section N (rarely needed)

After:
  tools/
  ├── README.md (~4k tokens always included)  # Slim index
  ├── topic1/README.md   # Detailed docs (~1k tokens each)
  ├── topic2/README.md   # Loaded on-demand
  └── .../README.md      # etc.

When to Use

Good candidates for progressive disclosure:

  • Files > 500 lines or 5k tokens
  • Files always included in context (via gptme.toml or similar)
  • Files with distinct sections that are rarely needed together
  • Documentation with both overview and detailed reference content

Keep as single file when:

  • File is < 200 lines
  • Content is frequently needed together
  • Structure is already lean
  • Users commonly need complete view

Implementation Guide

Step 1: Analyze Current Structure

Count tokens and identify sections:

# Count lines and estimate tokens
wc -l LARGE_FILE.md
# Rough: 1 line ≈ 8 tokens

# Identify sections (look for ## headers)
grep "^## " LARGE_FILE.md | head -20

Step 2: Create Directory Structure

# Create topic directories
mkdir -p topic_dir/{topic1,topic2,topic3}

Step 3: Write Slim Index (README.md)

The index should contain:

  • Brief overview (2-3 paragraphs max)
  • Quick reference/cheatsheet (most common operations)
  • Navigation links to detailed docs
  • When to read which section

Example slim index structure:

# Topic Name

Brief overview of what this covers and core concepts.

## Quick Reference

| Command | Description |
|---------|-------------|
| cmd1    | Most common operation |
| cmd2    | Second most common |
| cmd3    | Third most common |

## Detailed Documentation

For specific topics, see:

- **Topic 1** (topic1/README.md) - When you need X
- **Topic 2** (topic2/README.md) - When you need Y
- **Topic 3** (topic3/README.md) - When you need Z

## Principles

1. Core principle 1
2. Core principle 2
3. Core principle 3

Step 4: Create Detail Files

Each topic README should:

  • Be self-contained for its topic
  • Include examples and use cases
  • Cross-reference related topics
  • Stay under 200 lines (1.5k tokens)

Step 5: Update Configuration

Update gptme.toml (or equivalent) to include the slim index:

[context]
# Before: included entire large file
# files = ["LARGE_FILE.md"]

# After: include only slim index
files = ["topic_dir/README.md"]

Example: TOOLS.md Migration

Before:

  • TOOLS.md: 1380 lines, ~11k tokens, always included
  • 15+ sections covering shell, git, github, context, etc.

After:

  • tools/README.md: 200 lines, ~1.5k tokens (slim index)
  • tools/{shell,git,github,context,...}/README.md: ~150 lines each
  • Agent reads detailed docs only when working on specific topics

Result: 44% reduction in always-included context

Benefits

  1. Token Efficiency: 40-60% reduction in always-included tokens
  2. Faster Responses: Less context to process
  3. Better Focus: Agent sees relevant content when needed
  4. Maintainability: Smaller files easier to update
  5. Claude-Style Organization: Similar to Anthropic's skill folders

Anti-patterns to Avoid

  1. Too many small files: Don't split into 50+ tiny files
  2. Deep nesting: Keep to 2 levels max (index + details)
  3. Orphan content: Ensure all detail files linked from index
  4. Missing quick reference: Index must have actionable content
  5. Over-splitting: Some content should stay together

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