enhance-docs
Optimizes documentation for both human readability and AI retrieval (RAG readiness) through structural improvements.
Install
mkdir -p .claude/skills/enhance-docs-christophacham && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/16365" && unzip -o skill.zip -d .claude/skills/enhance-docs-christophacham && rm skill.zipInstalls to .claude/skills/enhance-docs-christophacham
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Use when improving documentation structure, accuracy, and RAG readiness.Key capabilities
- →Analyze documentation for readability
- →Validate link integrity
- →Check heading hierarchy and structure
- →Optimize content for token reduction
- →Improve RAG readiness with chunking guidelines
- →Apply auto-fixes for identified issues
How it works
The skill discovers and parses Markdown files, runs pattern checks for links, structure, token efficiency, and RAG optimization, then generates a report and applies auto-fixes if requested.
Inputs & outputs
When to use enhance-docs
- →Optimizing docs for AI agents
- →Fixing broken links in READMEs
- →Structuring documentation for RAG
About this skill
enhance-docs
Analyze documentation for readability, structure, and RAG optimization.
Parse Arguments
const args = '$ARGUMENTS'.split(' ').filter(Boolean);
const targetPath = args.find(a => !a.startsWith('--')) || '.';
const fix = args.includes('--fix');
const aiMode = args.includes('--ai');
Documentation Locations
| Type | Location | Purpose |
|---|---|---|
| User docs | docs/*.md, README.md | Human-readable guides |
| Agent docs | agent-docs/*.md | AI reference material |
| Project memory | CLAUDE.md, AGENTS.md | AI context/instructions |
Optimization Modes
AI-Only Mode (--ai)
For agent-docs and RAG-optimized documentation:
- Aggressive token reduction
- Dense information packing
- Self-contained sections for retrieval
- Optimal chunking boundaries
Both Mode (--both, default)
For user-facing documentation:
- Balance readability with AI-friendliness
- Clear structure for both humans and retrievers
Workflow
- Discover - Find all .md files
- Parse - Extract structure and content
- Check - Run pattern checks based on mode
- Report - Generate markdown output
- Fix - Apply auto-fixes if --fix
Detection Patterns
1. Link Validation (HIGH)
- Broken anchor links (
[text](#missing-anchor)) - Links to non-existent files
- Malformed link syntax
2. Structure Validation (HIGH)
Heading hierarchy:
- No jumps (H1 → H3 without H2)
- Single H1 per document
- Code blocks with language tags
Position-aware content (based on "lost in the middle" research):
- Critical info at START or END of document
- Supporting details in MIDDLE
- Flag important content buried in middle sections
Recommended structure:
1. Overview/Purpose (START - high attention)
2. Quick Start / TL;DR
3. Detailed Content
4. Reference / API
5. Summary / Key Points (END - high attention)
3. Token Efficiency (HIGH - AI Mode)
Token estimation: characters / 4 or words * 1.3
Unnecessary prose:
- "In this document..."
- "As you can see..."
- "Let's explore..."
- "It's important to note that..."
Verbose phrases:
| Verbose | Concise |
|---|---|
| "in order to" | "to" |
| "due to the fact that" | "because" |
| "has the ability to" | "can" |
| "at this point in time" | "now" |
| "for the purpose of" | "for" |
| "in the event that" | "if" |
Target: ~1500 tokens for project memory files, flexible for reference docs.
4. RAG Optimization (MEDIUM - AI Mode)
Chunk size guidelines:
| Size | Issue |
|---|---|
| >1000 tokens | Too long, split into subtopics |
| <50 tokens | Too short, merge with related content |
| 200-500 tokens | Optimal for retrieval |
Semantic boundaries:
- Single topic per section
- Self-contained sections (avoid "It", "This" at section start)
- Clear section titles that describe content
Context anchors:
# Bad - ambiguous start
## Configuration
It requires several settings...
# Good - self-contained
## Configuration
The plugin configuration requires several settings...
5. Information Density (MEDIUM - AI Mode)
Prefer tables over prose:
# Bad - verbose
The function accepts a path parameter which is required,
a limit parameter which defaults to 10, and an optional
format parameter.
# Good - dense
| Param | Required | Default | Description |
|-------|----------|---------|-------------|
| path | Yes | - | File path |
| limit | No | 10 | Max results |
| format | No | json | Output format |
Prefer lists over paragraphs for sequential items.
Use code blocks for examples, commands, configurations.
6. Cross-Reference Quality (MEDIUM)
- Internal links should use relative paths
- External links should be stable (avoid commit hashes)
- Reference sections should point to canonical sources
7. Balance Suggestions (MEDIUM - Both Mode)
- Missing section headers in long content (>500 words without heading)
- Important information buried late in document
- Missing TL;DR or summary for long documents
Auto-Fixes
| Issue | Fix |
|---|---|
| Inconsistent headings | H1 → H3 becomes H1 → H2 |
| Verbose phrases | Replace with concise alternatives |
| Missing code language | Add based on content detection |
Output Format
## Documentation Analysis: {name}
**File**: {path}
**Mode**: {AI-only | Both}
**Tokens**: ~{count}
| Certainty | Count |
|-----------|-------|
| HIGH | {n} |
| MEDIUM | {n} |
### Link Issues
| Line | Issue | Fix | Certainty |
### Structure Issues
| Line | Issue | Fix | Certainty |
### Efficiency Issues [AI mode]
| Line | Issue | Fix | Certainty |
### RAG Issues [AI mode]
| Line | Issue | Fix | Certainty |
Pattern Statistics
| Category | Patterns | Mode | Certainty |
|---|---|---|---|
| Links | 3 | shared | HIGH |
| Structure | 4 | shared | HIGH |
| Token Efficiency | 3 | ai | HIGH |
| RAG Optimization | 3 | ai | MEDIUM |
| Information Density | 2 | ai | MEDIUM |
| Cross-Reference | 2 | shared | MEDIUM |
| Balance | 3 | both | MEDIUM |
| Total | 20 | - | - |
RAG Chunking
<bad_example>
## Installation
[2000+ tokens of mixed content covering install, config, and usage]
</bad_example> <good_example>
## Installation
[400 tokens - installation only]
## Configuration
[300 tokens - config only]
## Usage
[400 tokens - usage only]
</good_example>
Position-Aware Content
<bad_example>
## Introduction
[Long background...]
## History
[More context...]
## Critical Setup Steps
[Important info buried in middle]
</bad_example> <good_example>
## Quick Start (Critical)
[Important setup steps at START]
## Background
[Supporting context in middle]
## Reference
[Details...]
## Key Reminders
[Critical points repeated at END]
</good_example>
Tables vs Prose
<bad_example>
The API accepts three parameters. The first is `query` which is required.
The second is `limit` which defaults to 10. The third is `format`.
</bad_example> <good_example>
| Param | Required | Default |
|-------|----------|---------|
| query | Yes | - |
| limit | No | 10 |
| format | No | json |
</good_example> </examples>
References
agent-docs/CONTEXT-OPTIMIZATION-REFERENCE.md- Token budgeting, position awareness, chunkingagent-docs/PROMPT-ENGINEERING-REFERENCE.md- Structure, information density
Constraints
- Auto-fix only HIGH certainty issues
- Preserve original tone and style
- Balance AI optimization with human readability (default mode)
- Don't remove content, only restructure or condense
Limitations
- →Auto-fix only HIGH certainty issues
- →Preserve original tone and style
- →Balance AI optimization with human readability (default mode)
How it compares
This skill specifically optimizes documentation for AI retrieval and token efficiency while balancing human readability, unlike general documentation linters.
Compared to similar skills
enhance-docs side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| enhance-docs (this skill) | 0 | 5mo | No flags | Intermediate |
| postmortem-writing | 17 | 2mo | No flags | Beginner |
| wiki-page-writer | 5 | 3mo | No flags | Advanced |
| confluence-assistant | 1 | 5mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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