documentation-quality-metrics

Documentation Quality Metrics

Use this skill to evaluate developer documentation quality and choose metrics that answer real questions. It combines editorial quality with analytics and user outcomes.

This skill is derived from Docs for Developers: An Engineer's Field Guide to Technical Writing, especially Chapter 9, "Measuring documentation quality." It is expanded with paraphrased guidance from Christopher Gales and the Splunk Documentation Team's The Product Is Docs: Writing Technical Documentation in a Product Development Group, especially Chapter 11, "Measuring Success." Do not copy book prose into user outputs. Source: https://link.springer.com/book/10.1007/978-1-4842-7217-6

Quick Start

1. Load guidelines.md to choose the smallest useful reference set.
2. Identify the decision the measurement should support.
3. Decide how much uncertainty the team needs to reduce before acting.
4. Audit functional quality before structural polish.
5. Use workflows/audit-doc-quality.md for a full quality or metric plan.
6. Prefer metric clusters with baseline and context over isolated numbers.

Default Output

When auditing quality or designing metrics, return:

1. Measurement question - what decision the metric or audit supports.
2. Uncertainty to reduce - what the team does not know yet and how precise the answer needs to be.
3. Functional quality findings - accessibility, purpose, findability, accuracy, completeness.
4. Structural quality findings - clarity, concision, consistency.
5. Metric plan - qualitative and quantitative signals, baselines, owners, and limits.
6. Recommended actions - fixes, experiments, or data collection.
7. Caveats - what the available metrics cannot prove.

Contents

Need	Start Here
Understand quality dimensions	references/core/knowledge.md
Apply audit and metric rules	references/core/knowledge.md
See metric examples	references/core/knowledge.md
Run quality audit	workflows/audit-doc-quality.md
Route by task	guidelines.md

Core Posture

• Good docs fulfill their purpose for users.
• Functional quality matters before stylistic polish.
• Metrics reduce uncertainty for a decision; counting is not measurement by itself.
• Combine analytics with qualitative evidence; neither tells the full story alone.