name	documentation-as-code
description	README standards, API docs, architecture decision records (ADRs), inline doc conventions. Use when formalizing documentation standards or writing technical docs.

Documentation as Code

Treat documentation with the same rigor, version control, and automation as source code. Document to communicate context, decisions, and usage, not to echo code syntax.

1. The README Standard

Every repository or major sub-module MUST have a README.md addressing:

Title and One-Liner: What this is and who it's for.
Getting Started:
- Exact prerequisites (OS, packages).
- Explicit, copy-pasteable installation steps.
Minimal Example: A fast "Hello World" or equivalent usage snippet that proves the system works.
Architecture Overview: High-level map of where critical components live.
Development Setup: How to run tests and linters.

2. Architecture Decision Records (ADRs)

For significant technical decisions (e.g., "Why did we choose a microservice architecture instead of a monolith?"), do not rely on Slack history or pull request comments. Create an ADR in a docs/architecture/decisions/ directory.

ADR Format snippet:

Title: [Short description of decision]
Status: [Proposed | Accepted | Superseded]
Context: [What forced this decision? What were the constraints?]
Decision: [What we decided doing.]
Consequences: [Positive and negative ramifications of this choice.]

3. Inline Documentation & Docstrings

Use standard formats (e.g., Google or NumPy style) for all docstrings.
Comments explain WHY, code explains WHAT/HOW.
- Bad: # Loop over workers
- Good: # We shuffle workers here to prevent bias towards IDs evaluated earlier in the loop
If you find yourself writing a huge comment explaining a complex block of code, consider refactoring the code to be clearer or extracting it into a named function.

4. Self-Documenting Data Structures

For configuration blobs or parameter dictionaries, do not document the structure solely in a comment.
Use formal typing, dataclasses, or Pydantic models. The code structure itself becomes the strict documentation for what keys exist and what types they require.

5. Artifact generation

For Python projects, aim for docs that can be automatically extracted using Sphinx, MkDocs, or pdoc.
Keep source markdown files simple. Avoid overly complex HTML tables or styling that hinders readability in raw text.

Documentation as Code

Treat documentation with the same rigor, version control, and automation as source code. Document to communicate context, decisions, and usage, not to echo code syntax.

1. The README Standard

Every repository or major sub-module MUST have a README.md addressing:

Title and One-Liner: What this is and who it's for.

Getting Started:

Exact prerequisites (OS, packages).
Explicit, copy-pasteable installation steps.

Minimal Example: A fast "Hello World" or equivalent usage snippet that proves the system works.

Architecture Overview: High-level map of where critical components live.

Development Setup: How to run tests and linters.

2. Architecture Decision Records (ADRs)

ADR Format snippet:

Title: [Short description of decision]

Status: [Proposed | Accepted | Superseded]

Context: [What forced this decision? What were the constraints?]

Decision: [What we decided doing.]

Consequences: [Positive and negative ramifications of this choice.]

3. Inline Documentation & Docstrings

Use standard formats (e.g., Google or NumPy style) for all docstrings.

Comments explain WHY, code explains WHAT/HOW.

Bad: # Loop over workers
Good: # We shuffle workers here to prevent bias towards IDs evaluated earlier in the loop

If you find yourself writing a huge comment explaining a complex block of code, consider refactoring the code to be clearer or extracting it into a named function.

4. Self-Documenting Data Structures

For configuration blobs or parameter dictionaries, do not document the structure solely in a comment.

Use formal typing, dataclasses, or Pydantic models. The code structure itself becomes the strict documentation for what keys exist and what types they require.

5. Artifact generation

For Python projects, aim for docs that can be automatically extracted using Sphinx, MkDocs, or pdoc.

Keep source markdown files simple. Avoid overly complex HTML tables or styling that hinders readability in raw text.

documentation-as-code