ADR Writing

ADR Writing

Overview

Generate Architectural Decision Records (ADRs) following the MADR template with systematic completeness checking.

Quick Reference

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│  SEQUENCE   │ ──▶ │   EXPLORE    │ ──▶ │    FILL     │
│  (get next  │     │  (context,   │     │  (template  │
│   number)   │     │   ADRs)      │     │   sections) │
└─────────────┘     └──────────────┘     └─────────────┘
       │                                        │
       │                                        ▼
       │                                 ┌─────────────┐
       │                                 │   VERIFY    │
       │                                 │  (DoD       │
       └─────────────────────────────────│   checklist)│
                                         └─────────────┘

When To Use

• Documenting architectural decisions from extracted requirements
• Converting meeting notes or discussions to formal ADRs
• Recording technical choices from PR discussions
• Creating decision records from design documents

Workflow

Step 1: Get Sequence Number

If a number was pre-assigned (e.g., when called from /​beagle:write-adr with parallel writes):

• Use the pre-assigned number directly
• Do NOT call the script - this prevents duplicate numbers in parallel execution

If no number was pre-assigned (standalone use):

python scripts/​next_adr_number.py

This outputs the next available ADR number (e.g., 0003).

For parallel allocation (used by parent commands):

python scripts/​next_adr_number.py --count 3
# Outputs: 0003, 0004, 0005 (one per line)

Step 2: Explore Context

Before writing, gather additional context:

1. Related code - Find implementations affected by this decision
2. Existing ADRs - Check docs/​adrs/ for related or superseded decisions
3. Discussion sources - PRs, issues, or documents referenced in decision

Step 3: Load Template

Load references/​madr-template.md for the official MADR structure.

Step 4: Fill Sections

Populate each section from your decision data:

Step 5: Apply Definition of Done

Load references/​definition-of-done.md and verify E.C.A.D.R. criteria:

• Explicit problem statement
• Comprehensive options analysis
• Actionable decision
• Documented consequences
• Reviewable by stakeholders

Step 6: Mark Gaps

For sections that cannot be filled from available data, insert investigation prompts:

* [INVESTIGATE: Review PR #42 discussion for additional drivers]
* [INVESTIGATE: Confirm with security team on compliance requirements]
* [INVESTIGATE: Benchmark performance of Option 2 vs Option 3]

These prompts signal incomplete sections for later follow-up.

Step 7: Write File

IMPORTANT: Every ADR MUST start with YAML frontmatter.

The frontmatter block is REQUIRED and must include at minimum:

---
status: draft
date: YYYY-MM-DD
---

Full frontmatter template:

---
status: draft
date: 2024-01-15
decision-makers: [alice, bob]
consulted: []
informed: []
---

Validation: Before writing the file, verify the content starts with --- followed by valid YAML frontmatter. If frontmatter is missing, add it before writing.

Save to docs/​adrs/​NNNN-slugified-title.md:

docs/​adrs/​0003-use-postgresql-for-user-data.md
docs/​adrs/​0004-adopt-event-sourcing-pattern.md
docs/​adrs/​0005-migrate-to-kubernetes.md

Step 8: Verify Frontmatter

After writing, confirm the file:

1. Starts with --- on the first line
2. Contains status: draft (or other valid status)
3. Contains date: YYYY-MM-DD with actual date
4. Ends frontmatter with --- before the title

File Naming Convention

Format: NNNN-slugified-title.md

Reference Files

• references/​madr-template.md - Official MADR template structure
• references/​definition-of-done.md - E.C.A.D.R. quality criteria

Output Example

---
status: draft
date: 2024-01-15
decision-makers: [alice, bob]
---

# Use PostgreSQL for User Data Storage

## Context and Problem Statement

We need a database for user account data...

## Decision Drivers

* Data integrity requirements
* Query flexibility needs
* [INVESTIGATE: Confirm scaling projections with infrastructure team]

## Considered Options

* PostgreSQL
* MongoDB
* CockroachDB

## Decision Outcome

Chosen option: PostgreSQL, because...

## Consequences

### Good

* ACID compliance ensures data integrity

### Bad

* Requires more upfront schema design

### Neutral

* Team has moderate PostgreSQL experience