adr-writing
Write Architectural Decision Records following MADR template. Applies Definition of Done criteria, marks gaps for later completion. Use when generating ADR documents from extracted decisions.
Best use case
adr-writing is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Write Architectural Decision Records following MADR template. Applies Definition of Done criteria, marks gaps for later completion. Use when generating ADR documents from extracted decisions.
Teams using adr-writing should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/adr-writing/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How adr-writing Compares
| Feature / Agent | adr-writing | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Write Architectural Decision Records following MADR template. Applies Definition of Done criteria, marks gaps for later completion. Use when generating ADR documents from extracted decisions.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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):
```bash
python scripts/next_adr_number.py
```
This outputs the next available ADR number (e.g., `0003`).
For parallel allocation (used by parent commands):
```bash
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:
| Section | Source |
|---------|--------|
| Title | Decision summary (imperative mood) |
| Status | Always `draft` initially |
| Context | Problem statement, constraints |
| Decision Drivers | Prioritized requirements |
| Considered Options | All viable alternatives |
| Decision Outcome | Chosen option with rationale |
| Consequences | Good, bad, neutral impacts |
### Step 5: Apply Definition of Done
Load `references/definition-of-done.md` and verify E.C.A.D.R. criteria:
- **E**xplicit problem statement
- **C**omprehensive options analysis
- **A**ctionable decision
- **D**ocumented consequences
- **R**eviewable by stakeholders
### Step 6: Mark Gaps
For sections that cannot be filled from available data, insert investigation prompts:
```markdown
* [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:
```yaml
---
status: draft
date: YYYY-MM-DD
---
```
Full frontmatter template:
```yaml
---
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`
| Component | Rule |
|-----------|------|
| `NNNN` | Zero-padded sequence number from script |
| `-` | Separator |
| `slugified-title` | Lowercase, hyphens, no special characters |
| `.md` | Markdown extension |
## Reference Files
- `references/madr-template.md` - Official MADR template structure
- `references/definition-of-done.md` - E.C.A.D.R. quality criteria
## Output Example
```markdown
---
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
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