Generate Architectural Decision Records (ADRs) following the MADR template with systematic completeness checking.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionadr-writingExecute the skills CLI command in your project's root directory to begin installation:
Fetches adr-writing from existential-birds/beagle and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate adr-writing. Access via /adr-writing in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Generate Architectural Decision Records (ADRs) following the MADR template with systematic completeness checking.
┌─────────────┐ ┌──────────────┐ ┌─────────────┐
│ SEQUENCE │ ──▶ │ EXPLORE │ ──▶ │ FILL │
│ (get next │ │ (context, │ │ (template │
│ number) │ │ ADRs) │ │ sections) │
└─────────────┘ └──────────────┘ └─────────────┘
│ │
│ ▼
│ ┌─────────────┐
│ │ VERIFY │
│ │ (DoD │
└─────────────────────────────────│ checklist)│
└─────────────┘
If a number was pre-assigned (e.g., when called from /beagle:write-adr with parallel writes):
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)
Before writing, gather additional context:
docs/adrs/ for related or superseded decisionsLoad references/madr-template.md for the official MADR structure.
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 |
Load references/definition-of-done.md and verify E.C.A.D.R. criteria:
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.
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
After writing, confirm the file:
--- on the first linestatus: draft (or other valid status)date: YYYY-MM-DD with actual date--- before the titleFormat: NNNN-slugified-title.md
| Component | Rule |
|---|---|
NNNN |
Zero-padded sequence number from script |
- |
Separator |
slugified-title |
Lowercase, hyphens, no special characters |
.md |
Markdown extension |
references/madr-template.md - Official MADR template structurereferences/definition-of-done.md - E.C.A.D.R. quality criteria---
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
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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ailabs-393/ai-labs-claude-skills
adr-writing fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend adr-writing for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: adr-writing is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for adr-writing matched our evaluation — installs cleanly and behaves as described in the markdown.
We added adr-writing from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
adr-writing is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in adr-writing — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for adr-writing matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: adr-writing is the kind of skill you can hand to a new teammate without a long onboarding doc.
adr-writing reduced setup friction for our internal harness; good balance of opinion and flexibility.
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