Capture architectural decisions as they happen during coding sessions. Instead of decisions living only in Slack threads, PR comments, or someone's memory, this skill produces structured ADR documents that live alongside the code.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionarchitecture-decision-recordsExecute the skills CLI command in your project's root directory to begin installation:
Fetches architecture-decision-records from affaan-m/everything-claude-code 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 architecture-decision-records. Access via /architecture-decision-records 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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Capture architectural decisions as they happen during coding sessions. Instead of decisions living only in Slack threads, PR comments, or someone's memory, this skill produces structured ADR documents that live alongside the code.
Use the lightweight ADR format proposed by Michael Nygard, adapted for AI-assisted development:
# ADR-NNNN: [Decision Title]
**Date**: YYYY-MM-DD
**Status**: proposed | accepted | deprecated | superseded by ADR-NNNN
**Deciders**: [who was involved]
## Context
What is the issue that we're seeing that is motivating this decision or change?
[2-5 sentences describing the situation, constraints, and forces at play]
## Decision
What is the change that we're proposing and/or doing?
[1-3 sentences stating the decision clearly]
## Alternatives Considered
### Alternative 1: [Name]
- **Pros**: [benefits]
- **Cons**: [drawbacks]
- **Why not**: [specific reason this was rejected]
### Alternative 2: [Name]
- **Pros**: [benefits]
- **Cons**: [drawbacks]
- **Why not**: [specific reason this was rejected]
## Consequences
What becomes easier or more difficult to do because of this change?
### Positive
- [benefit 1]
- [benefit 2]
### Negative
- [trade-off 1]
- [trade-off 2]
### Risks
- [risk and mitigation]
When a decision moment is detected:
docs/adr/ does not exist, ask the user for confirmation before creating the directory, a README.md seeded with the index table header (see ADR Index Format below), and a blank template.md for manual use. Do not create files without explicit consent.docs/adr/ and incrementdocs/adr/NNNN-decision-title.md after explicit approval. If the user declines, discard the draft without writing any files.docs/adr/README.mdWhen a user asks "why did we choose X?":
docs/adr/ exists — if not, respond: "No ADRs found in this project. Would you like to start recording architectural decisions?"docs/adr/README.md index for relevant entriesdocs/
└── adr/
├── README.md ← index of all ADRs
├── 0001-use-nextjs.md
├── 0002-postgres-over-mongo.md
├── 0003-rest-over-graphql.md
└── template.md ← blank template for manual use
# Architecture Decision Records
| ADR | Title | Status | Date |
|-----|-------|--------|------|
| [0001](0001-use-nextjs.md) | Use Next.js as frontend framework | accepted | 2026-01-15 |
| [0002](0002-postgres-over-mongo.md) | PostgreSQL over MongoDB for primary datastore | accepted | 2026-01-20 |
| [0003](0003-rest-over-graphql.md) | REST API over GraphQL | accepted | 2026-02-01 |
Watch for these patterns in conversation that indicate an architectural decision:
Explicit signals
Implicit signals (suggest recording an ADR — do not auto-create without user confirmation)
proposed → accepted → [deprecated | superseded by ADR-NNNN]
| Category | Examples |
|---|---|
| Technology choices | Framework, language, database, cloud provider |
| Architecture patterns | Monolith vs microservices, event-driven, CQRS |
| API design | REST vs GraphQL, versioning strategy, auth mechanism |
| Data modeling | Schema design, normalization decisions, caching strategy |
| Infrastructure | Deployment model, CI/CD pipeline, monitoring stack |
| Security | Auth strategy, encryption approach, secret management |
| Testing | Test framework, coverage targets, E2E vs integration balance |
| Process | Branching strategy, review process, release cadence |
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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
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mattpocock/skills
architecture-decision-records is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: architecture-decision-records is focused, and the summary matches what you get after install.
architecture-decision-records fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for architecture-decision-records matched our evaluation — installs cleanly and behaves as described in the markdown.
architecture-decision-records reduced setup friction for our internal harness; good balance of opinion and flexibility.
architecture-decision-records fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: architecture-decision-records is the kind of skill you can hand to a new teammate without a long onboarding doc.
architecture-decision-records has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for architecture-decision-records matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend architecture-decision-records for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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