architecture-decision-records▌
affaan-m/everything-claude-code · updated Apr 8, 2026
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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.
Architecture Decision Records
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.
When to Activate
- User explicitly says "let's record this decision" or "ADR this"
- User chooses between significant alternatives (framework, library, pattern, database, API design)
- User says "we decided to..." or "the reason we're doing X instead of Y is..."
- User asks "why did we choose X?" (read existing ADRs)
- During planning phases when architectural trade-offs are discussed
ADR Format
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**Status**: proposed | accepted | deprecated | superseded by ADR-NNNN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]
Workflow
Capturing a New ADR
When a decision moment is detected:
- Initialize (first time only) — if
docs/adr/does not exist, ask the user for confirmation before creating the directory, aREADME.mdseeded with the index table header (see ADR Index Format below), and a blanktemplate.mdfor manual use. Do not create files without explicit consent. - Identify the decision — extract the core architectural choice being made
- Gather context — what problem prompted this? What constraints exist?
- Document alternatives — what other options were considered? Why were they rejected?
- State consequences — what are the trade-offs? What becomes easier/harder?
- Assign a number — scan existing ADRs in
docs/adr/and increment - Confirm and write — present the draft ADR to the user for review. Only write to
docs/adr/NNNN-decision-title.mdafter explicit approval. If the user declines, discard the draft without writing any files. - Update the index — append to
docs/adr/README.md
Reading Existing ADRs
When a user asks "why did we choose X?":
- Check if
docs/adr/exists — if not, respond: "No ADRs found in this project. Would you like to start recording architectural decisions?" - If it exists, scan
docs/adr/README.mdindex for relevant entries - Read matching ADR files and present the Context and Decision sections
- If no match is found, respond: "No ADR found for that decision. Would you like to record one now?"
ADR Directory Structure
docs/
└── 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
ADR Index Format
# 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 |
Decision Detection Signals
Watch for these patterns in conversation that indicate an architectural decision:
Explicit signals
- "Let's go with X"
- "We should use X instead of Y"
- "The trade-off is worth it because..."
- "Record this as an ADR"
Implicit signals (suggest recording an ADR — do not auto-create without user confirmation)
- Comparing two frameworks or libraries and reaching a conclusion
- Making a database schema design choice with stated rationale
- Choosing between architectural patterns (monolith vs microservices, REST vs GraphQL)
- Deciding on authentication/authorization strategy
- Selecting deployment infrastructure after evaluating alternatives
What Makes a Good ADR
Do
- Be specific — "Use Prisma ORM" not "use an ORM"
- Record the why — the rationale matters more than the what
- Include rejected alternatives — future developers need to know what was considered
- State consequences honestly — every decision has trade-offs
- Keep it short — an ADR should be readable in 2 minutes
- Use present tense — "We use X" not "We will use X"
Don't
- Record trivial decisions — variable naming or formatting choices don't need ADRs
- Write essays — if the context section exceeds 10 lines, it's too long
- Omit alternatives — "we just picked it" is not a valid rationale
- Backfill without marking it — if recording a past decision, note the original date
- Let ADRs go stale — superseded decisions should reference their replacement
ADR Lifecycle
proposed → accepted → [deprecated | superseded by ADR-NNNN]
- proposed: decision is under discussion, not yet committed
- accepted: decision is in effect and being followed
- deprecated: decision is no longer relevant (e.g., feature removed)
- superseded: a newer ADR replaces this one (always link the replacement)
Categories of Decisions Worth Recording
| 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 |
Integration with Other Skills
- Planner agent: when the planner proposes architecture changes, suggest creating an ADR
- Code reviewer agent: flag PRs that introduce architectural changes without a corresponding ADR
How to use architecture-decision-records on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add architecture-decision-records
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches architecture-decision-records from GitHub repository affaan-m/everything-claude-code and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate architecture-decision-records. Access the skill through slash commands (e.g., /architecture-decision-records) or your agent's skill management interface.
Security & Verification Notice
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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★52 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
architecture-decision-records is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Charlotte Thompson· Dec 24, 2024
Solid pick for teams standardizing on skills: architecture-decision-records is focused, and the summary matches what you get after install.
- ★★★★★Carlos Khan· Dec 20, 2024
architecture-decision-records fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ava Gupta· Dec 4, 2024
Registry listing for architecture-decision-records matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Nia Khan· Nov 27, 2024
architecture-decision-records reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Hiroshi Shah· Nov 23, 2024
architecture-decision-records fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yash Thakker· Nov 19, 2024
Keeps context tight: architecture-decision-records is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Charlotte Brown· Nov 15, 2024
architecture-decision-records has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kwame Zhang· Nov 11, 2024
Registry listing for architecture-decision-records matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Nia Rahman· Oct 18, 2024
I recommend architecture-decision-records for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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