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architecture-decision-records

Capture architectural decisions made during Claude Code sessions as structured ADRs. Auto-detects decision moments, records context, alternatives considered, and rationale. Maintains an ADR log so future developers understand why the codebase is shaped the way it is.

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architecture-decision-records is a Claude Code agent skill that capture architectural decisions made during Claude Code sessions as structured ADRs. Auto-detects decision moments, records context, alternatives considered, and rationale. Maintains an ADR log so future developers understand why the codebase is shaped the way it is.

지원 대상Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/architecture-decision-records

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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**Status**: proposed | accepted | deprecated | superseded by ADR-NNNN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:

  1. Initialize (first time only) — if 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.
  2. Identify the decision — extract the core architectural choice being made
  3. Gather context — what problem prompted this? What constraints exist?
  4. Document alternatives — what other options were considered? Why were they rejected?
  5. State consequences — what are the trade-offs? What becomes easier/harder?
  6. Assign a number — scan existing ADRs in docs/adr/ and increment
  7. Confirm and write — present the draft ADR to the user for review. Only write to docs/adr/NNNN-decision-title.md after explicit approval. If the user declines, discard the draft without writing any files.
  8. Update the index — append to docs/adr/README.md

Reading Existing ADRs

When a user asks "why did we choose X?":

  1. Check if docs/adr/ exists — if not, respond: "No ADRs found in this project. Would you like to start recording architectural decisions?"
  2. If it exists, scan docs/adr/README.md index for relevant entries
  3. Read matching ADR files and present the Context and Decision sections
  4. 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

CategoryExamples
Technology choicesFramework, language, database, cloud provider
Architecture patternsMonolith vs microservices, event-driven, CQRS
API designREST vs GraphQL, versioning strategy, auth mechanism
Data modelingSchema design, normalization decisions, caching strategy
InfrastructureDeployment model, CI/CD pipeline, monitoring stack
SecurityAuth strategy, encryption approach, secret management
TestingTest framework, coverage targets, E2E vs integration balance
ProcessBranching 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

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

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affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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