name

architecture-decision-records

description

Capture architectural decisions made during coding 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. TRIGGER when: user says "record this decision" or "ADR this", or when significant architectural choices are made during the AI engineering pipeline.

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
As Phase 2 of the AI engineering pipeline (running as a background service during blueprint → eval-harness → self-improve → ai-slop-cleaner)

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**Status**: proposed | accepted | deprecated | superseded by ADR-NNNN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

Instructions

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, 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.
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.md after 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.md index 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

Pipeline Integration

This skill is Phase 2 of the AI engineering pipeline. It operates as a continuous background service throughout the pipeline:

During blueprint (Phase 1): suggest recording ADRs for architectural choices in the plan
During eval-harness (Phase 3): record decisions about evaluation methodology
During self-improve (Phase 4): capture why certain optimization approaches were chosen over others — critical for understanding code evolution across dozens of iterations
During ai-slop-cleaner (Phase 5): record decisions about what was cleaned and why

Its special value: in self-improve's dozens of evolution rounds, if you don't record "why approach A was chosen over B" at each round, after a few rounds nobody (including the AI itself) can understand why the code looks the way it does.