Adr Writer

Turn an architectural decision into a durable, reviewable record. The skill takes the decision the user describes, gathers the real constraints that shaped it from the repository, and writes a Nygard-style Architecture Decision Record — context and problem, the decision and its status, the consequences, and the alternatives that were considered and rejected. The result is a numbered, immutable document that explains why a choice was made to whoever reads the code in two years.

When to use this skill

• You made a consequential, hard-to-reverse choice (datastore, framework, auth model, sync vs. async, monorepo vs. polyrepo) and want the reasoning captured before it's forgotten.
• You're starting an ADR log in a repo that has none, or adding the next record to an existing docs/adr/ directory.
• A pull request changes architecture and a reviewer asked "where is this written down?"
• You're revisiting an old decision and need to supersede it with a new record instead of silently editing history.

Instructions

1. Locate the ADR log. Search for an existing directory — docs/adr/, docs/decisions/, doc/adr/, or adr/. Read one or two existing records to match the local heading set, status vocabulary, and front-matter (some logs use MADR, some Nygard, some carry a date:/deciders: block). If no log exists, default to docs/adr/.
2. Assign the number and slug. Find the highest existing NNNN-*.md and increment it, zero-padded to four digits (0001, 0002, ...). Build the filename as docs/adr/NNNN-kebab-title.md from a short, decision-focused title (0007-use-postgres-for-primary-store.md). Never reuse or renumber an existing file.
3. Detect the real constraints — don't invent them. Mine the repo for evidence that shaped the decision instead of writing generic pros and cons:
   • Read package.json / requirements.txt / go.mod / Cargo.toml for the current stack and what's already a dependency.
   • Grep for the systems in play (grep -ri "mongoose\|prisma\|pg\|sqlalchemy") to see what's actually wired up.
   • Check docker-compose.yml, *.tf, CI config, and any README/CLAUDE.md for deployment targets, scale signals, and stated team conventions.
   • Note the requirement that forces the decision (transactions, relational queries, a managed offering already in the cloud account, an existing team skillset).
4. Write the record. Use the Nygard section order: # NNNN. Title, then ## Status, ## Context, ## Decision, ## Consequences (split into positive and negative — be honest about the costs you're accepting), and ## Considered Alternatives (an added section not in Nygard's original — list each rejected option with the specific reason it lost). Write in past/decided tense, name concrete tradeoffs, and cite the constraints from step 3 rather than restating textbook definitions.
5. Set the status deliberately. Use Proposed for an open decision under review, Accepted once it's agreed, Deprecated for an outdated record, or Superseded by ADR-NNNN when replaced. If this record retires an older one, update that file's status line to point here.
6. Verify and report. Confirm the filename number is unique, the title slug matches the heading, and every alternative has a stated reason for rejection. List the file you wrote, and flag any section you filled from assumption rather than repo evidence so the user can correct it before merging.

Examples

For the decision "choose Postgres over MongoDB for the primary datastore," the skill detects no mongoose/prisma is wired yet, sees a managed Postgres already in the cloud account, and writes docs/adr/0001-use-postgres-as-primary-datastore.md:

# 0001. Use PostgreSQL as the primary datastore
 
## Status
 
Accepted — 2026-06-03
 
## Context
 
The billing and account services need ACID transactions across orders,
invoices, and ledger entries, and most read paths join three or more
entities. Our data is strongly relational with a stable schema. The team
already operates a managed Postgres instance in the existing cloud account
and knows SQL; no one has run MongoDB in production here.
 
## Decision
 
We will use PostgreSQL 16 as the primary datastore for all transactional
services, accessed through a single connection pool per service. Document-
shaped, schemaless data (audit blobs, webhook payloads) will live in
`jsonb` columns rather than a separate document database.
 
## Consequences
 
**Positive**
 
- Multi-row transactions and foreign keys enforce invariants in the
  database instead of in application code.
- One datastore to operate, back up, and monitor — reuses the managed
  instance and the team's existing SQL skills.
- `jsonb` covers the few semi-structured cases without a second system.
 
**Negative**
 
- Horizontal write scaling requires deliberate work (partitioning, read
  replicas) if write volume outgrows a single primary.
- Schema changes need migrations and review; less forgiving than a
  schemaless store during rapid early iteration.
 
## Considered Alternatives
 
- **MongoDB** — rejected: our access patterns are relational and need
  cross-document transactions, which fight against its document model and
  would push join logic into the application.
- **Postgres + a separate document DB** — rejected: doubles operational
  surface for a small amount of semi-structured data that `jsonb` handles.
- **SQLite** — rejected: no managed multi-writer story for our concurrency
  and availability needs.

After writing, report the path and note any section (e.g. projected write volume) that came from an assumption rather than a measured constraint, so the user can verify it before merging.