squid-architecture-review

Architecture review — periodic structural audit

The team's day-to-day pipeline (/squid-implement-task, /squid-implement-night) operates at task grain. Long-horizon codebase health — drift, unintended coupling, dead modules, layering violations — accumulates between tasks and never gets addressed unless someone deliberately looks. This skill is that deliberate look.

The output is not a single PR. It's a prioritised backlog of refactor proposals, each shaped so /squid-refactor can pick one up and turn it into a Tasks Plan.

You are the auditor — you delegate exploration to sub-agents, you read docs/adr/ to avoid re-proposing settled questions, and you produce a written report. You do NOT write code, do NOT start refactors, and do NOT decide priority for the team — you propose, the human prioritises.

$ARGUMENTS:

• Empty → audit the whole repo.
• A path (packages/backend/) → audit only that subtree.
• A component name (backend, frontend-web) → audit only that component.

Smaller scopes produce sharper findings; whole-repo audits produce broader maps but blunter recommendations.

When to use

• The team hasn't done a structural review in 6+ months.
• A major version bump is imminent and you want to clear debt before locking in compatibility.
• A new contributor surfaces "why is X like this?" questions that nobody can answer cleanly.
• A bug was caused by hidden coupling and you suspect more like it exist.
• After a successful /squid-refactor lands and you want to find the next one.

When NOT to use

• During active feature delivery — review when the team has bandwidth to act on findings, not as theatre.
• On a codebase < 6 months old. There isn't yet enough crystallised pattern to review against.
• When the team has clear architectural anxieties already named — go straight to /squid-refactor with the named target.
• As a substitute for code review on a specific PR — open or update the PR with gh and review it there instead.
• On infra-only repos (Terraform, CI tooling). The shape doesn't fit; this skill assumes application code with modules, layers, dependencies.

Step 1 — Frame the scope

Resolve $ARGUMENTS. If empty, ask the user one question via AskUserQuestion:

Whole repo, one component, or a specific subtree?

Echo the resolved scope back as a one-line confirmation. Don't block.

Step 2 — Read prior decisions

Read docs/adr/ end-to-end if it exists. Build a mental model of:

• What decisions are Accepted and still in force.
• What decisions are Superseded (and by what).
• What constraints (technical, business, team-shape) the ADRs cite.

This is the single highest-value step. An architecture review that proposes "split the auth module" when ADR-0005 already explained why it's deliberately combined wastes everyone's time. If ADRs don't exist, note that — recommend adr.md as a prerequisite to capturing this review's outcome.

Also read, if present:

• docs/glossary.md — to know whether the team already names concepts consistently.
• The root CLAUDE.md and any per-component CLAUDE.md — for stated rules.

Step 3 — Map the current architecture

Spawn 2–3 Explore agents in parallel:

Agent(
  subagent_type="Explore",
  prompt="""Architecture mapping pass on {scope}.

  Produce four artefacts:
  (1) Module / package list with one-line responsibility per module (file:line for the canonical entry point).
  (2) Dependency graph — who imports whom. Flag any cycles. Flag any "junk drawer" modules imported by everyone.
  (3) Layering — name the de-facto layers (e.g., handlers → services → repositories → models) and list any layer-skipping imports.
  (4) Public surface — what's exported / consumed by other components or external callers. Mark anything intended-internal that's leaked.

  Be exhaustive on the dependency graph; missing an edge produces a wrong recommendation. Report as four sections."""
)

Agent(
  subagent_type="Explore",
  prompt="""Hot-spot scan on {scope}.

  Find:
  (1) Files with > 500 lines.
  (2) Files / modules touched by > 30% of commits in the last 6 months — `git log --since='6 months ago' --name-only | sort | uniq -c | sort -rn | head -20`.
  (3) Tests with > 100 setup lines or > 5 fixtures — usually a smell that the system-under-test is too coupled.
  (4) `# TODO` / `# FIXME` / `# HACK` markers — count and cluster.
  (5) Any `# type: ignore` / `eslint-disable` / `nolint` clusters — places where the team is fighting their own static analysis.

  Report as five sections with file:line."""
)

When agents return, read the top-3 most-implicated files yourself. Don't rely on summaries on the load-bearing modules — you need firsthand familiarity to call findings credibly.

Step 4 — Identify smells

Walk these dimensions. For each, form 0 or more findings. Don't manufacture findings to pad the report — empty dimensions are fine and signal health.

Dimension	What to look for
Cycles	Import cycles between modules / packages. Always a smell.
Layer violations	Lower layers (e.g., models) importing higher layers (e.g., HTTP handlers).
God modules	One module that everything imports; usually means responsibilities accreted.
Junk drawers	utils/, common/, helpers/ modules with > 10 unrelated functions.
Hidden coupling	Two modules that should be independent but share a hidden contract (a shape, a magic string, an env var).
Test smells	High setup-to-assert ratio; brittle tests on private internals; flaky tests. Usually rooted in a structural problem in the SUT.
API leakage	Internal types reaching public surface; public surface that should be private (e.g., a route that no caller uses).
Dead code	Unused exports, unreachable branches, modules with zero importers.
Over-engineering	Abstraction the code doesn't earn: a single-implementation interface / factory / wrapper that only forwards, a layer with one caller, config nobody sets, hand-rolled logic the stdlib ships, or a dependency duplicating a platform/framework feature. Tag each with the same delete/stdlib/native/yagni/shrink vocabulary the PR-Reviewer uses (Dimension F).
Drift from CLAUDE.md	Stated rules the codebase no longer follows. The rule is right; the code drifted.
ADR drift	Decisions ADRs describe that the code no longer reflects (silently superseded). Either update the ADR or fix the code.
Performance shape	Sync I/O in async handlers; N+1 patterns; in-memory aggregation that should be a query.
Observability gaps	Critical paths without logs / metrics / traces; logs that don't include correlation IDs.

Step 5 — Score and prioritise

For each finding, assign:

• Severity: S1 (active risk — security, data loss, ongoing pain) / S2 (significant friction) / S3 (technical debt without acute symptom) / S4 (cosmetic, defer).
• Effort: S (small, ≤ half a day) / M (medium, 1–3 days) / L (large, > 3 days; probably needs to split).
• Confidence: High (you've read the code yourself) / Medium (one sub-agent finding, you spot-checked) / Low (sub-agent finding, you haven't verified).

Prioritise by severity / effort ratio, with confidence as a tiebreaker. Do not propose more than 10 findings. A 30-finding report gets ignored; a 7-finding report gets acted on. Cluster related findings into one if they share a root cause.

Step 6 — Write the report

Path:

• File mode: tracker/squid-architecture-review-{YYYY-MM-DD}.md.
• gh mode: a single issue, label squid-architecture-review.

Template:

# Architecture review — {scope}, {YYYY-MM-DD}

**Scope:** {whole repo / component / path}
**ADRs read:** {N} ({list, e.g. "0001–0007"})
**Findings:** {N total — by severity: S1×N, S2×N, S3×N, S4×N}

## Summary

{2–3 sentences. The headline shape: "Three module cycles between auth/ and core/, all introduced in the last 4 months, blocking the planned auth-extraction work. Tests on the affected modules have grown 2× in setup size. No ADR governs the boundary. Recommend ADR + extraction refactor."}

## Findings

### F1 — {one-line title} (S{1–4}, effort: {S/M/L}, confidence: {H/M/L})

**Where:** `path/to/a.py:42`, `path/to/b.py:117`

**Symptom:** {what is concretely wrong, observable}

**Root cause hypothesis:** {your best read on why}

**Proposed refactor:** {one paragraph; the shape of the fix, not the fix itself}

**ADR exposure:** {does an ADR govern this? if yes, cite. If no, an ADR should accompany the refactor.}

**Out of scope (explicit):** {what this finding is NOT recommending}

### F2 — ...

(Repeat for each finding, top to bottom by priority.)

## Not recommended

{Smells you found but are deliberately NOT proposing. State why — e.g., "F-skip-1: The `utils/strings.py` module has 12 unrelated functions and looks like a junk drawer. Skipping because every function is < 10 lines and the cost of splitting exceeds the reading cost."}

## Pre-existing decisions (from ADRs) you should NOT undo

{One-bullet-each summary of ADRs that explain *why* something looks weird but is intentional. This protects the next reader from re-litigating.}

## Suggested order of operations

1. F{best-first} — {why it goes first}
2. F{next} — {why}
...

Each finding above can be picked up by `/squid-refactor F{N}` (paste the finding's body into the refactor goal). Don't tackle all of them at once — pick the top 1–3 and run them through the pipeline first.

Step 7 — Hand off

Single block:

## Architecture review complete — {scope}

**Report:** {path or issue URL}
**Findings:** {N} ({severity breakdown})
**Top recommendation:** F1 — {title}

### Recommended next step

`/squid-refactor F1` (or paste the F1 body into a fresh `/squid-refactor` invocation). The first finding is the highest leverage by severity-over-effort.

### Open question (if applicable)

{Anything you found but couldn't conclude on without team input — e.g., "F4 hinges on whether the auth/billing boundary should be a hard-coded contract or a feature-flag-toggled split. Surfacing for team decision before refactoring."}

### ADR action

{One of:}
- "Recommend ADR-{NNNN} to record the resolution of F{X} when its refactor lands."
- "No ADRs in `docs/adr/` — recommend bootstrapping with [`adr.md`](../squid-scaffold/specs/adr.md) before proceeding so this review's findings stay durable."

Notes on shape

• The ADR read is non-negotiable. Half the findings in a poorly-scoped review are decisions the team already made and rejected. Reading ADRs first is the cheapest way to be useful.
• Cap findings at 10. Past 10, the report becomes a wishlist; the team picks zero. Force prioritisation by enforcing the cap.
• Confidence affects priority. A High-confidence S2 beats a Low-confidence S1 — don't recommend a refactor on a finding you haven't personally read.
• Propose, don't prescribe. This skill outputs findings with proposed refactors. The team prioritises. /squid-refactor then plans. /squid-implement-night then executes. Skipping ahead to "we should do X" pre-commits the team.
• Don't re-litigate. The "Pre-existing decisions" section is a discipline — it prevents the next architecture review (or the next contributor) from undoing things that are intentional.
• Empty findings = signal. If a dimension produced no finding, say so explicitly ("Layer violations: none observed"). It's information.
• Pair output with ADR proposals. Every accepted refactor that lands deserves an ADR if its decision wasn't already governed. Otherwise the next review re-finds it.