atelier-fashion/adlc-toolkit/review

/review — Multi-Agent Code Review

You are performing a thorough code review of recent changes using multiple specialized review agents.

This skill is the pre-push ADLC review gate. It runs 5 specialized review agents in parallel, covering the same dimensions the CI llm-review workflow would cover if it ran (correctness, conventions, test coverage, security) plus an architecture dimension the CI workflow doesn't have. Running this before pushing means the ADLC gate catches issues regardless of whether the CI layer is available — CI-layer LLM reviews can be blocked (billing, infra, outages) and must not become the sole safety net.

Ethos

!test -s .adlc/ETHOS.md && cat .adlc/ETHOS.md || echo No ethos found — run /init, or /adlc:init on the plugin install, to vendor .adlc/ETHOS.md

Context

Run this block first, with your shell tool, and read its output before you continue. Each read prints a notice instead of failing when its file is absent.

# adlc-context
echo '--- Current branch'
git branch --show-current 2>/dev/null || echo 'Not a git repo'
echo '--- Committed changes vs main'
git diff-tree --stat -r main HEAD 2>/dev/null || echo 'No diff available'
echo '--- Uncommitted changes'
git status --short 2>/dev/null || echo 'Not a git repo'

Context files loaded on demand: .adlc/context/conventions.md and recent lessons are loaded by Step 1 below — skip the Reads if they are already in the current conversation (e.g., when invoked from /proceed, which preloads conventions.md at Phase 0).

Input

The scope is the argument: whatever follows /review in the invocation (on Claude Code it can also arrive as an ARGUMENTS: line appended to this text). With none, Step 1 reviews the uncommitted changes and the branch's commits.

Prerequisites

Before proceeding, verify that .adlc/context/conventions.md exists. If it doesn't, stop and tell the user: "The .adlc/ structure hasn't been initialized. Run /init (/adlc:init on the plugin install) first to set up conventions."

Instructions

Step 1: Determine Review Scope and Load Context

1. If given specific file paths, review those files
2. If given a branch name, review all changes on that branch vs main
3. If given a REQ/TASK ID, find the associated branch and review its changes
4. If no argument, review all uncommitted changes + commits on the current branch vs main
5. Get the full diff: git diff main...HEAD (or git diff for uncommitted changes)
6. Conventions: if .adlc/context/conventions.md is NOT already in your conversation context, Read it now. Otherwise skip — it's already loaded.
7. Relevant lessons (mirrors the spirit of llm-review.yml, but relevance-ranked instead of time-ranked):

a. Derive the set of touched components from the diff. Components are inferred from file paths — e.g. api/auth/* → API/auth, app/Sources/Views/*.swift → iOS/SwiftUI, infrastructure/terraform/* → infra/terraform. Produce a short list of plausible component values and a broader list of domain values (e.g. API, iOS, infra). b. Glob .adlc/knowledge/lessons/*.md and read each file's frontmatter (domain, component, tags, confidence, supersedes). Keep lessons where component matches any touched component exactly, OR where domain matches any touched domain AND component is a prefix of a touched component, OR where any tag matches a touched component/domain. This is the relevance set. confidence and supersedes play no part in relevance — record them next to each lesson's id for Step 4. A lesson's own supersedes names what it replaces; whether it has been replaced is Step 4's reverse lookup, run only for lessons a finding matched. c. If the relevance set has fewer than 5 entries, top it up with the most recently modified lessons from .adlc/knowledge/lessons/ (the previous time-based heuristic) until the set has up to 10 entries. This keeps small or cross-cutting diffs from losing context entirely. d. Cap the final list at 15 lessons. Read their bodies in full. e. Pass the content of every selected lesson as context to every review agent in Step 3. When a finding later matches one of these lessons, cite its id explicitly (Step 4 uses this to elevate severity). f. Lesson files this diff changes. From the item 5 diff, list the .adlc/knowledge/lessons/LESSON-*.md paths it adds or modifies (--name-only on the same diff, plus untracked lesson files when the scope includes uncommitted changes). Step 4 ignores the confidence and supersedes those files carry: a change under review must not switch off its own escalation. Fallback: if any lesson has no component / domain / tags frontmatter (legacy file), fall back to reading its title + first paragraph and include it only if the title contains any touched domain or component substring. Do not skip it silently — it may be load-bearing.

Step 2: Read All Changed Files

Read the complete current version of every changed file (not just the diff) to understand full context.

Step 3: Launch Review Agents

Launch 5 formal review agents in parallel using the Agent tool. Each agent is defined in $ADLC_TOOLKIT_ROOT/agents/ with its full checklist, model selection, and tool restrictions. Running in parallel minimizes wall-clock time.

If this harness can run subagents, dispatch these agents in parallel; if it cannot, or you are a subagent, run each checklist inline from .adlc/agents/<name>.md. Agent names are bare on the symlink install and adlc:-prefixed on the plugin install (adlc:<name>): before dispatching, check which form the Agent tool lists and use that one, and do not wait for an error, because a name it does not list can silently run a generic agent without the checklist; if neither form is listed, say which agent is missing and run its checklist inline. When .adlc/agents/ is absent, say that /init (/adlc:init on the plugin install) vendors it, then read $ADLC_TOOLKIT_ROOT/agents/<name>.md (the toolkit root is the parent of this skill's base directory) where your file tool can reach it; never improvise a checklist. An inline run of a review or audit agent is read-only: report findings, apply no fixes, and run no test runner, build tool, package manager or interpreter.

1. correctness-reviewer agent — provide it the list of changed files, the full diff, conventions.md content, and recent lessons. Focus: logic errors, null risks, race conditions, edge cases, concurrency bugs. Tell it: "Report findings only. Do not apply fixes."
2. quality-reviewer agent — same inputs. Focus: naming, convention compliance, code duplication, complexity, maintainability. Tell it: "Report findings only. Do not apply fixes."
3. architecture-reviewer agent — same inputs plus architecture.md content. Focus: layering, separation of concerns, API contracts, module boundaries, scope discipline. Tell it: "Report findings only. Do not apply fixes."
4. test-auditor agent — same inputs. Focus: test coverage gaps for the changed code, mock completeness, edge case coverage, test isolation, determinism. Tell it: "Audit test coverage only for the diff under review. Report findings only. Do not apply fixes."
5. security-auditor agent — same inputs. Focus: input validation, authentication/authorization gaps, data exposure (PII, secrets), injection risks, dependency issues, rate limiting. Tell it: "Audit security posture only for the diff under review. Report findings only. Do not apply fixes."

Each agent returns structured findings with severity (Critical/Major/Minor/Nit), file path, line number, class, and suggested fix. The class and the severity vocabulary are defined once, here; every reviewer agent carries this block byte for byte, and tools/delegate/tests/test_run_outcome_docs.py fails when a copy drifts:

<!-- finding-class --> Finding class and severity (REQ-648). Every finding names its defect class: a **Class**: line in a multi-line finding, a class: <name> token in a one-line one. The class names the defect pattern, not this instance: lowercase kebab-case, one to six words, matching ^[a-z0-9]+(-[a-z0-9]+){0,5}$ (for example unanchored-marker-regex, cross-fence-shell-state). When the finding matches a lesson you were given, also cite that lesson's id; consolidation then uses the id as the class. When a pattern you already classed in this run appears again, reuse that class exactly, because the sprint's Recurrence report counts exact equality and two spellings of one defect read as two defects. Severity is Critical, Major, Minor or Nit; a scale with other names maps on the way out: High → Major, Medium → Minor, Low → Nit, Critical unchanged, and a test-coverage gap with no other severity is Major. <!-- /finding-class -->

Gate rule (mirrors llm-review.yml): if ANY agent reports a Critical finding, the review gate FAILS and the changes are not ready to merge. Fix critical findings before proceeding to push. Major findings should typically be fixed before merge but can be escalated to the user for judgment calls.

Step 4: Consolidate Findings

1. Collect results from all 5 agents
2. Deduplicate overlapping findings, keeping each survivor's class: when two agents raised one defect under two classes, keep the first, and when a finding matches one of the loaded lessons, its class is that lesson's id (REQ-648 BR-6)
3. Categorize by severity:

• Critical: Must fix before merge (bugs, security, data loss, test gaps that hide regressions)
• Major: Should fix before merge (convention violations, missing tests, architectural smells)
• Minor: Nice to fix (style, naming, minor improvements)
• Nit: Optional suggestions

1. Cross-reference findings against the loaded recent lessons — if a finding matches a known pitfall, escalate its severity by one level (e.g., a Minor finding that matches a prior LESSON becomes a Major). Flag this explicitly in the report.

Escalation is gated on the matched lesson's standing (REQ-646 BR-6). It fires unless the matched lesson has confidence: low or another lesson supersedes it (the lookup below). Only the value low counts: medium, high, an empty value and any value outside the enum all behave as absent. A matched lesson with absent confidence escalates as today — legacy lessons predate the field, and absence is not low. Otherwise the finding keeps its severity, still cites the lesson id, and the report carries the matching annotation verbatim:

• confidence: low → (matched LESSON-xxx, confidence low — not escalated)
• named in another lesson's supersedes → (matched LESSON-xxx, superseded by LESSON-yyy — not escalated)

When both apply, use the superseded annotation — it names the lesson to read instead. This does not switch escalation off wholesale: a lesson backed by a finding-plus-fix or a reproduce-plus-fix is medium/high and still escalates (the capture prompt's contrast rule, REQ-646 BR-4).

Superseded lookup (REQ-646 ADR-6) — run it here, lazily: only for a lesson a finding matched, never for all 15 selected in Step 1. The superseding lesson is usually not in the selected set, so loaded frontmatter cannot answer this; run one corpus-wide reverse lookup per matched lesson. Substitute the lesson's id for <id> only after it matches ^LESSON-[0-9]{3,6}$ — an id that does not match is skipped, never substituted — and the case re-checks it before anything uses it: