From liti-garage
Composite review — fan out reviewer agents over a plan (requirements checklist) or code changes (violation report), merge into a single report with a machine-parsed VERDICT line.
How this skill is triggered — by the user, by Claude, or both
Slash command
/liti-garage:reviewThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Run the appropriate reviewer agents based on the mode and stack.
Run the appropriate reviewer agents based on the mode and stack.
/review plan [<path>] — Review a plan/feature description before coding. Produces a requirements checklist. With <path>, reviewers read that file directly (used by /brief's review gate)./review code — Review actual code changes. Produces a violation report./review code backend — Limit code review to backend reviewers only./review code frontend — Limit code review to frontend reviewers only.planWhen: Before coding starts. The user has a feature description or plan draft.
What it does: Launches three agents in parallel — one for engineering standards (@agents/plan-requirements-reviewer.md), one for business/product readiness (@agents/business-readiness-reviewer.md), and one for security & threat modeling (@agents/security-threat-modeling-reviewer.md). Together they produce a comprehensive review covering technical requirements, business blind spots, and design-level security gaps. No code scanning.
Behavior:
<path> argument was given, the plan is that file — do not paste its contents into the reviewer prompts; instead append to each reviewer prompt: "The plan to review is at <path> — read it with the Read tool." (Reviewers reading the file themselves stay fresh-context; a pasted summary would carry this session's framing.) If no path was given, read the user's feature description or plan draft from context and include it in the prompts.subagent_type: "general-purpose":
@agents/plan-requirements-reviewer.md (engineering standards)@agents/business-readiness-reviewer.md (business/product readiness)@agents/security-threat-modeling-reviewer.md (security & threat modeling)VERDICT: line (see Verdict lines below).codeWhen: After coding. The user has made changes and wants them reviewed.
What it does: Launches specialized reviewer agents in parallel to scan actual code for violations.
Behavior:
Determine scope: Look at the files the user wants reviewed. If no files are specified, use recent git changes (git diff --name-only HEAD~1).
Detect stack (if not explicitly provided):
.py, .go, .java, .rs files, models/, api/, services/, domains/, background_jobs/.tsx, .jsx, .ts, .js, .swift, .kt, .dart files, components/, screens/, pages/, hooks/Launch reviewers in parallel using the Task tool with subagent_type: "general-purpose":
For backend code, launch three parallel agents:
@agents/production-hardening-reviewer.md scoped to the target files@agents/audit-compliance-reviewer.md scoped to the target files@agents/security-controls-reviewer.md scoped to the target backend files (backend pillars only)For frontend/mobile code, launch three parallel agents:
@agents/production-hardening-reviewer.md scoped to the target frontend files (frontend pillars only — kill switch, client resilience)@agents/analytics-coverage-reviewer.md scoped to the target files@agents/security-controls-reviewer.md scoped to the target frontend files (frontend pillars only)For mixed stack, launch all four agents — a single production-hardening invocation covering both stacks, audit-compliance, analytics-coverage, and a single security-controls-reviewer invocation that covers both backend and frontend pillars across the full file scope.
Merge results: Combine the outputs from all agents into a single report ending with the merged VERDICT: line (see Verdict lines below).
Every reviewer agent ends its output with a machine-parsed FRAGMENT VERDICT: line. The merged report ends with a VERDICT: line that is the arithmetic sum of the fragment lines — never re-judged, re-graded, or adjusted by the session merging the report. The session that requested the review (and may have authored the plan or code under review) does not get to soften the numbers; if a fragment's counts look wrong, re-run that reviewer, don't override it.
FRAGMENT VERDICT: BLOCKING=<n> ADVISORY=<m>; the merged report ends with VERDICT: BLOCKING=<n> ADVISORY=<m> (sums).FRAGMENT VERDICT: CRITICAL=<n> HIGH=<n> MEDIUM=<n> LOW=<n>; the merged report ends with VERDICT: CRITICAL=<n> HIGH=<n> MEDIUM=<n> LOW=<n> (sums).These lines are consumed by /brief (review gate: loop until BLOCKING=0) and /parallel-feature (garage review gate: pass requires CRITICAL=0 HIGH=0). The verdict line must be the last line of the report.
# Plan Review: [Feature Name]
## Stack: [Backend / Frontend / Full Stack]
## Engineering Requirements
### [Category]
- ⚠️ **[Requirement]**: Why it applies and what the plan should specify
- ✅ **[Requirement]**: Already addressed in the plan
## Business & Product Readiness
### [Pillar Name]
- ⚠️ **[Requirement]**: Why it applies and what the plan should specify
- ✅ **[Requirement]**: Already addressed in the plan
## Security & Threat Model
### [Pillar Name]
- ⚠️ **[Requirement]**: Why it applies and what the plan should specify
- ✅ **[Requirement]**: Already addressed in the plan
## Summary
- **Engineering gaps**: N items
- **Business/product gaps**: N items
- **Security gaps**: N items
- **Already covered**: N items
VERDICT: BLOCKING=<n> ADVISORY=<m>
# Code Review Report
## Stack: [Backend / Frontend / Full Stack]
## Files reviewed: [list or summary]
---
## Production Hardening
[Output from production-hardening-reviewer]
---
## Audit Compliance
[Output from audit-compliance-reviewer]
---
## Security Controls
[Output from security-controls-reviewer]
---
## Analytics Coverage
[Output from analytics-coverage-reviewer]
---
## Summary
- **Critical**: N findings
- **High**: N findings
- **Medium**: N findings
- **Low**: N findings
VERDICT: CRITICAL=<n> HIGH=<n> MEDIUM=<n> LOW=<n>
npx claudepluginhub litisaude/garage --plugin liti-garageGuides creation and editing of skills using test-driven development with pressure scenarios and subagents to verify agent compliance.
Creates platform-native content for X, LinkedIn, TikTok, YouTube, and newsletters from source material. Adapts voice and format per platform while avoiding engagement bait and filler.