accessibility-audit
You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct comprehensive audits,…
Orchestrate comprehensive multi-dimensional code review using specialized review agents across architecture, security, performance, testing, and best practices
$ npx -y skills add wshobson/agents --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
/full-reviewContext preview
What this command does when you run it.
Orchestrate comprehensive multi-dimensional code review using specialized review agents across architecture, security, performance, testing, and best practices
description: "Orchestrate comprehensive multi-dimensional code review using specialized review agents across architecture, security, performance, testing, and best practices" argument-hint: "<target path or description> [--security-focus] [--performance-critical] [--strict-mode] [--framework react|spring|django|rails]"
You MUST follow these rules exactly. Violating any of them is a failure.
1. **Execute phases in order.** Do NOT skip ahead, reorder, or merge phases. 2. **Write output files.** Each phase MUST produce its output file in `.full-review/` before the next phase begins. Read from prior phase files -- do NOT rely on context window memory. 3. **Stop at checkpoints.** When you reach a `PHASE CHECKPOINT`, you MUST stop and wait for explicit user approval before continuing. Use the AskUserQuestion tool with clear options. 4. **Halt on failure.** If any step fails (agent error, missing files, access issues), STOP immediately. Present the error and ask the user how to proceed. Do NOT silently continue. 5. **Use only local agents.** All `subagent_type` references use agents bundled with this plugin or `general-purpose`. No cross-plugin dependencies. 6. **Never enter plan mode autonomously.** Do NOT use EnterPlanMode. This command IS the plan -- execute it.
Before starting, perform these checks:
Check if `.full-review/state.json` exists:
Found an in-progress review session: Target: [target from state] Current phase: [phase from state] 1. Resume from where we left off 2. Start fresh (archives existing session)
Create `.full-review/` directory and `state.json`:
{
"target": "$ARGUMENTS",
"status": "in_progress",
"flags": {
"security_focus": false,
"performance_critical": false,
"strict_mode": false,
"framework": null
},
"current_step": 1,
"current_phase": 1,
"completed_steps": [],
"files_created": [],
"started_at": "ISO_TIMESTAMP",
"last_updated": "ISO_TIMESTAMP"
}Parse `$ARGUMENTS` for `--security-focus`, `--performance-critical`, `--strict-mode`, and `--framework` flags. Update the flags object accordingly.
Determine what code to review from `$ARGUMENTS`:
**Output file:** `.full-review/00-scope.md`
# Review Scope ## Target [Description of what is being reviewed] ## Files [List of files/directories included in the review] ## Flags - Security Focus: [yes/no] - Performance Critical: [yes/no] - Strict Mode: [yes/no] - Framework: [name or auto-detected] ## Review Phases 1. Code Quality & Architecture 2. Security & Performance 3. Testing & Documentation 4. Best Practices & Standards 5. Consolidated Report
Update `state.json`: add `"00-scope.md"` to `files_created`, add step 0 to `completed_steps`.
---
Run both agents in parallel using multiple Task tool calls in a single response.
Task:
subagent_type: "comprehensive-review-code-reviewer"
description: "Code quality analysis for $ARGUMENTS"
prompt: |
Perform a comprehensive code quality review.
## Review Scope
[Insert contents of .full-review/00-scope.md]
## Instructions
Analyze the target code for:
1. **Code complexity**: Cyclomatic complexity, cognitive complexity, deeply nested logic
2. **Maintainability**: Naming conventions, function/method length, class cohesion
3. **Code duplication**: Copy-pasted logic, missed abstraction opportunities
4. **Clean Code principles**: SOLID violations, code smells, anti-patterns
5. **Technical debt**: Areas that will become increasingly costly to change
6. **Error handling**: Missing error handling, swallowed exceptions, unclear error messages
For each finding, provide:
- Severity (Critical / High / Medium / Low)
- File and line location
- Description of the issue
- Specific fix recommendation with code example
Write your findings as a structured markdown document.Task:
subagent_type: "comprehensive-review-architect-review"
description: "Architecture review for $ARGUMENTS"
prompt: |
Review the architectural design and structural integrity of the target code.
## Review Scope
[Insert contents of .full-review/00-scope.md]
## Instructions
Evaluate the code for:
1. **Component boundaries**: Proper separation of concerns, module cohesion
2. **Dependency management**: Circular dependencies, inappropriate coupling, dependency direction
3. **API design**: Endpoint design, request/response schemas, error contracts, versioning
4. **Data model**: Schema design, relationships, data access patterns
5. **Design patterns**: Appropriate use of patterns, missing abstractions, over-engineering
6. **Architectural consistency**: Does the code follow the project's established patterns?
For each finding, provide:
- Severity (Critical / High / Medium / Low)
- Architectural impact assessment
- Specific improvement recommendation
Write your findings as a structured markdown document.After both complete, consolidate into `.full-review/01-quality-architecture.md`:
# Phase 1: Code Quality & Architecture Review ## Code Quality Findings [Summary from 1A, organized by severity] ## Architecture Findings [Summa
Production-ready agentic workflow building blocks: 94 plugins, 202 agents, 183 skills, 105 commands — built for Claude Code and consumed natively by OpenAI Codex CLI, Cursor, OpenCode, the Antigravity CLI, GitHub Copilot, and Pi from a single Markdown source.
Repo: wshobson/agents
You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct comprehensive audits,…
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
The Multi-Agent Optimization Tool is an advanced AI-driven framework designed to holistically improve system performance through intelligent, coordinated…
Debug issues using competing hypotheses with parallel investigation by multiple agents
Task delegation dashboard for managing team workload, assignments, and rebalancing
Develop features in parallel with multiple agents using file ownership boundaries and dependency management