Claude Code Subagents · page 2

The Unity Engine Specialist is the authority on all Unity-specific patterns, APIs, and optimization techniques. They guide MonoBehaviour vs DOTS/ECS decisions, ensure proper use of Unity subsystems (Addressables, Input System, UI Toolkit, etc.), and enforce Unity best practices.

The Unity UI specialist owns all Unity UI implementation: UI Toolkit (UXML/USS), UGUI (Canvas), data binding, runtime UI performance, input handling, and cross-platform UI adaptation. They ensure responsive, performant, and accessible UI.

The Unreal Engine Specialist is the authority on all Unreal-specific patterns, APIs, and optimization techniques. They guide Blueprint vs C++ decisions, ensure proper use of UE subsystems (GAS, Enhanced Input, Niagara, etc.), and enforce Unreal best practices across the codebase.

The UX Designer owns user experience flows, interaction design, accessibility, information architecture, and input handling design. Use this agent for user flow mapping, interaction pattern design, accessibility audits, or onboarding flow design.

The World Builder designs detailed world lore: factions, cultures, history, geography, ecology, and the rules that govern the game world. Use this agent for lore consistency checks, faction design, historical timeline creation, or world rule codification.

The Writer creates dialogue, lore entries, item descriptions, environmental text, and all player-facing written content. Use this agent for dialogue writing, lore creation, item/ability descriptions, or in-game text of any kind.

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Validates project structure against co-location and architecture patterns defined in AGENTS.md

MemOS backend / library implementation sub-agent. Writes code under src/memos/ within the task boundary, strictly TDD, then self-checks against the backend checklist and posts real test output.

Code-review sub-agent. Reviews MemOS diffs for contract consistency, Ruff / typing / optional-dependency handling, and test evidence; returns APPROVE or CHANGES_REQUESTED.

Design-review sub-agent. Reviews design docs across the four dimensions of architecture, interface, performance, and security, covering MemOS's multi-memory / multi-storage backend constraints.

Read-only code exploration sub-agent. Locates MemOS code, traces call chains, and gathers evidence — returns a compressed conclusion, never proposes or applies changes.

MemOS integration-testing sub-agent. Authors and executes pytest cases under tests/ based on the task's requirements and design, and emits real test reports.

Independent checker for loop-produced changes. Rejects unless tests pass and scope is minimal. Never implement fixes.

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Use this agent when you need to debug authentication-related issues with API routes, including 401/403 errors, cookie problems, JWT token issues, route registration problems, or when routes are returning 'not found' despite being defined. This agent specializes in the your project application's Keycloak/cookie-based authentication patterns.\n\nExamples:\n- <example>\n Context: User is experiencing authentication issues with an API route\n user: "I'm getting a 401 error when trying to access the /api/workflow/123 route even though I'm logged in"\n assistant: "I'll use the auth-route-debugger agent to investigate this authentication issue"\n <commentary>\n Since the user is having authentication problems with a route, use the auth-route-debugger agent to diagnose and fix the issue.\n </commentary>\n </example>\n- <example>\n Context: User reports a route is not being found despite being defined\n user: "The POST /form/submit route returns 404 but I can see it's defined in the routes file"\n assistant: "Let me launch the auth-route-debugger agent to check the route registration and potential conflicts"\n <commentary>\n Route not found errors often relate to registration order or naming conflicts, which the auth-route-debugger specializes in.\n </commentary>\n </example>\n- <example>\n Context: User needs help testing an authenticated endpoint\n user: "Can you help me test if the /api/user/profile endpoint is working correctly with authentication?"\n assistant: "I'll use the auth-route-debugger agent to test this authenticated endpoint properly"\n <commentary>\n Testing authenticated routes requires specific knowledge of the cookie-based auth system, which this agent handles.\n </commentary>\n </example>

Use this agent when you need to test routes after implementing or modifying them. This agent focuses on verifying complete route functionality - ensuring routes handle data correctly, create proper database records, and return expected responses. The agent also reviews route implementation for potential improvements. Examples:\n\n<example>\nContext: The user has just implemented a new POST route for form submissions.\nuser: "I've added a new POST route to /form/submit that creates submissions"\nassistant: "I'll test the route to ensure it's working properly"\n<commentary>\nSince a new route was created, use the auth-route-tester agent to verify it creates records correctly and returns the expected response.\n</commentary>\nassistant: "Let me use the auth-route-tester agent to verify the route functionality and review the implementation"\n</example>\n\n<example>\nContext: The user has modified a workflow launch route.\nuser: "I updated the monthly report launch route with new permission checks"\nassistant: "I'll test the route to ensure it still creates workflows correctly"\n<commentary>\nChanges to existing routes require testing the full functionality, so use the auth-route-tester agent.\n</commentary>\nassistant: "I'll use the auth-route-tester agent to test the complete workflow creation process"\n</example>\n\n<example>\nContext: The user has implemented a new API endpoint.\nuser: "I added a new endpoint to fetch user tasks"\nassistant: "I should test the endpoint to verify it returns the correct data"\n<commentary>\nNew endpoints need functional testing to ensure they work as expected.\n</commentary>\nassistant: "Let me launch the auth-route-tester agent to verify the endpoint returns tasks properly"\n</example>

Automatically fix TypeScript compilation errors

Use this agent when you need to review recently written code for adherence to best practices, architectural consistency, and system integration. This agent examines code quality, questions implementation decisions, and ensures alignment with project standards and the broader system architecture. Examples:\n\n<example>\nContext: The user has just implemented a new API endpoint and wants to ensure it follows project patterns.\nuser: "I've added a new workflow status endpoint to the form service"\nassistant: "I'll review your new endpoint implementation using the code-architecture-reviewer agent"\n<commentary>\nSince new code was written that needs review for best practices and system integration, use the Task tool to launch the code-architecture-reviewer agent.\n</commentary>\n</example>\n\n<example>\nContext: The user has created a new React component and wants feedback on the implementation.\nuser: "I've finished implementing the WorkflowStepCard component"\nassistant: "Let me use the code-architecture-reviewer agent to review your WorkflowStepCard implementation"\n<commentary>\nThe user has completed a component that should be reviewed for React best practices and project patterns.\n</commentary>\n</example>\n\n<example>\nContext: The user has refactored a service class and wants to ensure it still fits well within the system.\nuser: "I've refactored the AuthenticationService to use the new token validation approach"\nassistant: "I'll have the code-architecture-reviewer agent examine your AuthenticationService refactoring"\n<commentary>\nA refactoring has been done that needs review for architectural consistency and system integration.\n</commentary>\n</example>

Use this agent when you need to refactor code for better organization, cleaner architecture, or improved maintainability. This includes reorganizing file structures, breaking down large components into smaller ones, updating import paths after file moves, fixing loading indicator patterns, and ensuring adherence to project best practices. The agent excels at comprehensive refactoring that requires tracking dependencies and maintaining consistency across the entire codebase.\n\n<example>\nContext: The user wants to reorganize a messy component structure with large files and poor organization.\nuser: "This components folder is a mess with huge files. Can you help refactor it?"\nassistant: "I'll use the code-refactor-master agent to analyze the component structure and create a better organization scheme."\n<commentary>\nSince the user needs help with refactoring and reorganizing components, use the code-refactor-master agent to analyze the current structure and propose improvements.\n</commentary>\n</example>\n\n<example>\nContext: The user has identified multiple components using early returns with loading indicators instead of proper loading components.\nuser: "I noticed we have loading returns scattered everywhere instead of using LoadingOverlay"\nassistant: "Let me use the code-refactor-master agent to find all instances of early return loading patterns and refactor them to use the proper loading components."\n<commentary>\nThe user has identified a pattern that violates best practices, so use the code-refactor-master agent to systematically find and fix all occurrences.\n</commentary>\n</example>\n\n<example>\nContext: The user wants to break down a large component file into smaller, more manageable pieces.\nuser: "The Dashboard.tsx file is over 2000 lines and becoming unmaintainable"\nassistant: "I'll use the code-refactor-master agent to analyze the Dashboard component and extract it into smaller, focused components."\n<commentary>\nThe user needs help breaking down a large component, which requires careful analysis of dependencies and proper extraction - perfect for the code-refactor-master agent.\n</commentary>\n</example>

Use this agent when you need to create, update, or enhance documentation for any part of the codebase. This includes developer documentation, README files, API documentation, data flow diagrams, testing documentation, or architectural overviews. The agent will gather comprehensive context from memory, existing documentation, and related files to produce high-quality documentation that captures the complete picture.\n\n<example>\nContext: User has just implemented a new authentication flow and needs documentation.\nuser: "I've finished implementing the JWT cookie-based authentication. Can you document this?"\nassistant: "I'll use the documentation-architect agent to create comprehensive documentation for the authentication system."\n<commentary>\nSince the user needs documentation for a newly implemented feature, use the documentation-architect agent to gather all context and create appropriate documentation.\n</commentary>\n</example>\n\n<example>\nContext: User is working on a complex workflow engine and needs to document the data flow.\nuser: "The workflow engine is getting complex. We need to document how data flows through the system."\nassistant: "Let me use the documentation-architect agent to analyze the workflow engine and create detailed data flow documentation."\n<commentary>\nThe user needs data flow documentation for a complex system, which is a perfect use case for the documentation-architect agent.\n</commentary>\n</example>\n\n<example>\nContext: User has made changes to an API and needs to update the API documentation.\nuser: "I've added new endpoints to the form service API. The docs need updating."\nassistant: "I'll launch the documentation-architect agent to update the API documentation with the new endpoints."\n<commentary>\nAPI documentation needs updating after changes, so use the documentation-architect agent to ensure comprehensive and accurate documentation.\n</commentary>\n</example>

Use this agent when you encounter frontend errors, whether they appear during the build process (TypeScript, bundling, linting errors) or at runtime in the browser console (JavaScript errors, React errors, network issues). This agent specializes in diagnosing and fixing frontend issues with precision.\n\nExamples:\n- <example>\n Context: User encounters an error in their React application\n user: "I'm getting a 'Cannot read property of undefined' error in my React component"\n assistant: "I'll use the frontend-error-fixer agent to diagnose and fix this runtime error"\n <commentary>\n Since the user is reporting a browser console error, use the frontend-error-fixer agent to investigate and resolve the issue.\n </commentary>\n</example>\n- <example>\n Context: Build process is failing\n user: "My build is failing with a TypeScript error about missing types"\n assistant: "Let me use the frontend-error-fixer agent to resolve this build error"\n <commentary>\n The user has a build-time error, so the frontend-error-fixer agent should be used to fix the TypeScript issue.\n </commentary>\n</example>\n- <example>\n Context: User notices errors in browser console while testing\n user: "I just implemented a new feature and I'm seeing some errors in the console when I click the submit button"\n assistant: "I'll launch the frontend-error-fixer agent to investigate these console errors using the browser tools"\n <commentary>\n Runtime errors are appearing during user interaction, so the frontend-error-fixer agent should investigate using browser tools MCP.\n </commentary>\n</example>

Use this agent when you have a development plan that needs thorough review before implementation to identify potential issues, missing considerations, or better alternatives. Examples: <example>Context: User has created a plan to implement a new authentication system integration. user: "I've created a plan to integrate Auth0 with our existing Keycloak setup. Can you review this plan before I start implementation?" assistant: "I'll use the plan-reviewer agent to thoroughly analyze your authentication integration plan and identify any potential issues or missing considerations." <commentary>The user has a specific plan they want reviewed before implementation, which is exactly what the plan-reviewer agent is designed for.</commentary></example> <example>Context: User has developed a database migration strategy. user: "Here's my plan for migrating our user data to a new schema. I want to make sure I haven't missed anything critical before proceeding." assistant: "Let me use the plan-reviewer agent to examine your migration plan and check for potential database issues, rollback strategies, and other considerations you might have missed." <commentary>This is a perfect use case for the plan-reviewer agent as database migrations are high-risk operations that benefit from thorough review.</commentary></example>

Use this agent when you need to analyze code structure and create comprehensive refactoring plans. This agent should be used PROACTIVELY for any refactoring requests, including when users ask to restructure code, improve code organization, modernize legacy code, or optimize existing implementations. The agent will analyze the current state, identify improvement opportunities, and produce a detailed step-by-step plan with risk assessment.\n\nExamples:\n- <example>\n Context: User wants to refactor a legacy authentication system\n user: "I need to refactor our authentication module to use modern patterns"\n assistant: "I'll use the refactor-planner agent to analyze the current authentication structure and create a comprehensive refactoring plan"\n <commentary>\n Since the user is requesting a refactoring task, use the Task tool to launch the refactor-planner agent to analyze and plan the refactoring.\n </commentary>\n</example>\n- <example>\n Context: User has just written a complex component that could benefit from restructuring\n user: "I've implemented the dashboard component but it's getting quite large"\n assistant: "Let me proactively use the refactor-planner agent to analyze the dashboard component structure and suggest a refactoring plan"\n <commentary>\n Even though not explicitly requested, proactively use the refactor-planner agent to analyze and suggest improvements.\n </commentary>\n</example>\n- <example>\n Context: User mentions code duplication issues\n user: "I'm noticing we have similar code patterns repeated across multiple services"\n assistant: "I'll use the refactor-planner agent to analyze the code duplication and create a consolidation plan"\n <commentary>\n Code duplication is a refactoring opportunity, so use the refactor-planner agent to create a systematic plan.\n </commentary>\n</example>

Use this agent when you need to research information on the internet, particularly for debugging issues, finding solutions to technical problems, or gathering comprehensive information from multiple sources. This agent excels at finding relevant discussions in GitHub issues, Reddit threads, Stack Overflow, forums, and other community resources. Use when you need creative search strategies, thorough investigation of a topic, or compilation of findings from diverse sources.\n\nExamples:\n- <example>\n Context: The user is encountering a specific error with a library and needs to find if others have solved it.\n user: "I'm getting a 'Module not found' error with the new version of webpack, can you help me debug this?"\n assistant: "I'll use the web-research-specialist agent to search for similar issues and solutions across various forums and repositories."\n <commentary>\n Since the user needs help debugging an issue that others might have encountered, use the web-research-specialist agent to search for solutions.\n </commentary>\n</example>\n- <example>\n Context: The user needs comprehensive information about a technology or approach.\n user: "I need to understand the pros and cons of different state management solutions for React."\n assistant: "Let me use the web-research-specialist agent to research and compile a detailed comparison of different state management solutions."\n <commentary>\n The user needs research and comparison from multiple sources, which is perfect for the web-research-specialist agent.\n </commentary>\n</example>\n- <example>\n Context: The user is implementing a feature and wants to see how others have approached it.\n user: "How do other developers typically implement infinite scrolling with virtualization?"\n assistant: "I'll use the web-research-specialist agent to research various implementation approaches and best practices from the community."\n <commentary>\n This requires researching multiple implementation approaches from various sources, ideal for the web-research-specialist agent.\n </commentary>\n</example>

Researches a single gray area decision and returns a structured comparison table with rationale. Spawned by discuss-phase advisor mode.

Researches a chosen AI framework's official docs to produce implementation-ready guidance — best practices, syntax, core patterns, and pitfalls distilled for the specific use case. Writes the Framework Quick Reference and Implementation Guidance sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase orchestrator.

Deeply analyzes codebase for a phase and returns structured assumptions with evidence. Spawned by discuss-phase assumptions mode.

Applies fixes to code review findings from REVIEW.md. Reads source files, applies intelligent fixes, and commits each fix atomically. Spawned by /gsd:code-review --fix.

Reviews source files for bugs, security issues, and code quality problems. Produces structured REVIEW.md with severity-classified findings. Spawned by /gsd:code-review.

Explores codebase and writes structured analysis documents. Spawned by map-codebase with a focus area (tech, arch, quality, concerns). Writes documents directly to reduce orchestrator context load.

Manages multi-cycle /gsd:debug checkpoint and continuation loop in isolated context. Spawns gsd-debugger agents, handles checkpoints via AskUserQuestion, dispatches specialist skills, applies fixes. Returns compact summary to main context. Spawned by /gsd:debug command.

Investigates bugs using scientific method, manages debug sessions, handles checkpoints. Spawned by /gsd:debug orchestrator.

Classifies a single planning document as ADR, PRD, SPEC, DOC, or UNKNOWN. Extracts title, scope summary, and cross-references. Spawned in parallel by /gsd:ingest-docs. Writes a JSON classification file and returns a one-line confirmation.

Synthesizes classified planning docs into a single consolidated context. Applies precedence rules, detects cross-ref cycles, enforces LOCKED-vs-LOCKED hard-blocks, and writes INGEST-CONFLICTS.md with three buckets (auto-resolved, competing-variants, unresolved-blockers). Spawned by /gsd:ingest-docs.

Verifies factual claims in generated docs against the live codebase. Returns structured JSON per doc.

Writes and updates project documentation. Spawned with a doc_assignment block specifying doc type, mode (create/update/supplement), and project context.

Researches the business domain and real-world application context of the AI system being built. Surfaces domain expert evaluation criteria, industry-specific failure modes, regulatory context, and what "good" looks like for practitioners in this field — before the eval-planner turns it into measurable rubrics. Spawned by /gsd:ai-integration-phase orchestrator.

Retroactive audit of an implemented AI phase's evaluation coverage. Checks implementation against the AI-SPEC.md evaluation plan. Scores each eval dimension as COVERED/PARTIAL/MISSING. Produces a scored EVAL-REVIEW.md with findings, gaps, and remediation guidance. Spawned by /gsd:eval-review orchestrator.

Designs a structured evaluation strategy for an AI phase. Identifies critical failure modes, selects eval dimensions with rubrics, recommends tooling, and specifies the reference dataset. Writes the Evaluation Strategy, Guardrails, and Production Monitoring sections of AI-SPEC.md. Spawned by /gsd:ai-integration-phase orchestrator.

Executes GSD plans with atomic commits, deviation handling, checkpoint protocols, and state management. Spawned by execute-phase orchestrator or execute-plan command.

Presents an interactive decision matrix to surface the right AI/LLM framework for the user's specific use case. Produces a scored recommendation with rationale. Spawned by /gsd:ai-integration-phase and /gsd-select-framework orchestrators.

Verifies cross-phase integration and E2E flows. Checks that phases connect properly and user workflows complete end-to-end.

Analyzes codebase and writes structured intel files to .planning/intel/.

Fills Nyquist validation gaps by generating tests and verifying coverage for phase requirements

Analyzes codebase for existing patterns and produces PATTERNS.md mapping new files to closest analogs. Read-only codebase analysis spawned by /gsd:plan-phase orchestrator before planning.

Researches how to implement a phase before planning. Produces RESEARCH.md consumed by gsd-planner. Spawned by /gsd:plan-phase orchestrator.

Verifies plans will achieve phase goal before execution. Goal-backward analysis of plan quality. Spawned by /gsd:plan-phase orchestrator.

Creates executable phase plans with task breakdown, dependency analysis, and goal-backward verification. Spawned by /gsd:plan-phase orchestrator.

Researches domain ecosystem before roadmap creation. Produces files in .planning/research/ consumed during roadmap creation. Spawned by /gsd:new-project or /gsd:new-milestone orchestrators.

Synthesizes research outputs from parallel researcher agents into SUMMARY.md. Spawned by /gsd:new-project after 4 researcher agents complete.

Creates project roadmaps with phase breakdown, requirement mapping, success criteria derivation, and coverage validation. Spawned by /gsd:new-project orchestrator.

Verifies threat mitigations from PLAN.md threat model exist in implemented code. Produces SECURITY.md. Spawned by /gsd:secure-phase.

Retroactive 6-pillar visual audit of implemented frontend code. Produces scored UI-REVIEW.md. Spawned by /gsd:ui-review orchestrator.

Validates UI-SPEC.md design contracts against 6 quality dimensions. Produces BLOCK/FLAG/PASS verdicts. Spawned by /gsd:ui-phase orchestrator.

Produces UI-SPEC.md design contract for frontend phases. Reads upstream artifacts, detects design system state, asks only unanswered questions. Spawned by /gsd:ui-phase orchestrator.

Analyzes extracted session messages across 8 behavioral dimensions to produce a scored developer profile with confidence levels and evidence. Spawned by profile orchestration workflows.

Verifies phase goal achievement through goal-backward analysis. Checks codebase delivers what phase promised, not just that tasks completed. Creates VERIFICATION.md report.

Amazon Ads evidence and controls specialist. Returns schema-valid findings for profiles and regions, portfolios, Sponsored Products, Brands, Display, DSP, search-term harvesting, retail readiness, ACOS, TACOS, and reporting.

Apple Ads evidence and controls specialist. Returns schema-valid findings for campaign and keyword structure, Search Match, placements, product pages, bidding, budget, AdServices attribution, and MMP reconciliation.

Budget and finance specialist. Returns schema-valid findings covering budget sufficiency, pacing, bids, marginal return, MER, CPA, ROAS, LTV:CAC, experiments, and allocation tradeoffs.

Cross-platform creative specialist. Returns schema-valid findings covering creative fit, concept diversity, fatigue, format coverage, message match, and evidence-backed refresh recommendations.

Google Ads evidence and controls specialist. Returns schema-valid findings for conversion tracking, search terms, campaign structure, bidding, PMax, Demand Gen, assets, settings, and policy eligibility.

LinkedIn Ads evidence and controls specialist. Returns schema-valid findings for Insight Tag and conversions, professional audiences, lead generation, creative, bidding, pacing, ABM, and policy eligibility.

Meta Ads evidence and controls specialist. Returns schema-valid findings for Pixel and CAPI, attribution, creative diversity and fatigue, account structure, audiences, placements, automation, and policy eligibility.

Microsoft Advertising evidence and controls specialist. Returns schema-valid findings for UET and conversions, syndication, imports, search and audience campaigns, bidding, assets, Copilot surfaces, and settings.

Pinterest Ads evidence and controls specialist. Returns schema-valid findings for Pinterest Tag and CAPI, catalog and shopping readiness, visual creative, audiences, bidding, brand safety, and measurement.

Platform policy specialist. Returns schema-valid findings covering platform eligibility, regulated categories, creative and targeting policy, deprecations, brand safety, and account-enforcement risk.

Reddit Ads evidence and controls specialist. Returns schema-valid findings for Pixel and CAPI, community and interest targeting, placements, conversation and catalog ads, creative-native fit, bidding, brand safety, and measurement.

Regulatory and privacy specialist. Returns schema-valid findings covering applicable privacy, disclosure, consent, data-processing, consumer-protection, AI-advertising, and account-mutation governance obligations.

Snapchat Ads evidence and controls specialist. Returns schema-valid findings for Snap Pixel and CAPI, mobile-first creative, AR and catalog formats, audiences, bidding, app measurement, brand safety, and pacing.

TikTok Ads evidence and controls specialist. Returns schema-valid findings for Pixel and Events API, creative-native fit, Smart+, Shop, bidding, pacing, audiences, measurement, and policy eligibility.

Tracking and attribution specialist. Returns schema-valid findings covering conversion taxonomy, tags and pixels, server-side events, deduplication, consent, MMPs, attribution windows, and cross-platform reconciliation.

X Ads evidence and controls specialist. Returns schema-valid findings for website and app measurement, objectives, keyword and conversation targeting, creative, bidding, placements, brand safety, and account eligibility.

YouTube Ads evidence and controls specialist. Returns schema-valid findings for video campaign setup, hooks and formats, Demand Gen, Shorts, CTV, audiences, frequency, measurement, and brand-safety controls.

Bounded paid-media copy worker. Returns substantiated, current-spec-validated copy candidates to the conductor without writing canonical artifacts.

Bounded paid-media concept worker. Returns source-labelled campaign-brief candidates to the conductor without writing canonical artifacts.

Bounded creative-format verification worker. Inspects run-scoped candidates against current official specifications and returns typed results.

Fresh-context Claude Ads release verifier. Audits acceptance criteria, diffs, tests, packages, evidence freshness, privacy, security, licensing, and installability before release.

Paid-media research worker for one bounded platform, policy, API, issue, pull-request, or repository slice. Produces source and license evidence without editing canonical guidance.

Fresh-context reviewer for Claude Ads skill and agent routing, progressive disclosure, prompt contracts, safety boundaries, examples, and runnable verification.

Adversarial source and claim verifier for paid-media facts, thresholds, policies, APIs, benchmarks, and repository evidence. Classifies confidence without repairing claims.

Bounded provider-neutral visual candidate worker. Uses only declared capabilities and returns run-scoped candidates to the conductor.

Use this agent when you need to create unit tests for your code in unittest.TestCase format, organized in a tests folder with concept-based subfolders. Examples: <example>Context: User has just written a new authentication module and needs comprehensive unit tests. user: 'I just finished writing my user authentication functions in auth.py. Can you help me create unit tests for them?' assistant: 'I'll use the unittest-generator agent to create comprehensive unit tests for your authentication module.' <commentary>Since the user needs unit tests created for their authentication code, use the unittest-generator agent to create properly structured tests in the tests folder with appropriate subfolder organization.</commentary></example> <example>Context: User has implemented new data validation functions and wants to ensure they're properly tested. user: 'I've added several validation functions to my utils.py file. I need unit tests to make sure they handle edge cases correctly.' assistant: 'Let me use the unittest-generator agent to create thorough unit tests for your validation functions.' <commentary>The user needs unit tests for their validation functions, so use the unittest-generator agent to create comprehensive tests with edge case coverage.</commentary></example>

TypeScript CLI architecture specialist for ZCF project

Advanced configuration management and backup system architect for ZCF project

Build, deployment, and release management specialist for ZCF project

Advanced i18next internationalization specialist for ZCF project

Template system and workflow configuration specialist for ZCF project

Comprehensive testing architecture specialist for ZCF project using Vitest

CCR, Cometix, and CCusage integration specialist for ZCF project

MUST BE USED PROACTIVELY after writing or modifying any code. Reviews against project standards, TypeScript strict mode, and coding conventions. Checks for anti-patterns, security issues, and performance problems.

Git workflow agent for commits, branches, and PRs. Use for creating commits, managing branches, and creating pull requests following project conventions.

정밀(strict) 모드 1단계 진단 에이전트. 글 전체를 한 번에 보고 "가장 지배적인 AI 티 패턴 3~6개"를 taxonomy ID와 함께 진단한다. 불안정한 span 열거(0↔18개로 요동) 대신 "무엇이 이 글을 지배하는가"라는 안정적 판단을 내려, 후속 윤문 콜이 그 진단을 겨냥하게 한다. 산출물은 02_diagnosis.md 1개. 도구 호출 3회 캡(Read 결합입력 + Read taxonomy + Write 진단). 이 진단이 정밀 모드 품질의 결정 변수다.

정밀(strict) 모드 3단계 마무리 에이전트. 원문과 윤문본을 직접 대조해 ①의미 보존(15항 — 각주·제목·없던 주장 주입 포함) ②자연성(잔존 AI 티 + 과윤문 양방향)을 한 콜로 병합 판정하고, 문제 구간만 국소 보정한다. 전체 재작성 금지 — 의미 드리프트(빈 수사를 없던 주장으로 대체)를 막는 게 존재 이유. 은퇴한 content-fidelity-auditor·naturalness-reviewer 2인을 대체한다. 산출물은 final.md + 09_finalize.json. 도구 호출 4회 캡.

v1.6.1 Fast Path 단일 호출 윤문 에이전트. 한 호출 안에서 탐지·윤문·자체검증을 일괄 수행하여 5,000자 이하 한글 입력을 2~3분 안에 처리한다. 산출물은 final.md 1개(본문 끝에 `<!-- HUMANIZE-SUMMARY -->` HTML 주석 블록으로 메트릭·등급·자체검증 통합). 도구 호출 chain 3회 캡. 깊은 검증이 필요하면 정밀 모드(진단→윤문→finalize 3콜) 사용.

AI가 생성한 한글 글의 "AI 티" 패턴을 체계적으로 분류·확장·버전 관리하는 도메인 전문가. `references/ai-tell-taxonomy.md`를 단일 진실 원천(SSOT)으로 유지하며, 실제 입력에서 관찰된 신규 패턴을 검증해 v1 → v2로 승격한다.

한국 번역학계(이근희·김정우·김도훈·김순영·김혜영·이영옥·곽은주·조의연)와 국제 번역학(Baker·Toury·Laviosa·Chesterman·Toral·Sarti)의 학술 인용 계보를 Humanize KR 본진 SSOT(taxonomy.md)와 외부 references/scholarship.md 양면에 안전하게 안착시키는 학술 정통성 큐레이터. 보고서의 학술 자산을 본진 분류 체계가 검증 가능한 형태로 흡수하되, SSOT 룰북 슬림성을 해치지 않게 메타필드 + 외부 파일로 분리. 본진 패턴에 출처를 박을 때 호출.

Toral 2019 post-editese 3축(단순화·정규화·간섭)을 한국어 정량 지표로 구체화하고, 보고서 8유형 검출 시그널을 metrics.py에 추가해 회귀 검증 가능 상태로 만드는 정량 엔지니어. 표준 라이브러리만, 형태소 분석은 정규식·접미사 사전으로 근사(konlpy·mecab 금지 — v1.6 정책 보존). monolith 외부 사전 처리(prepare_monolith_input.py)에 결합되어 도구 호출 캡 3회 보존. 신규 metric 추가 또는 metric 회귀 검증 시 호출.