agent-detector（Agent 探测器）

Aura Frog Agent Detector

Priority: HIGHEST - Runs FIRST for every message Version: 3.0.0

When to Use

ALWAYS - Every user message, no exceptions.

Auto-Complexity Detection

AI auto-detects task complexity. User doesn't need :fast or :hard variants.

Complexity Levels

complexity[3]{level,criteria,approach}:
  Quick,Single file/Simple fix/Clear scope,Direct implementation - skip research
  Standard,2-5 files/Feature add/Some unknowns,Scout first then implement
  Deep,6+ files/Architecture/Vague scope,Research + plan + implement

Auto-Detection Criteria

Quick (1-2 tool calls):

• Typo fix, single variable rename
• Add console.log/debugging
• Simple CSS change
• Clear file path given
• "Just do X" explicit instruction

Standard (3-6 tool calls):

• New component/function
• Bug fix with clear error
• API endpoint addition
• File modification with tests

Deep (7+ tool calls, use plan mode):

• New feature across multiple files
• Refactoring/architecture change
• Vague requirements ("make it better")
• Security audit
• Performance optimization
• User asks to "plan" or "design"

Detection Logic

1. Count mentioned files/components
2. Check for vague vs specific language
3. Detect scope modifiers (all, entire, every)
4. Check for research keywords (how, why, best way)
5. Assign complexity level

Model Selection

Auto-select model based on task complexity and agent type.

Model Mapping

model_selection[3]{model,when_to_use,agents}:
  haiku,Quick tasks/Simple queries/Orchestration,pm-operations-orchestrator/project-detector/voice-operations
  sonnet,Standard implementation/Coding/Testing/Bug fixes,All dev agents/qa-automation/ui-designer
  opus,Architecture/Deep analysis/Security audits/Complex planning,security-expert (audits)/Any agent (architecture mode)

Complexity → Model

complexity_model[3]{complexity,default_model,override_to_opus}:
  Quick,haiku,Never
  Standard,sonnet,User asks for architecture/design
  Deep,sonnet,Always consider opus for planning phase

Task Type → Model

task_model[8]{task_type,model,reason}:
  Typo fix / config change,haiku,Minimal reasoning needed
  Bug fix / feature add,sonnet,Standard implementation
  API endpoint / component,sonnet,Standard implementation
  Test writing,sonnet,Requires code understanding
  Code review,sonnet,Pattern matching + analysis
  Architecture design,opus,Complex trade-off analysis
  Security audit,opus,Deep vulnerability analysis
  Refactoring / migration,opus,Cross-cutting impact analysis

Agent Default Models

agent_models[11]{agent,default_model,opus_when}:
  pm-operations-orchestrator,haiku,Never (orchestration only)
  project-manager,haiku,Never (detection/context loading)
  smart-agent-detector,haiku,Never (routing only)
  architect,sonnet,Schema design / migration planning / system architecture
  ui-expert,sonnet,Design system architecture
  mobile-expert,sonnet,Architecture decisions
  game-developer,sonnet,Game architecture decisions
  security-expert,sonnet,opus for full audits
  qa-automation,sonnet,Never
  devops-cicd,sonnet,Infrastructure architecture
  voice-operations,haiku,Never (notifications only)

Model Selection Output

Include in detection result:

## Detection Result
- **Agent:** backend-nodejs
- **Model:** sonnet
- **Complexity:** Standard
- **Reason:** API endpoint implementation

When spawning Task tool, use the detected model:

Task(subagent_type="backend-nodejs", model="sonnet", ...)

Multi-Layer Detection System

Layer 0: Task Content Analysis (NEW - Highest Priority)

Analyze the actual task, not just the repo. A backend repo may have frontend tasks (templates, PDFs, emails).

Full patterns: task-based-agent-selection.md

task_content_triggers[7]{category,example_patterns,activates,score_boost}:
  Frontend,html template/blade/twig/email template/pdf styling/css,ui-expert,+50 to +60
  Backend,api endpoint/controller/middleware/queue job/webhook,architect (+ framework skill),+50 to +55
  Database,migration/schema/query optimization/slow query/n+1,architect,+55 to +60
  Security,xss/sql injection/csrf/vulnerability/auth bypass,security-expert,+55 to +60
  DevOps,docker/kubernetes/ci-cd/terraform/deployment,devops-cicd,+50 to +55
  Testing,unit test/e2e test/coverage/mock/fixture,qa-automation,+45 to +55
  Design,figma/wireframe/design system/accessibility,ui-expert,+50 to +60

Key insight: Task content score ≥50 → Override or co-lead with repo-based agent.

Examples:

# Backend repo, but frontend task
Repo: Laravel API
Task: "Fix email template styling"
→ ui-expert (PRIMARY) + architect (SECONDARY)

# Frontend repo, but backend task
Repo: Next.js
Task: "Add rate limiting to API route"
→ architect (PRIMARY) + ui-expert (SECONDARY)

Layer 1: Explicit Technology Detection

Check if user directly mentions a technology:

tech_detection[10]{technology,keywords,agent,score}:
  React Native,react-native/expo/RN,mobile-react-native,+60
  Flutter,flutter/dart/bloc,mobile-flutter,+60
  Angular,angular/ngrx/rxjs,web-angular,+60
  Vue.js,vue/vuejs/pinia/nuxt,web-vuejs,+60
  React,react/reactjs/jsx,web-reactjs,+60
  Next.js,next/nextjs/ssr/ssg,web-nextjs,+60
  Node.js,nodejs/express/nestjs/fastify,backend-nodejs,+60
  Python,python/django/fastapi/flask,backend-python,+60
  Go,go/golang/gin/fiber,backend-go,+60
  Laravel,laravel/php/eloquent/artisan,backend-laravel,+60

Layer 2: Intent Detection Patterns

Detect user intent from action keywords:

intent_detection[8]{intent,keywords,primary,secondary}:
  Implementation,implement/create/add/build/develop,Dev agent,ui-designer/qa-automation
  Bug Fix,fix/bug/error/issue/broken/crash,Dev agent,qa-automation
  Testing,test/testing/coverage/QA/spec,qa-automation,Dev agent
  Design/UI,design/UI/UX/layout/figma/style,ui-designer,Dev agent
  Database,database/schema/query/migration/SQL,database-specialist,Backend agent
  Security,security/vulnerability/audit/owasp/secure,security-expert,Dev agent
  Performance,performance/slow/optimize/speed/memory,devops-cicd,Dev agent
  Deployment,deploy/docker/kubernetes/CI-CD/pipeline,devops-cicd,-

Layer 3: Project Context Detection

Read project files to infer tech stack:

project_detection[10]{file,indicates,agent,score}:
  app.json (with expo),React Native,mobile-react-native,+40
  pubspec.yaml,Flutter,mobile-flutter,+40
  angular.json,Angular,web-angular,+40
  *.vue files,Vue.js,web-vuejs,+40
  next.config.js,Next.js,web-nextjs,+40
  package.json + react (no next),React,web-reactjs,+40
  package.json + express/nestjs,Node.js,backend-nodejs,+40
  requirements.txt/pyproject.toml,Python,backend-python,+40
  go.mod/go.sum,Go,backend-go,+40
  artisan/composer.json + laravel,Laravel,backend-laravel,+40

Layer 4: File Pattern Detection

Check recent files and naming conventions:

file_patterns[9]{pattern,agent,score}:
  *.phone.tsx/*.tablet.tsx,mobile-react-native,+20
  *.dart/lib/ folder,mobile-flutter,+20
  *.component.ts/*.service.ts,web-angular,+20
  *.vue,web-vuejs,+20
  app/route.ts (Next.js),web-nextjs,+20
  *.controller.ts/*.module.ts,backend-nodejs,+20
  views.py/models.py,backend-python,+20
  *.go,backend-go,+20
  *Controller.php/*Model.php,backend-laravel,+20

Scoring Weights

weights[9]{criterion,weight,description}:
  Task Content Match,+50-60,Task-based patterns override repo (Layer 0) - HIGHEST PRIORITY
  Explicit Mention,+60,User directly mentions technology
  Keyword Exact Match,+50,Direct keyword match to intent
  Project Context,+40,CWD/file structure/package files
  Semantic Match,+35,Contextual/implied match
  Task Complexity,+30,Inferred complexity level
  Conversation History,+25,Previous context/active agents
  File Patterns,+20,Recent files/naming conventions
  Project Priority Bonus,+25,Agent in project-config.yaml priority list

Task Content Override Rule: When task content score ≥50 for a different domain than the repo, that domain's agent becomes PRIMARY or co-PRIMARY.

Agent Thresholds

thresholds[4]{level,score,role}:
  Primary Agent,≥80,Leads the task
  Secondary Agent,50-79,Supporting role
  Optional Agent,30-49,May assist
  Not Activated,<30,Not selected

QA Agent Conditional Activation

qa-automation is ALWAYS Secondary when:

• Intent = Implementation (+30 pts as secondary)
• Intent = Bug Fix (+35 pts as secondary)
• New feature being created
• Code modification requested

qa-automation is Primary when:

• Intent = Testing (keywords: test, coverage, QA)
• User explicitly asks for tests
• Coverage report requested

qa-automation is SKIPPED when:

• Pure documentation task
• Pure design discussion (no code)
• Research/exploration only

Detection Process

Step 0: Task Content Analysis (NEW - Do This First!)

Analyze the task itself before checking the repo.

User: "Update the invoice PDF layout - table breaks across pages"

Task Analysis:
- "PDF" → Frontend task pattern (+50)
- "layout" → Frontend keyword (+40)
- "table" → Frontend keyword (+30)
→ Total frontend score: 120 pts → web-expert is PRIMARY

Even if repo is pure backend, web-expert leads this task!

Apply patterns from: task-based-agent-selection.md

Step 1: Extract Keywords

User: "Fix the login button not working on iOS"

Extracted:
- Action: "fix" → Bug Fix intent
- Component: "login button" → UI element
- Platform: "iOS" → Mobile
- Issue: "not working" → Bug context

Step 2: Check Project Context (Use Cached Detection!)

IMPORTANT: Use cached project detection to avoid re-scanning every task.

# 1. Check detection first (fast path):
.claude/project-contexts/[project-name]/project-detection.json

# 2. If detection valid (< 24h, key files unchanged):
   → Use cached: framework, agents, testInfra, filePatterns

# 3. If detection invalid or missing:
   → Run full detection (reads package.json, etc.)
   → Save to project-contexts for next task

# 4. Load project-specific overrides:
.claude/project-contexts/[project]/project-config.yaml
.claude/project-contexts/[project]/conventions.md

Detection invalidation triggers:

• Key config files changed (package.json mtime/size)
• Detection older than 24 hours
• User runs /project:refresh

Commands:

• /project:status - Show project detection
• /project:refresh - Force fresh scan

Step 3: Score All Agents (Combine Task + Repo)

mobile-react-native:
  - "iOS" keyword: +35 (semantic)
  - CWD = /mobile-app: +40 (context)
  - Recent *.phone.tsx: +20 (file pattern)
  → Total: 95 pts ✅ PRIMARY

qa-automation:
  - Bug fix intent: +35 (secondary for bugs)
  → Total: 35 pts ✅ OPTIONAL

ui-designer:
  - "button" keyword: +20 (UI element)
  → Total: 20 pts ❌ NOT SELECTED

Step 4: Select Agents

• Primary: Highest score ≥80
• Secondary: Score 50-79
• Optional: Score 30-49

Step 5: Show Banner

See: rules/agent-identification-banner.md for official format.

Single Agent Banner:

⚡ 🐸 AURA FROG v1.2.0 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
┃ Agent: [agent-name] │ Phase: [phase] - [name]          ┃
┃ Model: [model] │ 🔥 [aura-message]                      ┃
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Multi-Agent Banner (when collaboration needed):

⚡ 🐸 AURA FROG v1.2.0 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
┃ Agents: [primary] + [secondary], [tertiary]            ┃
┃ Phase: [phase] - [name] │ 🔥 [aura-message]            ┃
┃ Model: [model]                                         ┃
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Available Agents

agents[4]{category,count,list}:
  Development,4,architect/ui-expert/mobile-expert/game-developer
  Quality & Security,2,security-expert/qa-automation
  DevOps & Operations,2,devops-cicd/voice-operations
  Infrastructure,3,smart-agent-detector/pm-operations-orchestrator/project-manager

Examples

Example 1: Explicit Technology Mention

User: "Create a React Native screen for user profile"

Layer 1 (Explicit): "React Native" → +60
Layer 2 (Intent): "create" → Implementation
Layer 4 (Files): *.phone.tsx present → +20

Detection Result:
  ✅ Agent: mobile-expert (PRIMARY, 80 pts)
  ✅ Model: sonnet
  ✅ Complexity: Standard
  ✅ Secondary: ui-expert (35), qa-automation (30)

Example 2: Context-Based Detection (No Tech Mention)

User: "Fix the login bug"

Layer 2 (Intent): "fix", "bug" → Bug Fix intent
Layer 3 (Context): CWD=/backend-api, composer.json has laravel → +40
Layer 4 (Files): AuthController.php recent → +20

Detection Result:
  ✅ Agent: architect (PRIMARY, 95 pts) + laravel-expert skill
  ✅ Model: sonnet
  ✅ Complexity: Standard
  ✅ Secondary: qa-automation (35)

Example 3: Architecture Task (Uses Opus)

User: "Design the authentication system architecture"

Layer 2 (Intent): "design", "architecture" → Architecture intent
Complexity: Deep (architecture keyword)

Detection Result:
  ✅ Agent: architect (PRIMARY)
  ✅ Model: opus (architecture task)
  ✅ Complexity: Deep
  ✅ Secondary: security-expert (55)

Example 4: Quick Fix (Uses Haiku)

User: "Fix typo in README.md line 42"

Complexity: Quick (single file, explicit location)

Detection Result:
  ✅ Agent: pm-operations-orchestrator
  ✅ Model: haiku
  ✅ Complexity: Quick

Example 5: Backend Repo, Frontend Task (Task-Based Override)

User: "Fix the password reset email template - the button styling is broken"

Repo Context: Laravel API (backend)
Task Content Analysis:
- "email template" → frontend_task_patterns (+55)
- "styling" → frontend_keywords (+40)
- "button" → frontend_keywords (+30)
→ Frontend score: 125 pts (OVERRIDE)

Detection Result:
  ✅ Agent: ui-expert (PRIMARY, 125 pts) - leads template fix
  ✅ Agent: architect (SECONDARY, 40 pts) - Blade context + laravel-expert skill
  ✅ Model: sonnet
  ✅ Complexity: Standard

Example 6: Frontend Repo, Database Task (Task-Based Override)

User: "The user list page is slow - optimize the query"

Repo Context: Next.js frontend
Task Content Analysis:
- "slow" → database_task_patterns (+50)
- "optimize" → database context
- "query" → database_task_patterns (+40)
→ Database score: 90 pts (OVERRIDE)

Detection Result:
  ✅ Agent: architect (PRIMARY, 90 pts) - database optimization
  ✅ Agent: ui-expert (SECONDARY, 40 pts) - API route context + nextjs-expert skill
  ✅ Model: sonnet
  ✅ Complexity: Standard

Example 7: Backend Repo, PDF Generation (Task-Based Override)

User: "Invoice PDF has layout issues - table breaks across pages incorrectly"

Repo Context: Node.js API
Task Content Analysis:
- "PDF" → frontend_task_patterns (+50)
- "layout" → frontend_keywords (+40)
- "table" → frontend_keywords (+30)
→ Frontend score: 120 pts (OVERRIDE)

Detection Result:
  ✅ Agent: ui-expert (PRIMARY, 120 pts) - HTML/CSS for PDF
  ✅ Agent: architect (SECONDARY, 40 pts) - PDF library integration + nodejs-expert skill
  ✅ Model: sonnet
  ✅ Complexity: Standard

After Detection

1. Output detection result with agent, model, and complexity
2. Load agent instructions from agents/[agent-name].md
3. Use detected model when spawning Task tool:

   Task(subagent_type="[agent]", model="[detected-model]", ...)

1. Invoke appropriate skill:

- Complex feature → workflow-orchestrator - Bug fix → bugfix-quick - Test request → test-writer - Code review → code-reviewer

1. Always load project context via project-context-loader before major actions

Manual Override

User can force specific agent:

User: "Use only qa-automation for this task"
→ Override automatic selection
→ qa-automation becomes PRIMARY regardless of scoring

Full detection algorithm: agents/smart-agent-detector.md Selection guide: docs/AGENT_SELECTION_GUIDE.md

MANDATORY: Always show agent banner at start of EVERY response.