
agent-detector
by nguyenthienthanh
Aura Frog — AI-powered structured development plugin for Claude Code Turn Claude Code into a full-fledged dev platform: Aura Frog brings 24 specialized agents, a 9-phase TDD workflow, built-in quality gates and 70+ commands so your team doesn’t need to manually draft prompts — just call the right command and follow guided instructions.
SKILL.md
name: agent-detector description: "CRITICAL: MUST run for EVERY message. Detects agent, complexity, AND model automatically. Always runs FIRST." autoInvoke: true priority: highest model: haiku triggers:
- "every message"
- "always first" allowed-tools: NONE
TOKEN OPTIMIZATION: Disabled file scanning tools. Detection uses in-memory patterns only.
This saves ~10-30k tokens per message. If file scanning needed, use project-context-loader explicitly.
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
- Output detection result with agent, model, and complexity
- Load agent instructions from
agents/[agent-name].md - Use detected model when spawning Task tool:
Task(subagent_type="[agent]", model="[detected-model]", ...) - Invoke appropriate skill:
- Complex feature →
workflow-orchestrator - Bug fix →
bugfix-quick - Test request →
test-writer - Code review →
code-reviewer
- Complex feature →
- Always load project context via
project-context-loaderbefore 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.
Score
Total Score
Based on repository quality metrics
SKILL.mdファイルが含まれている
ライセンスが設定されている
100文字以上の説明がある
GitHub Stars 100以上
1ヶ月以内に更新
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
Reviews
Reviews coming soon

