# Intent Detection > Automatically detect user intent and route to appropriate agent Use when this capability is needed. - Skill: `tomevault-io/intent-detection` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/intent-detection` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/intent-detection/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/intent-detection --- ## Purpose Automatically detect user intent and route to the appropriate agent with full transparency. ## Detection Algorithm ### 1. Input Analysis ``` User Input: "Go 코드 리뷰해줘" │ ▼ ┌─────────────────────────────┐ │ Tokenize & Extract │ ├─────────────────────────────┤ │ Keywords: ["Go"] │ │ Actions: ["리뷰"] │ │ File refs: [] │ │ Context: [] │ └─────────────────────────────┘ ``` ### 2. Pattern Matching Match extracted tokens against agent triggers: ```yaml # For each agent in agent-triggers.yaml match_score = 0 # Keyword match for keyword in user_keywords: if keyword in agent.keywords: match_score += agent.keyword_weight (default: 40) # Action match for action in user_actions: if action in agent.actions: match_score += agent.action_weight (default: 40) # File pattern match for pattern in user_file_refs: if matches(pattern, agent.file_patterns): match_score += agent.file_weight (default: 30) # Context bonus if agent == recent_agent: match_score += context_bonus (default: 10) ``` ### 3. Confidence Calculation ``` confidence = min(100, match_score) ``` ### 4. Decision ``` if confidence >= 90: auto_execute() elif confidence >= 70: request_confirmation() else: list_options() ``` ## Detection Patterns ### Keyword Patterns ```yaml # Korean keywords korean: - "고" → go - "파이썬" → python - "러스트" → rust - "타입스크립트" → typescript # Action verbs (Korean) actions_kr: - "리뷰" → review - "분석" → analyze - "수정" → fix - "생성" → create - "만들어" → create - "확인" → check ``` ### File Pattern Matching ```yaml patterns: go: ["*.go", "go.mod", "go.sum"] python: ["*.py", "requirements.txt", "pyproject.toml", "setup.py"] rust: ["*.rs", "Cargo.toml"] typescript: ["*.ts", "*.tsx", "tsconfig.json"] kotlin: ["*.kt", "*.kts", "build.gradle.kts"] ``` ## Output Format ### High Confidence ``` [Intent Detected] ├── Input: "Go 코드 리뷰해줘" ├── Agent: lang-golang-expert ├── Confidence: 95% └── Reason: "Go" keyword + "리뷰" action ┌─ Agent: lang-golang-expert (sw-engineer) └─ Task: Code review ``` ### Medium Confidence ``` [Intent Detected] ├── Input: "백엔드 API 확인해줘" ├── Detected: be-go-backend-expert (?) ├── Confidence: 78% └── Alternatives available Select agent: 1. be-go-backend-expert (78%) 2. be-fastapi-expert (72%) 3. be-springboot-expert (68%) Choice [1-3, or agent name]: ``` ### Override ``` [Override Detected] ├── Input: "@lang-python-expert review api.py" ├── Agent: lang-python-expert (explicit) └── Bypassing intent detection ┌─ Agent: lang-python-expert (sw-engineer) └─ Task: Review api.py ``` ## Integration ### With Secretary ``` Secretary uses this skill to: 1. Parse incoming user requests 2. Detect intent and select agent 3. Display reasoning 4. Handle confirmations 5. Route to selected agent ``` ### With Agent Triggers ``` Load triggers from: .claude/skills/intent-detection/patterns/agent-triggers.yaml Each agent defines: - keywords (language names, tech terms) - file_patterns (extensions, config files) - actions (supported actions) - weights (scoring factors) ``` ## Error Handling ### No Match (< 30% confidence) **Generic task** (no identifiable domain): ``` [Intent Unclear] ├── Input: "도와줘" ├── Confidence: < 30% └── Too generic to detect intent How can I help? Please be more specific: - What type of task? (review, create, fix, ...) - What language/technology? (Go, Python, ...) - What file or project? ``` **Specialized task with identifiable domain** (keywords/files detected but no matching agent): ``` [No Matching Agent] ├── Input: "Terraform 모듈 리뷰해줘" ├── Domain: terraform (detected from keywords) ├── Matching Agent: none └── Action: Trigger dynamic agent creation → Delegating to mgr-creator with context: domain: terraform keywords: ["terraform", "모듈"] file_patterns: ["*.tf"] ``` ### Ambiguous Match ``` [Intent Ambiguous] ├── Input: "코드 리뷰" ├── Top matches: │ └── All experts: ~50% each └── Need file context or language hint Specify the language or provide a file path. ``` ## Configuration ```yaml intent_detection: enabled: true auto_execute_threshold: 90 confirm_threshold: 70 show_reasoning: true max_alternatives: 3 korean_support: true ``` ## Research Intent Routing When a research/information gathering intent is detected: ### Detection Keywords ```yaml # Korean korean: - "조사" → research - "검색" → search - "리서치" → research - "탐색" → explore - "찾아" → look up - "알아봐" → find out - "자료" → gather materials - "정보 수집" → gather information # English english: - "research" - "investigate" - "search for" - "look up" - "gather information" ``` ### Routing Logic ``` Research intent detected (confidence >= 70%) ↓ Check Codex CLI availability ├─ Available (codex binary + OPENAI_API_KEY) │ → Use codex-exec skill with --effort xhigh │ → Prompt: "Research and analyze: {user_request}" │ → Returns: structured findings for orchestrator └─ Unavailable → Fall back to Claude's WebFetch/WebSearch → Orchestrator handles directly or via general-purpose agent ``` ### Confidence Scoring | Factor | Weight | Example | |--------|--------|---------| | Research keyword match | +40 | "조사해줘", "research" | | Action verb match | +30 | "찾아", "investigate" | | URL/topic present | +20 | specific URL or topic mentioned | | Context (previous research) | +10 | follow-up research request | ### Output Format ``` [Intent Detected] ├── Input: "{user input}" ├── Workflow: research-workflow ├── Confidence: {percentage}% ├── Method: codex-exec (xhigh) | WebFetch fallback └── Reason: {explanation} ``` --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/baekenough) — claim your Tome and manage your conversions.