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Tencent SkillHub Β· AI
A.I. Smart Router
Expertise-aware model router with semantic domain scoring, context-overflow protection, and security redaction. Automatically selects the optimal AI model using weighted expertise scoring (Feb 2026 benchmarks). Supports Claude, GPT, Gemini, Grok with automatic fallback chains, HITL gates, and cost optimization.
skill openclawclawhub Free
Expertise-aware model router with semantic domain scoring, context-overflow protection, and security redaction. Automatically selects the optimal AI model using weighted expertise scoring (Feb 2026 benchmarks). Supports Claude, GPT, Gemini, Grok with automatic fallback chains, HITL gates, and cost optimization.
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Documentation
A.I. Smart-Router
Intelligently route requests to the optimal AI model using tiered classification with automatic fallback handling and cost optimization.
How It Works (Silent by Default)
The router operates transparentlyβusers send messages normally and get responses from the best model for their task. No special commands needed. Optional visibility: Include [show routing] in any message to see the routing decision.
Tiered Classification System
The router uses a three-tier decision process: βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β TIER 1: INTENT DETECTION β β Classify the primary purpose of the request β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ β CODE β ANALYSIS β CREATIVE β REALTIME β GENERAL β β write/debug β research β writing β news/live β Q&A/chat β β refactor β explain β stories β X/Twitter β translate β β review β compare β brainstorm β prices β summarize β ββββββββ¬ββββββββ΄βββββββ¬βββββββ΄ββββββ¬βββββββ΄ββββββ¬ββββββ΄ββββββ¬ββββββ β β β β β βΌ βΌ βΌ βΌ βΌ βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β TIER 2: COMPLEXITY ESTIMATION β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ β SIMPLE (Tier $) β MEDIUM (Tier $$) β COMPLEX (Tier $$$)β β β’ One-step task β β’ Multi-step task β β’ Deep reasoning β β β’ Short response OK β β’ Some nuance β β’ Extensive outputβ β β’ Factual lookup β β’ Moderate context β β’ Critical task β β β Haiku/Flash β β Sonnet/Grok/GPT β β Opus/GPT-5 β ββββββββββββββββββββββββββββ΄ββββββββββββββββββββββ΄ββββββββββββββββββββ β βΌ βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β TIER 3: SPECIAL CASE OVERRIDES β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ β CONDITION β OVERRIDE TO β β ββββββββββββββββββββββββββββββββββββββΌββββββββββββββββββββββββββ β Context >100K tokens β β Gemini Pro (1M ctx) β β Context >500K tokens β β Gemini Pro ONLY β β Needs real-time data β β Grok (regardless) β β Image/vision input β β Opus or Gemini Pro β β User explicit override β β Requested model β ββββββββββββββββββββββββββββββββββββββββ΄βββββββββββββββββββββββββββ
CODE Intent
Keywords: write, code, debug, fix, refactor, implement, function, class, script, API, bug, error, compile, test, PR, commit File extensions mentioned: .py, .js, .ts, .go, .rs, .java, etc. Code blocks in input
ANALYSIS Intent
Keywords: analyze, explain, compare, research, understand, why, how does, evaluate, assess, review, investigate, examine Long-form questions "Help me understand..."
CREATIVE Intent
Keywords: write (story/poem/essay), create, brainstorm, imagine, design, draft, compose Fiction/narrative requests Marketing/copy requests
REALTIME Intent
Keywords: now, today, current, latest, trending, news, happening, live, price, score, weather X/Twitter mentions Stock/crypto tickers Sports scores
GENERAL Intent (Default)
Simple Q&A Translations Summaries Conversational
MIXED Intent (Multiple Intents Detected)
When a request contains multiple clear intents (e.g., "Write code to analyze this data and explain it creatively"): Identify primary intent β What's the main deliverable? Route to highest-capability model β Mixed tasks need versatility Default to COMPLEX complexity β Multi-intent = multi-step Examples: "Write code AND explain how it works" β CODE (primary) + ANALYSIS β Route to Opus "Summarize this AND what's the latest news on it" β REALTIME takes precedence β Grok "Creative story using real current events" β REALTIME + CREATIVE β Grok (real-time wins)
Language Handling
Non-English requests are handled normally β all supported models have multilingual capabilities: ModelNon-English SupportOpus/Sonnet/HaikuExcellent (100+ languages)GPT-5Excellent (100+ languages)Gemini Pro/FlashExcellent (100+ languages)GrokGood (major languages) Intent detection still works because: Keyword patterns include common non-English equivalents Code intent detected by file extensions, code blocks (language-agnostic) Complexity estimated by query length (works across languages) Edge case: If intent unclear due to language, default to GENERAL intent with MEDIUM complexity.
Simple Complexity ($)
Short query (<50 words) Single question mark "Quick question", "Just tell me", "Briefly" Yes/no format Unit conversions, definitions
Medium Complexity ($$)
Moderate query (50-200 words) Multiple aspects to address "Explain", "Describe", "Compare" Some context provided
Complex Complexity ($$$)
Long query (>200 words) or complex task "Step by step", "Thoroughly", "In detail" Multi-part questions Critical/important qualifier Research, analysis, or creative work
Routing Matrix
IntentSimpleMediumComplexCODESonnetOpusOpusANALYSISFlashGPT-5OpusCREATIVESonnetOpusOpusREALTIMEGrokGrokGrok-3GENERALFlashSonnetOpus
Token Exhaustion & Automatic Model Switching
When a model becomes unavailable mid-session (token quota exhausted, rate limit hit, API error), the router automatically switches to the next best available model and notifies the user.
Notification Format
When a model switch occurs due to exhaustion, the user receives a notification: βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β β οΈ MODEL SWITCH NOTICE β β β β Your request could not be completed on claude-opus-4-5 β β (reason: token quota exhausted). β β β β β Request completed using: anthropic/claude-sonnet-4-5 β β β β The response below was generated by the fallback model. β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Switch Reasons
ReasonDescriptiontoken quota exhaustedDaily/monthly token limit reachedrate limit exceededToo many requests per minutecontext window exceededInput too large for modelAPI timeoutModel took too long to respondAPI errorProvider returned an errormodel unavailableModel temporarily offline
Implementation
def execute_with_fallback(primary_model: str, fallback_chain: list[str], request: str) -> Response: """ Execute request with automatic fallback and user notification. """ attempted_models = [] switch_reason = None # Try primary model first models_to_try = [primary_model] + fallback_chain for model in models_to_try: try: response = call_model(model, request) # If we switched models, prepend notification if attempted_models: notification = build_switch_notification( failed_model=attempted_models[0], reason=switch_reason, success_model=model ) return Response( content=notification + "\n\n---\n\n" + response.content, model_used=model, switched=True ) return Response(content=response.content, model_used=model, switched=False) except TokenQuotaExhausted: attempted_models.append(model) switch_reason = "token quota exhausted" log_fallback(model, switch_reason) continue except RateLimitExceeded: attempted_models.append(model) switch_reason = "rate limit exceeded" log_fallback(model, switch_reason) continue except ContextWindowExceeded: attempted_models.append(model) switch_reason = "context window exceeded" log_fallback(model, switch_reason) continue except APITimeout: attempted_models.append(model) switch_reason = "API timeout" log_fallback(model, switch_reason) continue except APIError as e: attempted_models.append(model) switch_reason = f"API error: {e.code}" log_fallback(model, switch_reason) continue # All models exhausted return build_exhaustion_error(attempted_models) def build_switch_notification(failed_model: str, reason: str, success_model: str) -> str: """Build user-facing notification when model switch occurs.""" return f"""β οΈ **MODEL SWITCH NOTICE** Your request could not be completed on `{failed_model}` (reason: {reason}). β **Request completed using:** `{success_model}` The response below was generated by the fallback model.""" def build_exhaustion_error(attempted_models: list[str]) -> Response: """Build error when all models are exhausted.""" models_tried = ", ".join(attempted_models) return Response( content=f"""β **REQUEST FAILED** Unable to complete your request. All available models have been exhausted. **Models attempted:** {models_tried} **What you can do:** 1. **Wait** β Token quotas typically reset hourly or daily 2. **Simplify** β Try a shorter or simpler request 3. **Check status** β Run `/router status` to see model availability If this persists, your human may need to check API quotas or add additional providers.""", model_used=None, switched=False, failed=True )
Fallback Priority for Token Exhaustion
When a model is exhausted, the router selects the next best model for the same task type: Original ModelFallback Priority (same capability)OpusSonnet β GPT-5 β Grok-3 β Gemini ProSonnetGPT-5 β Grok-3 β Opus β HaikuGPT-5Sonnet β Opus β Grok-3 β Gemini ProGemini ProFlash β GPT-5 β Opus β SonnetGrok-2/3(warn: no real-time fallback available)
User Acknowledgment
After a model switch, the agent should note in the response that: The original model was unavailable Which model actually completed the request The response quality may differ from the original model's typical output This ensures transparency and sets appropriate expectations.
Streaming Responses with Fallback
When using streaming responses, fallback handling requires special consideration: async def execute_with_streaming_fallback(primary_model: str, fallback_chain: list[str], request: str): """ Handle streaming responses with mid-stream fallback. If a model fails DURING streaming (not before), the partial response is lost. Strategy: Don't start streaming until first chunk received successfully. """ models_to_try = [primary_model] + fallback_chain for model in models_to_try: try: # Test with non-streaming ping first (optional, adds latency) # await test_model_availability(model) # Start streaming stream = await call_model_streaming(model, request) first_chunk = await stream.get_first_chunk(timeout=10_000) # 10s timeout for first chunk # If we got here, model is responding β continue streaming yield first_chunk async for chunk in stream: yield chunk return # Success except (FirstChunkTimeout, StreamError) as e: log_fallback(model, str(e)) continue # Try next model # All models failed yield build_exhaustion_error(models_to_try) Key insight: Wait for the first chunk before committing to a model. If the first chunk times out, fall back before any partial response is shown to the user.
Retry Timing Configuration
RETRY_CONFIG = { "initial_timeout_ms": 30_000, # 30s for first attempt "fallback_timeout_ms": 20_000, # 20s for fallback attempts (faster fail) "max_retries_per_model": 1, # Don't retry same model "backoff_multiplier": 1.5, # Not used (no same-model retry) "circuit_breaker_threshold": 3, # Failures before skipping model entirely "circuit_breaker_reset_ms": 300_000 # 5 min before trying failed model again } Circuit breaker: If a model fails 3 times in 5 minutes, skip it entirely for the next 5 minutes. This prevents repeatedly hitting a down service.
Fallback Chains
When the preferred model fails (rate limit, API down, error), cascade to the next option:
Code Tasks
Opus β Sonnet β GPT-5 β Gemini Pro
Analysis Tasks
Opus β GPT-5 β Gemini Pro β Sonnet
Creative Tasks
Opus β GPT-5 β Sonnet β Gemini Pro
Real-time Tasks
Grok-2 β Grok-3 β (warn: no real-time fallback)
General Tasks
Flash β Haiku β Sonnet β GPT-5
Long Context (Tiered by Size)
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β LONG CONTEXT FALLBACK CHAIN β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ β TOKEN COUNT β FALLBACK CHAIN β β ββββββββββββββββββββΌββββββββββββββββββββββββββββββββββββββββββββ β 128K - 200K β Opus (200K) β Sonnet (200K) β Gemini Pro β β 200K - 1M β Gemini Pro β Flash (1M) β ERROR_MESSAGE β β > 1M β ERROR_MESSAGE (no model supports this) β βββββββββββββββββββββββ΄ββββββββββββββββββββββββββββββββββββββββββββ Implementation: def handle_long_context(token_count: int, available_models: dict) -> str | ErrorMessage: """Route long-context requests with graceful degradation.""" # Tier 1: 128K - 200K tokens (Opus/Sonnet can handle) if token_count <= 200_000: for model in ["opus", "sonnet", "haiku", "gemini-pro", "flash"]: if model in available_models and get_context_limit(model) >= token_count: return model # Tier 2: 200K - 1M tokens (only Gemini) elif token_count <= 1_000_000: for model in ["gemini-pro", "flash"]: if model in available_models: return model # Tier 3: > 1M tokens (nothing available) # Fall through to error # No suitable model found β return helpful error return build_context_error(token_count, available_models) def build_context_error(token_count: int, available_models: dict) -> ErrorMessage: """Build a helpful error message when no model can handle the input.""" # Find the largest available context window max_available = max( (get_context_limit(m) for m in available_models), default=0 ) # Determine what's missing missing_models = [] if "gemini-pro" not in available_models and "flash" not in available_models: missing_models.append("Gemini Pro/Flash (1M context)") if token_count <= 200_000 and "opus" not in available_models: missing_models.append("Opus (200K context)") # Format token count for readability if token_count >= 1_000_000: token_display = f"{token_count / 1_000_000:.1f}M" else: token_display = f"{token_count // 1000}K" return ErrorMessage( title="Context Window Exceeded", message=f"""Your input is approximately **{token_display} tokens**, which exceeds the context window of all currently available models. **Required:** Gemini Pro (1M context) {"β currently unavailable" if "gemini-pro" not in available_models else ""} **Your max available:** {max_available // 1000}K tokens **Options:** 1. **Wait and retry** β Gemini may be temporarily down 2. **Reduce input size** β Remove unnecessary content to fit within {max_available // 1000}K tokens 3. **Split into chunks** β I can process your input sequentially in smaller pieces Would you like me to help split this into manageable chunks?""", recoverable=True, suggested_action="split_chunks" ) Example Error Output: β οΈ Context Window Exceeded Your input is approximately **340K tokens**, which exceeds the context window of all currently available models. Required: Gemini Pro (1M context) β currently unavailable Your max available: 200K tokens Options: 1. Wait and retry β Gemini may be temporarily down 2. Reduce input size β Remove unnecessary content to fit within 200K tokens 3. Split into chunks β I can process your input sequentially in smaller pieces Would you like me to help split this into manageable chunks?
Dynamic Model Discovery
The router auto-detects available providers at runtime: 1. Check configured auth profiles 2. Build available model list from authenticated providers 3. Construct routing table using ONLY available models 4. If preferred model unavailable, use best available alternative Example: If only Anthropic and Google are configured: Code tasks β Opus (Anthropic available β) Real-time tasks β β οΈ No Grok β Fall back to Opus + warn user Long docs β Gemini Pro (Google available β)
Cost Optimization
The router considers cost when complexity is LOW: ModelCost TierUse WhenGemini Flash$Simple tasks, high volumeClaude Haiku$Simple tasks, quick responsesClaude Sonnet$$Medium complexityGrok 2$$Real-time needs onlyGPT-5$$General fallbackGemini Pro$$$Long context needsClaude Opus$$$$Complex/critical tasks Rule: Never use Opus ($$$) for tasks that Flash ($) can handle.
Show Routing Decision
Add [show routing] to any message: [show routing] What's the weather in NYC? Output includes: [Routed β xai/grok-2-latest | Reason: REALTIME intent detected | Fallback: none available]
Force Specific Model
Explicit overrides: "use grok: ..." β Forces Grok "use claude: ..." β Forces Opus "use gemini: ..." β Forces Gemini Pro "use flash: ..." β Forces Gemini Flash "use gpt: ..." β Forces GPT-5
Check Router Status
Ask: "router status" or "/router" to see: Available providers Configured models Current routing table Recent routing decisions
For Agent Implementation
When processing a request: 1. DETECT available models (check auth profiles) 2. CLASSIFY intent (code/analysis/creative/realtime/general) 3. ESTIMATE complexity (simple/medium/complex) 4. CHECK special cases (context size, vision, explicit override) 5. FILTER by cost tier based on complexity β BEFORE model selection 6. SELECT model from filtered pool using routing matrix 7. VERIFY model available, else use fallback chain (also cost-filtered) 8. EXECUTE request with selected model 9. IF failure, try next in fallback chain 10. LOG routing decision (for debugging)
Cost-Aware Routing Flow (Critical Order)
def route_with_fallback(request): """ Main routing function with CORRECT execution order. Cost filtering MUST happen BEFORE routing table lookup. """ # Step 1: Discover available models available_models = discover_providers() # Step 2: Classify intent intent = classify_intent(request) # Step 3: Estimate complexity complexity = estimate_complexity(request) # Step 4: Check special-case overrides (these bypass cost filtering) if user_override := get_user_model_override(request): return execute_with_fallback(user_override, []) # No cost filter for explicit override if token_count > 128_000: return handle_long_context(token_count, available_models) # Special handling if needs_realtime(request): return execute_with_fallback("grok-2", ["grok-3"]) # Realtime bypasses cost # βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ # β STEP 5: FILTER BY COST TIER β THIS MUST COME FIRST! β # β β # β Cost filtering happens BEFORE the routing table lookup, β # β NOT after. This ensures "what's 2+2?" never considers β # β Opus even momentarily. β # βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ allowed_tiers = get_allowed_tiers(complexity) # SIMPLE β ["$"] # MEDIUM β ["$", "$$"] # COMPLEX β ["$", "$$", "$$$"] cost_filtered_models = { model: meta for model, meta in available_models.items() if COST_TIERS.get(model) in allowed_tiers } # Step 6: NOW select from cost-filtered pool using routing preferences preferences = ROUTING_PREFERENCES.get((intent, complexity), []) for model in preferences: if model in cost_filtered_models: # Only consider cost-appropriate models selected_model = model break else: # No preferred model in cost-filtered pool β use cheapest available selected_model = select_cheapest(cost_filtered_models) # Step 7: Build cost-filtered fallback chain task_type = get_task_type(intent, complexity) full_chain = MASTER_FALLBACK_CHAINS.get(task_type, []) filtered_chain = [m for m in full_chain if m in cost_filtered_models and m != selected_model] # Step 8-10: Execute with fallback + logging return execute_with_fallback(selected_model, filtered_chain) def get_allowed_tiers(complexity: str) -> list[str]: """Return allowed cost tiers for a given complexity level.""" return { "SIMPLE": ["$"], # Budget only β no exceptions "MEDIUM": ["$", "$$"], # Budget + standard "COMPLEX": ["$", "$$", "$$$", "$$$$"], # All tiers β complex tasks deserve the best }.get(complexity, ["$", "$$"]) # Example flow for "what's 2+2?": # # 1. available_models = {opus, sonnet, haiku, flash, grok-2, ...} # 2. intent = GENERAL # 3. complexity = SIMPLE # 4. (no special cases) # 5. allowed_tiers = ["$"] β SIMPLE means $ only # cost_filtered_models = {haiku, flash, grok-2} β Opus/Sonnet EXCLUDED # 6. preferences for (GENERAL, SIMPLE) = [flash, haiku, grok-2, sonnet] # first match in cost_filtered = flash β # 7. fallback_chain = [haiku, grok-2] β Also cost-filtered # 8. execute with flash # # Result: Opus is NEVER considered, not even momentarily.
Cost Optimization: Two Approaches
βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β COST OPTIMIZATION IMPLEMENTATION OPTIONS β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ β β β APPROACH 1: Explicit filter_by_cost() (shown above) β β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β β β’ Calls get_allowed_tiers(complexity) explicitly β β β’ Filters available_models BEFORE routing table lookup β β β’ Most defensive β impossible to route wrong tier β β β’ Recommended for security-critical deployments β β β β APPROACH 2: Preference ordering (implicit) β β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β β β’ ROUTING_PREFERENCES lists cheapest capable models first β β β’ For SIMPLE tasks: [flash, haiku, grok-2, sonnet] β β β’ First available match wins β naturally picks cheapest β β β’ Simpler code, relies on correct preference ordering β β β β This implementation uses BOTH for defense-in-depth: β β β’ Preference ordering provides first line of cost awareness β β β’ Explicit filter_by_cost() guarantees tier enforcement β β β β For alternative implementations that rely solely on β β preference ordering, see references/models.md for the β β filter_by_cost() function if explicit enforcement is needed. β β β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
Spawning with Different Models
Use sessions_spawn for model routing: sessions_spawn( task: "user's request", model: "selected/model-id", label: "task-type-query" )
Security
Never send sensitive data to untrusted models API keys handled via environment/auth profiles only See references/security.md for full security guidance
Model Details
See references/models.md for detailed capabilities and pricing.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs3 Scripts
- SKILL.md
- README.md
- STATE.md
- compactor.py
- context_guard.py
- dashboard.py