Tencent SkillHub · AI

Intelligent Router

Intelligent model routing for sub-agent task delegation. Choose the optimal model based on task complexity, cost, and capability requirements. Reduces costs...

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Intelligent model routing for sub-agent task delegation. Choose the optimal model based on task complexity, cost, and capability requirements. Reduces costs...

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: README.md, SKILL.md, install.sh, scripts/auto_refresh_models.sh, scripts/discover_models.py, scripts/fix_tiers.py

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 3.0.1

Provenance

Publisher: bowen31337
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 25 sections Open source page

Intelligent Router — Core Skill

CORE SKILL: This skill is infrastructure, not guidance. Installation = enforcement. Run bash skills/intelligent-router/install.sh to activate.

What It Does

Automatically classifies any task into a tier (SIMPLE/MEDIUM/COMPLEX/REASONING/CRITICAL) and recommends the cheapest model that can handle it well. The problem it solves: Without routing, every cron job and sub-agent defaults to Sonnet (expensive). With routing, monitoring tasks use free local models, saving 80-95% on cost.

Before spawning any sub-agent:

python3 skills/intelligent-router/scripts/router.py classify "task description"

Before creating any cron job:

python3 skills/intelligent-router/scripts/spawn_helper.py "task description" # Outputs the exact model ID and payload snippet to use

To validate a cron payload has model set:

python3 skills/intelligent-router/scripts/spawn_helper.py --validate '{"kind":"agentTurn","message":"..."}'

❌ VIOLATION (never do this):

# Cron job without model = Sonnet default = expensive waste {"kind": "agentTurn", "message": "check server..."} # ← WRONG

✅ CORRECT:

# Always specify model from router recommendation {"kind": "agentTurn", "message": "check server...", "model": "ollama/glm-4.7-flash"}

Tier System

TierUse ForPrimary ModelCost🟢 SIMPLEMonitoring, heartbeat, checks, summariesanthropic-proxy-6/glm-4.7 (alt: proxy-4)$0.50/M🟡 MEDIUMCode fixes, patches, research, data analysisnvidia-nim/meta/llama-3.3-70b-instruct$0.40/M🟠 COMPLEXFeatures, architecture, multi-file, debuganthropic/claude-sonnet-4-6$3/M🔵 REASONINGProofs, formal logic, deep analysisnvidia-nim/moonshotai/kimi-k2-thinking$1/M🔴 CRITICALSecurity, production, high-stakesanthropic/claude-opus-4-6$5/M SIMPLE fallback chain: anthropic-proxy-4/glm-4.7 → nvidia-nim/qwen/qwen2.5-7b-instruct ($0.15/M) ⚠️ ollama-gpu-server is BLOCKED for cron/spawn use. Ollama binds to 127.0.0.1 by default — unreachable over LAN from the OpenClaw host. The router_policy.py enforcer will reject any payload referencing it. Tier classification uses 4 capability signals (not cost alone): effective_params (50%) — extracted from model ID or known-model-params.json for closed-source models context_window (20%) — larger = more capable cost_input (20%) — price as quality proxy (weak signal, last resort for unknown sizes) reasoning_flag (10%) — bonus for dedicated thinking specialists (R1, QwQ, Kimi-K2)

Policy Enforcer (NEW in v3.2.0)

router_policy.py catches bad model assignments before they are created, not after they fail.

Validate a cron payload before submitting

python3 skills/intelligent-router/scripts/router_policy.py check \ '{"kind":"agentTurn","model":"ollama-gpu-server/glm-4.7-flash","message":"check server"}' # Output: VIOLATION: Blocked model 'ollama-gpu-server/glm-4.7-flash'. Recommended: anthropic-proxy-6/glm-4.7

Get enforced model recommendation for a task

python3 skills/intelligent-router/scripts/router_policy.py recommend "monitor alphastrike service" # Output: Tier: SIMPLE Model: anthropic-proxy-6/glm-4.7 python3 skills/intelligent-router/scripts/router_policy.py recommend "monitor alphastrike service" --alt # Output: Tier: SIMPLE Model: anthropic-proxy-4/glm-4.7 ← alternate key for load distribution

Audit all existing cron jobs

python3 skills/intelligent-router/scripts/router_policy.py audit # Scans all crons, reports any with blocked or missing models

Show blocklist

python3 skills/intelligent-router/scripts/router_policy.py blocklist

Policy rules enforced

Model must be set — no model field = Sonnet default = expensive waste No blocked models — ollama-gpu-server/* and bare ollama/* are rejected for cron use CRITICAL tasks — warns if using a non-Opus model for classified-critical work

Installation (Core Skill Setup)

Run once to self-integrate into AGENTS.md: bash skills/intelligent-router/install.sh This patches AGENTS.md with the mandatory protocol so it's always in context.

CLI Reference

# ── Policy enforcer (run before creating any cron/spawn) ── python3 skills/intelligent-router/scripts/router_policy.py check '{"kind":"agentTurn","model":"...","message":"..."}' python3 skills/intelligent-router/scripts/router_policy.py recommend "task description" python3 skills/intelligent-router/scripts/router_policy.py recommend "task" --alt # alternate proxy key python3 skills/intelligent-router/scripts/router_policy.py audit # scan all crons python3 skills/intelligent-router/scripts/router_policy.py blocklist # ── Core router ── # Classify + recommend model python3 skills/intelligent-router/scripts/router.py classify "task" # Get model id only (for scripting) python3 skills/intelligent-router/scripts/spawn_helper.py --model-only "task" # Show spawn command python3 skills/intelligent-router/scripts/spawn_helper.py "task" # Validate cron payload has model set python3 skills/intelligent-router/scripts/spawn_helper.py --validate '{"kind":"agentTurn","message":"..."}' # List all models by tier python3 skills/intelligent-router/scripts/router.py models # Detailed scoring breakdown python3 skills/intelligent-router/scripts/router.py score "task" # Config health check python3 skills/intelligent-router/scripts/router.py health # Auto-discover working models (NEW) python3 skills/intelligent-router/scripts/discover_models.py # Auto-discover + update config python3 skills/intelligent-router/scripts/discover_models.py --auto-update # Test specific tier only python3 skills/intelligent-router/scripts/discover_models.py --tier COMPLEX

Scoring System

15-dimension weighted scoring (not just keywords): Reasoning markers (0.18) — prove, theorem, derive Code presence (0.15) — code blocks, file extensions Multi-step patterns (0.12) — first...then, numbered lists Agentic task (0.10) — run, fix, deploy, build Technical terms (0.10) — architecture, security, protocol Token count (0.08) — complexity from length Creative markers (0.05) — story, compose, brainstorm Question complexity (0.05) — multiple who/what/how Constraint count (0.04) — must, require, exactly Imperative verbs (0.03) — analyze, evaluate, audit Output format (0.03) — json, table, markdown Simple indicators (0.02) — check, get, show (inverted) Domain specificity (0.02) — acronyms, dotted notation Reference complexity (0.02) — "mentioned above" Negation complexity (0.01) — not, never, except Confidence: 1 / (1 + exp(-8 × (score - 0.5)))

Config

Models defined in config.json. Add new models there, router picks them up automatically. Local Ollama models have zero cost — always prefer them for SIMPLE tasks.

Auto-Discovery (Self-Healing)

The intelligent-router can automatically discover working models from all configured providers via real live inference tests (not config-existence checks).

How It Works

Provider Scanning: Reads ~/.openclaw/openclaw.json → finds all models Live Inference Test: Sends "hi" to each model, checks it actually responds (catches auth failures, quota exhaustion, 404s, timeouts) OAuth Bypass: Providers with sk-ant-oat01-* tokens (Anthropic OAuth) are skipped in raw HTTP — OpenClaw refreshes these transparently, so they're always marked available Thinking Model Support: Models that return content=None + reasoning_content (GLM-4.7, Kimi-K2, Qwen3-thinking) are correctly detected as available Auto-Classification: Tiers assigned via tier_classifier.py using 4 capability signals Config Update: Removes unavailable models, rebuilds tier primaries from working set Cron: Hourly refresh (cron id: a8992c1f) keeps model list current, alerts if availability changes by >2

Usage

# One-time discovery python3 skills/intelligent-router/scripts/discover_models.py # Auto-update config with working models only python3 skills/intelligent-router/scripts/discover_models.py --auto-update # Set up hourly refresh cron openclaw cron add --job '{ "name": "Model Discovery Refresh", "schedule": {"kind": "every", "everyMs": 3600000}, "payload": { "kind": "systemEvent", "text": "Run: bash skills/intelligent-router/scripts/auto_refresh_models.sh", "model": "ollama/glm-4.7-flash" } }'

Benefits

✅ Self-healing: Automatically removes broken models (e.g., expired OAuth) ✅ Zero maintenance: No manual model list updates ✅ New models: Auto-adds newly released models ✅ Cost optimization: Always uses cheapest working model per tier

Discovery Output

Results saved to skills/intelligent-router/discovered-models.json: { "scan_timestamp": "2026-02-19T21:00:00", "total_models": 25, "available_models": 23, "unavailable_models": 2, "providers": { "anthropic": { "available": 2, "unavailable": 0, "models": [...] } } }

Pinning Models

To preserve a model even if it fails discovery: { "id": "special-model", "tier": "COMPLEX", "pinned": true // Never remove during auto-update }

⚠️ Known Gap — Proactive Health-Based Routing (2026-03-04)

Current router is reactive not proactive: Fallback only fires AFTER a 429 is received No awareness of concurrent sessions on same proxy No cooldown tracking after rate-limit events Needed improvements: Track last-429 timestamp per provider → skip if within cooldown window Track active concurrent spawns per provider → if >1 active, route to OAuth Before spawning N parallel agents, check if single provider can handle N concurrent Expose router.get_best_available(n_concurrent=2) API

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

4 Scripts2 Docs

SKILL.md Primary doc
README.md Docs
install.sh Scripts
scripts/auto_refresh_models.sh Scripts
scripts/discover_models.py Scripts
scripts/fix_tiers.py Scripts