intelligent-router

Intelligent model routing for sub-agent task delegation. Choose the optimal model based on task complexity, cost, and capability requirements. Reduces costs by routing simple tasks to cheaper models while preserving quality for complex work.

# 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.

---

## MANDATORY Protocol (enforced via AGENTS.md)

### Before spawning any sub-agent:
```bash
python3 skills/intelligent-router/scripts/router.py classify "task description"
```

### Before creating any cron job:
```bash
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:
```bash
python3 skills/intelligent-router/scripts/spawn_helper.py --validate '{"kind":"agentTurn","message":"..."}'
```

### ❌ VIOLATION (never do this):
```python
# Cron job without model = Sonnet default = expensive waste
{"kind": "agentTurn", "message": "check server..."}  # ← WRONG
```

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

---

## Tier System

| Tier | Use For | Primary Model | Cost |
|------|---------|---------------|------|
| 🟢 SIMPLE | Monitoring, heartbeat, checks, summaries | `anthropic-proxy-6/glm-4.7` (alt: proxy-4) | $0.50/M |
| 🟡 MEDIUM | Code fixes, patches, research, data analysis | `nvidia-nim/meta/llama-3.3-70b-instruct` | $0.40/M |
| 🟠 COMPLEX | Features, architecture, multi-file, debug | `anthropic/claude-sonnet-4-6` | $3/M |
| 🔵 REASONING | Proofs, formal logic, deep analysis | `nvidia-nim/moonshotai/kimi-k2-thinking` | $1/M |
| 🔴 CRITICAL | Security, production, high-stakes | `anthropic/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
```bash
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
```bash
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
```bash
python3 skills/intelligent-router/scripts/router_policy.py audit
# Scans all crons, reports any with blocked or missing models
```

### Show blocklist
```bash
python3 skills/intelligent-router/scripts/router_policy.py blocklist
```

### Policy rules enforced
1. **Model must be set** — no model field = Sonnet default = expensive waste
2. **No blocked models** — `ollama-gpu-server/*` and bare `ollama/*` are rejected for cron use
3. **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
bash skills/intelligent-router/install.sh
```

This patches AGENTS.md with the mandatory protocol so it's always in context.

---

## CLI Reference

```bash
# ── 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):

1. **Reasoning markers** (0.18) — prove, theorem, derive
2. **Code presence** (0.15) — code blocks, file extensions
3. **Multi-step patterns** (0.12) — first...then, numbered lists
4. **Agentic task** (0.10) — run, fix, deploy, build
5. **Technical terms** (0.10) — architecture, security, protocol
6. **Token count** (0.08) — complexity from length
7. **Creative markers** (0.05) — story, compose, brainstorm
8. **Question complexity** (0.05) — multiple who/what/how
9. **Constraint count** (0.04) — must, require, exactly
10. **Imperative verbs** (0.03) — analyze, evaluate, audit
11. **Output format** (0.03) — json, table, markdown
12. **Simple indicators** (0.02) — check, get, show (inverted)
13. **Domain specificity** (0.02) — acronyms, dotted notation
14. **Reference complexity** (0.02) — "mentioned above"
15. **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

1. **Provider Scanning:** Reads `~/.openclaw/openclaw.json` → finds all models
2. **Live Inference Test:** Sends `"hi"` to each model, checks it actually responds (catches auth failures, quota exhaustion, 404s, timeouts)
3. **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
4. **Thinking Model Support:** Models that return `content=None` + `reasoning_content` (GLM-4.7, Kimi-K2, Qwen3-thinking) are correctly detected as available
5. **Auto-Classifi