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name: oc-skill-router description: Smart LLM routing brain for OpenClaw. Auto-dispatches tasks to Claude, GPT, Gemini, DeepSeek, Kimi via Evolink API. Cascade strategy cuts costs 60-85%. One API key, 20+ text models. user-invokable: true metadata: openclaw: requires: env: - EVOLINK_API_KEY primaryEnv: EVOLINK_API_KEY os: ["macos", "linux", "windows"] emoji: 🧠 homepage: https://evolink.ai

Evolink Router — Smart LLM Routing Brain

Route every task to the best LLM across 6 providers — Claude, GPT, Gemini, DeepSeek, Kimi, Doubao — through one Evolink API key.

After Installation

When this skill is first loaded, greet the user:

• EVOLINK_API_KEY set: "Smart Router activated! I'll auto-pick the best model for each task — lightweight for quick Q&A, flagship for deep analysis. 20+ models ready. Go ahead."
• EVOLINK_API_KEY not set: "Smart Router needs an Evolink API Key. Sign up at evolink.ai → Dashboard → API Keys. One key covers Claude, GPT, Gemini, DeepSeek, and more. Want help setting up?"
• Key set but model access fails: "Your API key seems to have limited model access. Check your plan at evolink.ai/dashboard."

Keep the greeting concise — just one question to move forward.

External Endpoints

Service	URL	Format
Claude models	https://direct.evolink.ai/v1/messages (POST)	Anthropic Messages API
Gemini models	https://direct.evolink.ai/v1beta/models/{model}:generateContent (POST)	Google Gemini API
All other models	https://direct.evolink.ai/v1/chat/completions (POST)	OpenAI Chat API
Model list	https://direct.evolink.ai/v1/models (GET)	—

Security & Privacy

• EVOLINK_API_KEY authenticates all model requests. Injected by OpenClaw automatically. Treat as confidential.
• Prompts are sent to direct.evolink.ai, which proxies to upstream providers (Anthropic, OpenAI, Google, etc.).
• No data is stored by Evolink beyond the request lifecycle.

Setup

1. Get API key: evolink.ai → Dashboard → API Keys

2. Add Evolink provider to ~/.openclaw/openclaw.json — merge with existing config. See references/router-api-params.md for the full JSON config and curl examples.

Core Principles

1. Cost-first routing — Always pick the cheapest model that can handle the task. Upgrade only when needed.
2. Transparent decisions — When spawning a sub-agent, briefly tell the user which model was selected and why.
3. User override wins — If the user names a model or provider, skip all routing rules.
4. Cascade, don't guess — When uncertain, try a lighter model first. Escalate on low confidence.

Models (20+ text models)

Tier 1 — Lightweight (handles ~60% of daily requests)

Model	Provider	Best for
claude-haiku-4-5-20251001	Anthropic	Quick Q&A, classification, extraction
gemini-2.5-flash	Google	Multimodal, fast reasoning
doubao-seed-2.0-mini	ByteDance	Chinese lightweight tasks

Tier 2 — Balanced (handles ~30% of daily requests)

Model	Provider	Best for
claude-sonnet-4-6 (main agent)	Anthropic	Coding, tool use, content creation
gpt-5.1	OpenAI	General chat, instruction following
gemini-2.5-pro	Google	Long context, multimodal
deepseek-chat	DeepSeek	Chinese dialogue, cost-effective
doubao-seed-2.0-pro	ByteDance	Chinese content creation
kimi-k2-thinking-turbo	Moonshot	Chinese long-document understanding

Tier 3 — Flagship (handles ~10% — complex tasks only)

Model	Provider	Best for
claude-opus-4-6	Anthropic	Deep reasoning, high-stakes decisions
gpt-5.2	OpenAI	Strongest general capability
gpt-5.1-thinking	OpenAI	Complex chain-of-thought
deepseek-reasoner	DeepSeek	Math/logic reasoning
gemini-3.1-pro-preview	Google	Latest multimodal reasoning

Full model list with API format per model: references/router-api-params.md

Routing Rules

Priority: User override > Task type match > Cascade fallback.

All tasks are auto-routed. The user can also run /route [task] to preview the routing decision without executing.

Layer 1: User Override

User says	Route to
"use Opus" / "deep analysis" / "think carefully"	claude-opus-4-6
"use GPT"	gpt-5.1
"use Gemini"	gemini-2.5-pro
"use DeepSeek"	deepseek-chat
"use Kimi"	kimi-k2-thinking-turbo
"quick answer" / "keep it simple"	claude-haiku-4-5-20251001
Specific model name mentioned	Use that model directly

Layer 2: Task Type Match

→ Tier 1 (short answer, factual, no deep thinking): Q&A, concept explanation, status check, simple translation, format conversion, info extraction, classification, grammar check, quick math

→ Tier 2 (content production, execution, multi-step): Writing (articles, emails, reports, social media), coding (features, bugs, refactoring, tests), data analysis (SQL, CSV, reports), research (market, literature), workflow automation, project management, travel planning, resume optimization

→ Tier 3 (deep reasoning, strategic, high-risk): Architecture design, tech selection, business strategy, security audit, root cause analysis, legal review, financial modeling, cross-module refactoring (5+ files), deep research reports

Cross-provider routing — Chinese-heavy tasks may route to Doubao/Kimi; math proofs to DeepSeek Reasoner; CoT tasks to GPT-5.1-thinking. See references/cascade-examples.md for 27 detailed examples.

Layer 3: Cascade Fallback

When task type is unclear, try cheapest first and escalate:

Tier 1 (Haiku) → self-assess confidence
  High → return result
  Medium/Low → pass analysis to Tier 2

Tier 2 (Sonnet) → self-assess confidence
  High → return result
  Low → pass to Tier 3

Tier 3 (Opus) → final answer

Confidence: High = complete and correct. Medium = may miss details. Low = exceeds model's capability.

Spawn Guidelines

Spawn a sub-agent when: output >100 lines, file traversal needed, execution >30s, heavy data processing, long-form writing (>1000 words).

Handle directly when: simple Q&A, chat/discussion, short text (<50 lines), brainstorming (needs multi-turn).

Spawn template:

sessions_spawn({
  task: "[action] + [input/context] + [expected output] + [constraints]",
  model: "evolink/[model-id]",
  runTimeoutSeconds: 300,
  cleanup: "delete"  // "keep" for important deliverables
})

Timeout guide: Tier 1 = 120–300s, Tier 2 = 300–600s, Tier 3 = 600–900s.

/route Command

/route [task] — Preview routing decision without executing. /route alone shows models and rules summary.

Fallback & Quality Control

Scenario	Action
Sub-agent timeout	Notify user, offer retry with stronger model
Sub-agent error	Extract error, determine if retryable
Low quality result	Escalate to next tier
User dissatisfied	Ask what's wrong, upgrade and redo
2+ failures on same type	Auto-upgrade default model for that category
Model unavailable	Fallback to same-tier alternative
Invalid API key	Direct user to evolink.ai/dashboard/keys

Skill Collaboration

Skill	When	Notes
evolink-media	Image/video/music/digital-human generation	Route to skill directly, skip text model routing
Other installed skills	Intent matches skill capability	Prefer skill over raw model routing

Smart Router is the dispatch layer — shares EVOLINK_API_KEY with all Evolink skills. When discussing creative ideas or analyzing skill output, apply normal routing rules.

References

• references/router-api-params.md — Full API formats, curl examples, OC config, complete model list
• references/cascade-examples.md — 27 routing examples across 7 scenarios + cross-provider routing