Agent skill

openrouter-skill

Orchestrate Chinese LLMs (DeepSeek, Qwen, Yi, Moonshot) through OpenRouter API with LangChain. Use when: openrouter, chinese llm, deepseek, qwen, moonshot, yi model, model routing, auto router, llm orchestration.

View SKILL.md on GitHub Repository
Stars 6
Forks 0

Install this agent skill to your Project

npx add-skill https://github.com/ScientiaCapital/skills/tree/main/active/openrouter-skill

SKILL.md

<quick_start>

1. Basic LangChain Setup

python
from langchain_openai import ChatOpenAI
import os

# Any OpenRouter model works with ChatOpenAI
llm = ChatOpenAI(
    model="deepseek/deepseek-chat",
    openai_api_key=os.getenv("OPENROUTER_API_KEY"),
    openai_api_base="https://openrouter.ai/api/v1",
    default_headers={
        "HTTP-Referer": "https://your-app.com",  # Optional but recommended
        "X-Title": "Your App Name"
    }
)

response = llm.invoke("Explain quantum computing in simple terms")

2. Vision Analysis (Charts, Documents)

python
from langchain_core.messages import HumanMessage
import base64

llm = ChatOpenAI(
    model="qwen/qwen-2-vl-72b-instruct",
    openai_api_key=os.getenv("OPENROUTER_API_KEY"),
    openai_api_base="https://openrouter.ai/api/v1"
)

# From URL
response = llm.invoke([
    HumanMessage(content=[
        {"type": "text", "text": "Analyze this chart and identify key trends"},
        {"type": "image_url", "image_url": {"url": "https://example.com/chart.png"}}
    ])
])

# From base64
with open("chart.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()

response = llm.invoke([
    HumanMessage(content=[
        {"type": "text", "text": "What does this chart show?"},
        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_data}"}}
    ])
])

3. Auto-Routing (Let OpenRouter Choose)

python
# OpenRouter's Auto model selects the best model for your prompt
llm = ChatOpenAI(
    model="openrouter/auto",  # Powered by NotDiamond
    openai_api_key=os.getenv("OPENROUTER_API_KEY"),
    openai_api_base="https://openrouter.ai/api/v1"
)

</quick_start>

<success_criteria>

  • OpenRouter API key configured and authenticated
  • Model selection follows the decision tree (vision -> Qwen-VL, code -> DeepSeek Coder, etc.)
  • LangChain ChatOpenAI integration working with correct base URL and headers
  • Cost savings of 60-97% vs Western model equivalents for comparable quality
  • Fallback chain configured for production reliability </success_criteria>

<core_concepts>

Model Selection Decision Tree

Task Type
│
├─ Vision/Charts ──────────> qwen/qwen-2-vl-72b-instruct ($0.40/M)
├─ Code Generation ────────> deepseek/deepseek-coder ($0.14/$0.28)
├─ Deep Reasoning ─────────> qwen/qwq-32b ($0.15/$0.40)
├─ Long Documents ─────────> moonshot/moonshot-v1-128k ($0.55/M)
├─ Fast/Cheap Tasks ───────> qwen/qwen-2.5-7b-instruct ($0.09/M)
├─ General Analysis ───────> deepseek/deepseek-chat ($0.27/$1.10)
└─ Unknown/Auto ───────────> openrouter/auto

Top Chinese LLMs

Model Best For Cost ($/1M tokens)
deepseek/deepseek-chat General reasoning, analysis $0.27 in / $1.10 out
deepseek/deepseek-coder Code generation $0.14 / $0.28
qwen/qwen-2-vl-72b-instruct Vision, charts $0.40 / $0.40
qwen/qwen-2.5-7b-instruct Fast, cheap tasks $0.09 / $0.09
qwen/qwq-32b Deep reasoning $0.15 / $0.40
moonshot/moonshot-v1-128k Long context (128K) $0.55 / $0.55

Cost Comparison vs Western Models

Task Western Model Cost Chinese Model Cost Savings
Chat GPT-4o $5.00/$15.00 DeepSeek Chat $0.27/$1.10 95%
Code Claude Sonnet $3.00/$15.00 DeepSeek Coder $0.14/$0.28 95%
Vision GPT-4o $5.00/$15.00 Qwen-VL $0.40/$0.40 97%
Fast GPT-4o-mini $0.15/$0.60 Qwen-7B $0.09/$0.09 60%

LangGraph Multi-Model Factory

python
from enum import Enum
from langchain_openai import ChatOpenAI
import os

class ChineseModel(str, Enum):
    DEEPSEEK_CHAT = "deepseek/deepseek-chat"
    DEEPSEEK_CODER = "deepseek/deepseek-coder"
    QWEN_VL = "qwen/qwen-2-vl-72b-instruct"
    QWEN_FAST = "qwen/qwen-2.5-7b-instruct"
    QWQ_REASONING = "qwen/qwq-32b"
    MOONSHOT_LONG = "moonshot/moonshot-v1-128k"
    AUTO = "openrouter/auto"

def create_llm(model: ChineseModel, **kwargs) -> ChatOpenAI:
    """Factory for OpenRouter LLMs with sensible defaults."""
    return ChatOpenAI(
        model=model.value,
        openai_api_key=os.getenv("OPENROUTER_API_KEY"),
        openai_api_base="https://openrouter.ai/api/v1",
        default_headers={
            "HTTP-Referer": os.getenv("APP_URL", "http://localhost"),
            "X-Title": os.getenv("APP_NAME", "LangChain App")
        },
        **kwargs
    )

# Usage
chat_llm = create_llm(ChineseModel.DEEPSEEK_CHAT)
vision_llm = create_llm(ChineseModel.QWEN_VL)
fast_llm = create_llm(ChineseModel.QWEN_FAST, temperature=0)

Environment Setup

bash
# .env
OPENROUTER_API_KEY=sk-or-v1-...
APP_URL=https://your-app.com      # For attribution (optional)
APP_NAME=Your App Name            # For attribution (optional)

</core_concepts>

  • reference/models-catalog.md - Complete model listing with capabilities
  • reference/routing-strategies.md - Auto, provider, and custom routing
  • reference/langchain-integration.md - LangChain/LangGraph patterns
  • reference/cost-optimization.md - Budget management and caching
  • reference/tool-calling.md - Function calling patterns
  • reference/multimodal.md - Vision, PDF, audio support
  • reference/observability.md - Monitoring and tracing
  • Set OPENROUTER_API_KEY environment variable
  • Choose appropriate model for task type (see decision tree)
  • Use ChatOpenAI with openai_api_base="https://openrouter.ai/api/v1"
  • Add HTTP-Referer and X-Title headers for attribution
  • Consider cost implications (Chinese models are 10-100x cheaper)
  • Enable streaming for chat interfaces
  • Implement fallback chain for production reliability
  • Set up cost tracking/budget limits

Emit Outcome Sidecar

As the final step, write to ~/.claude/skill-analytics/last-outcome-openrouter.json:

json
{"ts":"[UTC ISO8601]","skill":"openrouter","version":"1.0.0","variant":"default",
 "status":"[success|partial|error]","runtime_ms":[estimated ms from start],
 "metrics":{"requests_routed":[n],"models_used":[n],"total_cost_usd":[n]},
 "error":null,"session_id":"[YYYY-MM-DD]"}

Use status "partial" if some stages failed but results were produced. Use "error" only if no output was generated.

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

ScientiaCapital/skills

project-context

Maintains project context and progress tracking across Claude sessions. Use at session start to load context, on session end to save progress. Triggers: load project context, save context, end session, what was I working on, switch to project, done for today.

6 0
Explore
ScientiaCapital/skills

workflow-enforcer-skill

Enforces workflow discipline across ALL projects. Ensures Claude checks for specialized agents before responding, announces skill/agent usage, and creates TodoWrite todos for multi-step tasks. Triggers: automatic on all sessions, use the right agent, follow workflow.

6 0
Explore
ScientiaCapital/skills

gtm-pricing

B2B go-to-market strategy, pricing models, ICP development, positioning, and competitive intelligence. Use when planning GTM strategy, setting pricing, defining ICP, or evaluating opportunities.

6 0
Explore
ScientiaCapital/skills

subagent-teams

Orchestrate in-session Task tool teams for parallel work. Fan-out research, implementation, review, and documentation across subagents. Use when: parallel tasks, fan-out, subagent team, Task tool, in-session agents.

6 0
Explore
ScientiaCapital/skills

planning-prompts

Comprehensive skill for project planning and prompt engineering. Covers hierarchical plans (briefs, roadmaps, phases), Claude-to-Claude meta-prompts, and multi-stage workflows. Use when: planning, prompt creation, agentic pipeline work, project roadmap, meta-prompts, research to implement workflow.

6 0
Explore
ScientiaCapital/skills

linkedin-sales-navigator-alt-skill

Build targeted prospect lists by analyzing LinkedIn profiles, extracting job titles, companies, locations, and recent activity. Identifies decision-makers, tracks job changes for warm outreach, and enriches contact data. Use when users need to find prospects, build lead lists, or track decision-maker movements.

6 0
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results