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Agent skill
libllm
libllm - LLM API client for OpenAI-compatible endpoints. LlmApi class handles chat completions and embeddings via HTTP. Supports GitHub Models, Azure OpenAI, and standard OpenAI endpoints. Handles streaming responses, token counting, and multi-tool parallel call fixes. Use for LLM completions, embeddings, and AI model integration.
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Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/libllm
SKILL.md
libllm Skill
When to Use
- Making chat completion requests to LLM providers
- Generating text embeddings for vector search
- Integrating with OpenAI-compatible APIs
- Handling streaming LLM responses
Key Concepts
LlmApi: HTTP client for OpenAI-compatible endpoints. Handles authentication, streaming, and response parsing.
DEFAULT_MAX_TOKENS: Standard token limit for completions.
Usage Patterns
Pattern 1: Chat completion
javascript
import { LlmApi } from "@copilot-ld/libllm";
const api = new LlmApi(config, logger);
const response = await api.completion([{ role: "user", content: "Hello" }], {
model: "gpt-4",
maxTokens: 1000,
});
Pattern 2: Generate embeddings
javascript
const embeddings = await api.embed(["text to embed"]);
// Returns array of vectors
Integration
Used by LLM service. Configurable via environment for different providers (OpenAI, Azure, GitHub Models).
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