Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问许可证需确认审计提醒

faion-llm-integrationfaion LLM 集成

Agent Skill

faion-llm-integration 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

210

周安装

9

GitHub Stars

2

下载量

73
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:faion-llm-integration(faion LLM 集成)
来源仓库:https://github.com/faionfaion/faion-network
仓库路径:skills/faion-llm-integration
安装命令:
npx skills add https://github.com/faionfaion/faion-network --skill faion-llm-integration
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/faionfaion/faion-network --skill faion-llm-integration

简介

faion-llm-integration 专用于 LLM API 对接,支持 OpenAI、Claude 和 Gemini 等平台。

  • 涵盖提示工程、输出格式化和密钥安全管理。
  • 可分析现有 SDK 版本和环境变量配置,避免重复造轮子。
  • 安装命令:npx skills add https://github.com/faionfaion/faion-network --skill faion-llm-integration。
  • API 密钥等敏感信息不得明文输出,建议使用 .env 管理。

SKILL.md

Entry point: /faion-net — invoke this skill for automatic routing to the appropriate domain.

LLM Integration Skill

Communication: User's language. Code: English.

Purpose

Handles direct integration with LLM APIs. Covers OpenAI, Claude, Gemini, local models, prompt engineering, and output structuring.

Context Discovery

Auto-Investigation

Check these project signals before asking questions:

SignalWhere to CheckWhat to Look For
Dependenciespackage.json, requirements.txt, go.modopenai, anthropic, google-generativeai, langchain
Config files.env, config/*.{yml,json}API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY)
Existing codeGrep for "openai", "anthropic", "genai"Existing LLM integrations
DocumentationREADME.md, docs/LLM usage patterns

Discovery Questions

question: "What LLM task are you building?"
header: "Task Type"
multiSelect: false
options:
  - label: "Chat/completion API"
    description: "Direct LLM API calls for text generation"
  - label: "Function calling / tool use"
    description: "LLM selects and executes functions"
  - label: "Structured output (JSON mode)"
    description: "Constrain LLM to return valid JSON/schemas"
  - label: "Prompt optimization"
    description: "Improve existing prompts (few-shot, CoT, templates)"
question: "Which LLM provider(s)?"
header: "Provider"
multiSelect: true
options:
  - label: "OpenAI (GPT-4o, o1)"
    description: "OpenAI API integration"
  - label: "Claude (Anthropic)"
    description: "Claude Opus/Sonnet via Anthropic API"
  - label: "Gemini (Google)"
    description: "Gemini Pro/Flash via Google AI"
  - label: "Local LLM (Ollama)"
    description: "Self-hosted models for privacy"
question: "Do you need safety/content moderation?"
header: "Guardrails"
multiSelect: false
options:
  - label: "Yes - content filtering/PII detection"
    description: "Implement guardrails for safety"
  - label: "No - internal use only"
    description: "Skip guardrails"

Scope

AreaCoverage
LLM APIsOpenAI (GPT-4o, o1), Claude (Opus 4.5, Sonnet 4), Gemini (Pro, Flash)
Prompt EngineeringFew-shot, CoT, chain-of-thought techniques
Structured OutputJSON mode, function calling, tool use
GuardrailsContent safety, validation, error handling
Local LLMsOllama integration, privacy-focused deployments

Quick Start

TaskFiles
OpenAI integrationopenai-api-integration.md → openai-chat-completions.md
Claude integrationclaude-api-basics.md → claude-messages-api.md
Gemini integrationgemini-basics.md → gemini-multimodal.md
Local LLMlocal-llm-ollama.md
Promptsprompt-basics.md → prompt-techniques.md
Function callingfunction-calling-patterns.md + tool-use-basics.md

Methodologies (26)

OpenAI (5):

  • openai-api-integration: API setup, authentication, models
  • openai-chat-completions: Chat API, streaming, parameters
  • openai-function-calling: Tool definitions, execution
  • openai-embeddings: Text embeddings (moved to rag-engineer)
  • openai-assistants: Assistant API, threads, tools

Claude (6):

  • claude-api-basics: Anthropic API setup
  • claude-messages-api: Messages API, streaming
  • claude-tool-use: Tool definitions, structured output
  • claude-advanced-features: Extended thinking, prompt caching
  • claude-best-practices: Safety, context management
  • claude-api-integration: SDK integration patterns

Gemini (4):

  • gemini-basics: Google AI setup, models
  • gemini-multimodal: Vision, audio, video inputs
  • gemini-function-calling: Function declarations
  • gemini-api-integration: SDK patterns

Prompt Engineering (6):

  • prompt-basics: Structure, few-shot, roles
  • prompt-techniques: Advanced patterns, templates
  • cot-basics: Chain-of-thought fundamentals
  • cot-techniques: Zero-shot CoT, reasoning chains
  • structured-output-basics: JSON mode, schemas
  • structured-output-patterns: Advanced structuring

Safety & Tools (4):

  • guardrails-basics: Content safety, PII detection
  • guardrails-implementation: Implementation patterns
  • function-calling-patterns: Tool design, error handling
  • tool-use-basics: Tool fundamentals

Local (1):

  • local-llm-ollama: Ollama setup, model management

Code Examples

OpenAI Chat Completion

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing"}
    ]
)
print(response.choices[0].message.content)

Claude Messages

import anthropic

client = anthropic.Anthropic()
message = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain RAG systems"}]
)
print(message.content[0].text)

Function Calling

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"}
            }
        }
    }
}]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the weather in SF?"}],
    tools=tools
)

Related Skills

SkillRelationship
faion-rag-engineerUses embeddings APIs
faion-ai-agentsUses tool calling
faion-ml-opsUses for evaluation

*LLM Integration v1.0 | 26 methodologies*

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.52%
按下载量换算27

Claude

30.08%
按下载量换算22

Cursor

17.43%
按下载量换算13

Gemini CLI

8.77%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills