inference-sh-cli
NousResearch/hermes-agent · Agent Skill
透過 inference.sh CLI 執行 150 多個 AI 應用程式(影象、影片、LLM)。
前往安裝 ↓總覽
| 發布者 | |
|---|---|
| 儲存庫 | NousResearch/hermes-agent ↗ |
| 技能資料夾 | inference-sh-cli |
| 星 | 245,082 |
| 分支 | 50,941 |
| 附帶檔案 | 5 |
| 授權 | MIT |
| 最近更新 | 2026年9月13日 |
| 連結 | 技能原始碼 ↗ · 儲存庫 ↗ |
Mozgi 檢查
我們在技能檔案中發現的內容。詳細資訊需訂閱。
安裝
技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。
把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。
Install the “inference-sh-cli” skill from mozgi.io (https://mozgi.io/skills/inference-sh-cli/) for me. 1. Download the archive: https://mozgi.io/skills/inference-sh-cli/download If it fails, take the skill folder from the source: https://github.com/NousResearch/hermes-agent/tree/main/optional-skills/devops/inference-sh-cli 2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me. 3. Put the “inference-sh-cli” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.). 4. Make sure <skills folder>/inference-sh-cli/SKILL.md exists. 5. Briefly tell me what this skill does and offer to try it on my task. If you cannot do a step yourself, tell me what to click or type.
git clone --depth 1 --branch main https://github.com/NousResearch/hermes-agent.git /tmp/hermes-agent mkdir -p .claude/skills cp -r /tmp/hermes-agent/optional-skills/devops/inference-sh-cli .claude/skills/inference-sh-cli
重新啟動 Claude Code — 技能就會出現在清單中,手動呼叫:/inference-sh-cli。要套用到所有專案,請放在 ~/.claude/skills。
git clone --depth 1 --branch main https://github.com/NousResearch/hermes-agent.git /tmp/hermes-agent mkdir -p .agents/skills cp -r /tmp/hermes-agent/optional-skills/devops/inference-sh-cli .agents/skills/inference-sh-cli
Codex 會從目前資料夾往上到儲存庫根目錄,在 .agents/skills 中尋找技能;用 /skills 或 $inference-sh-cli 呼叫。所有專案:~/.agents/skills。
git clone --depth 1 --branch main https://github.com/NousResearch/hermes-agent.git /tmp/hermes-agent mkdir -p .cursor/skills cp -r /tmp/hermes-agent/optional-skills/devops/inference-sh-cli .cursor/skills/inference-sh-cli
Cursor 啟動時會載入技能;在 Agent 聊天輸入「/」呼叫。Cursor 也會讀取 .claude/skills 與 .agents/skills。所有專案:~/.cursor/skills。
git clone --depth 1 --branch main https://github.com/NousResearch/hermes-agent.git /tmp/hermes-agent mkdir -p .gemini/skills cp -r /tmp/hermes-agent/optional-skills/devops/inference-sh-cli .gemini/skills/inference-sh-cli
任務符合時,Gemini CLI 會提議啟用技能,由你確認。所有專案:~/.gemini/skills。
安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。
SKILL.md 內容
作者原文 · 唯讀inference.sh CLI
Run 150+ AI apps in the cloud with a simple CLI. No GPU required.
All commands use the terminal tool to run infsh commands.
When to Use
- User asks to generate images (FLUX, Reve, Seedream, Grok, Gemini image)
- User asks to generate video (Veo, Wan, Seedance, OmniHuman)
- User asks about inference.sh or infsh
- User wants to run AI apps without managing individual provider APIs
- User asks for AI-powered search (Tavily, Exa)
- User needs avatar/lipsync generation
Prerequisites
The infsh CLI must be installed and authenticated. Check with:
infsh me
If not installed:
curl -fsSL https://cli.inference.sh | sh
infsh login
See references/authentication.md for full setup details.
Workflow
1. Always Search First
Never guess app names — always search to find the correct app ID:
infsh app list --search flux
infsh app list --search video
infsh app list --search image
2. Run an App
Use the exact app ID from the search results. Always use --json for machine-readable output:
infsh app run <app-id> --input '{"prompt": "your prompt here"}' --json
3. Parse the Output
The JSON output contains URLs to generated media. Present these to the user with MEDIA:<url> for inline display.
Common Commands
Image Generation
# Search for image apps
infsh app list --search image
# FLUX Dev with LoRA
infsh app run falai/flux-dev-lora --input '{"prompt": "sunset over mountains", "num_images": 1}' --json
# Gemini image generation
infsh app run google/gemini-2-5-flash-image --input '{"prompt": "futuristic city", "num_images": 1}' --json
# Seedream (ByteDance)
infsh app run bytedance/seedream-5-lite --input '{"prompt": "nature scene"}' --json
# Grok Imagine (xAI)
infsh app run xai/grok-imagine-image --input '{"prompt": "abstract art"}' --json
Video Generation
# Search for video apps
infsh app list --search video
# Veo 3.1 (Google)
infsh app run google/veo-3-1-fast --input '{"prompt": "drone shot of coastline"}' --json
# Seedance (ByteDance)
infsh app run bytedance/seedance-1-5-pro --input '{"prompt": "dancing figure", "resolution": "1080p"}' --json
# Wan 2.5
infsh app run falai/wan-2-5 --input '{"prompt": "person walking through city"}' --json
Local File Uploads
The CLI automatically uploads local files when you provide a path:
# Upscale a local image
infsh app run falai/topaz-image-upscaler --input '{"image": "/path/to/photo.jpg", "upscale_factor": 2}' --json
# Image-to-video from local file
infsh app run falai/wan-2-5-i2v --input '{"image": "/path/to/image.png", "prompt": "make it move"}' --json
# Avatar with audio
infsh app run bytedance/omnihuman-1-5 --input '{"audio": "/path/to/audio.mp3", "image": "/path/to/face.jpg"}' --json
Search & Research
infsh app list --search search
infsh app run tavily/tavily-search --input '{"query": "latest AI news"}' --json
infsh app run exa/exa-search --input '{"query": "machine learning papers"}' --json
Other Categories
# 3D generation
infsh app list --search 3d
# Audio / TTS
infsh app list --search tts
# Twitter/X automation
infsh app list --search twitter
Pitfalls
1. Never guess app IDs — always run infsh app list --search <term> first. App IDs change and new apps are added frequently.
2. Always use --json — raw output is hard to parse. The --json flag gives structured output with URLs.
3. Check authentication — if commands fail with auth errors, run infsh login or verify INFSH_API_KEY is set.
4. Long-running apps — video generation can take 30-120 seconds. The terminal tool timeout should be sufficient, but warn the user it may take a moment.
5. Input format — the --input flag takes a JSON string. Make sure to properly escape quotes.
Reference Docs
references/authentication.md— Setup, login, API keysreferences/app-discovery.md— Searching and browsing the app catalogreferences/running-apps.md— Running apps, input formats, output handlingreferences/cli-reference.md— Complete CLI command reference
常見問題
inference-sh-cli 技能有什麼用途?
透過 inference.sh CLI 執行 150 多個 AI 應用程式(影象、影片、LLM)。
如何安裝 inference-sh-cli?
打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。
inference-sh-cli 適用哪些 AI 模型?
技能是純 Markdown 指示(Agent Skills 格式),因此 inference-sh-cli 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。
inference-sh-cli 是免費的嗎?
此技能由 NousResearch 公開發布,採用 MIT 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。
安裝技能安全嗎?
技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。
技能和 MCP 伺服器或外掛有何不同?
MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。