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hermes-agent/optional-skills/devops/cli/SKILL.md
Teknium db22efbe88 feat(optional-skills): declare platforms frontmatter for all 63 undeclared skills 
Extends the Windows-gating work to the optional-skills/ tree. Every
SKILL.md that previously omitted the platforms: field now carries an
explicit declaration, which Hermes's loader (agent.skill_utils.
skill_matches_platform) honors to skip-load on incompatible OSes.

58 skills declared cross-platform (platforms: [linux, macos, windows]):
  autonomous-ai-agents/blackbox, autonomous-ai-agents/honcho
  blockchain/base, blockchain/solana
  communication/one-three-one-rule
  creative/blender-mcp, creative/concept-diagrams, creative/hyperframes,
  creative/kanban-video-orchestrator, creative/meme-generation
  devops/cli (inference-sh-cli), devops/docker-management
  dogfood/adversarial-ux-test
  email/agentmail
  finance/3-statement-model, finance/comps-analysis, finance/dcf-model,
  finance/excel-author, finance/lbo-model, finance/merger-model,
  finance/pptx-author
  health/fitness-nutrition, health/neuroskill-bci
  mcp/fastmcp, mcp/mcporter
  migration/openclaw-migration
  mlops/accelerate, mlops/chroma, mlops/clip, mlops/guidance,
  mlops/hermes-atropos-environments, mlops/huggingface-tokenizers,
  mlops/instructor, mlops/lambda-labs, mlops/llava, mlops/modal,
  mlops/peft, mlops/pinecone, mlops/pytorch-lightning, mlops/qdrant,
  mlops/saelens, mlops/simpo, mlops/stable-diffusion
  productivity/canvas, productivity/shop-app, productivity/shopify,
  productivity/siyuan, productivity/telephony
  research/domain-intel, research/drug-discovery, research/duckduckgo-search,
  research/gitnexus-explorer, research/parallel-cli, research/scrapling
  security/1password, security/oss-forensics, security/sherlock
  web-development/page-agent

5 skills gated from Windows (platforms: [linux, macos]):
  mlops/flash-attention   - Flash Attention wheels are Linux-first; Windows
                            install requires building from source with CUDA
  mlops/faiss             - faiss-gpu has no Windows wheel; gate rather than
                            leak partial (faiss-cpu) support
  mlops/nemo-curator      - NVIDIA NeMo ecosystem has no first-class Windows path
  mlops/slime             - Megatron+SGLang RL stack is Linux-only in practice
  mlops/whisper           - openai-whisper + ffmpeg setup on Windows is
                            non-trivial; gate until Windows install stanza lands

Methodology: scanned every SKILL.md for Windows-hostile signals
(apt-get, brew, systemd, osascript, ptrace, X11 binaries, POSIX-only
Python APIs, Docker POSIX $(pwd) bind-mounts, explicit 'linux-only' /
'macos-only' text). 3 skills flagged as having hard signals on review:
docker-management and qdrant only had POSIX $(pwd) docker examples and
the tools themselves (Docker Desktop, Qdrant) run fine on Windows —
declared ALL. whisper had an apt/brew ffmpeg install path and nothing
else but the openai-whisper Windows install story is rough enough to
warrant gating.

Strict-over-lenient policy: when in doubt, gate. Easier to un-gate after
verified Windows support lands than to leak partial support that
manifests as mid-task failures for Windows users.
2026-05-08 14:27:40 -07:00

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name description version author license platforms metadata
inference-sh-cli Run 150+ AI apps via inference.sh CLI (infsh) — image generation, video creation, LLMs, search, 3D, social automation. Uses the terminal tool. Triggers: inference.sh, infsh, ai apps, flux, veo, image generation, video generation, seedream, seedance, tavily 1.0.0 okaris MIT
linux
macos
windows
hermes
tags related_skills
AI
image-generation
video
LLM
search
inference
FLUX
Veo
Claude

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 keys
  • references/app-discovery.md — Searching and browsing the app catalog
  • references/running-apps.md — Running apps, input formats, output handling
  • references/cli-reference.md — Complete CLI command reference