infsh-cli
Access 150+ cloud-based AI apps via CLI without GPU setup or infrastructure.
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Install Skill
Run in your terminal
3
installs
3
this week
304
stars
What it does
Covers image generation (FLUX, Gemini, Grok), video creation (Veo, Seedance, OmniHuman), LLMs (Claude, Gemini via OpenRouter), web search (Tavily, Exa), 3D modeling, and Twitter/X automation
Automatically uploads local files when provided as paths; supports batch operations and async execution with task status polling
Simple command structure: infsh app list , infsh app run <app> --input data.json , wit
Installation Guide
How to use infsh-cli on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
infsh-cli
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches infsh-cli from inferen-sh/skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate infsh-cli. Access via /infsh-cli in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
inference.sh
Run 150+ AI apps in the cloud with a simple CLI. No GPU required.

Install CLI
curl -fsSL https://cli.inference.sh | sh
infsh login
What does the installer do? The install script detects your OS and architecture, downloads the correct binary from
dist.inference.sh, verifies its SHA-256 checksum, and places it in your PATH. That's it — no elevated permissions, no background processes, no telemetry. If you have cosign installed, the installer also verifies the Sigstore signature automatically.Manual install (if you prefer not to pipe to sh):
# Download the binary and checksums curl -LO https://dist.inference.sh/cli/checksums.txt curl -LO $(curl -fsSL https://dist.inference.sh/cli/manifest.json | grep -o '"url":"[^"]*"' | grep $(uname -s | tr A-Z a-z)-$(uname -m | sed 's/x86_64/amd64/;s/aarch64/arm64/') | head -1 | cut -d'"' -f4) # Verify checksum sha256sum -c checksums.txt --ignore-missing # Extract and install tar -xzf inferencesh-cli-*.tar.gz mv inferencesh-cli-* ~/.local/bin/inferencesh
Quick Examples
# Generate an image
infsh app run falai/flux-dev-lora --input '{"prompt": "a cat astronaut"}'
# Generate a video
infsh app run google/veo-3-1-fast --input '{"prompt": "drone over mountains"}'
# Call Claude
infsh app run openrouter/claude-sonnet-45 --input '{"prompt": "Explain quantum computing"}'
# Web search
infsh app run tavily/search-assistant --input '{"query": "latest AI news"}'
# Post to Twitter
infsh app run x/post-tweet --input '{"text": "Hello from AI!"}'
# Generate 3D model
infsh app run infsh/rodin-3d-generator --input '{"prompt": "a wooden chair"}'
Local File Uploads
The CLI automatically uploads local files when you provide a path instead of a URL:
# Upscale a local image
infsh app run falai/topaz-image-upscaler --input '{"image": "/path/to/photo.jpg", "upscale_factor": 2}'
# Image-to-video from local file
infsh app run falai/wan-2-5-i2v --input '{"image": "./my-image.png", "prompt": "make it move"}'
# Avatar with local audio and image
infsh app run bytedance/omnihuman-1-5 --input '{"audio": "/path/to/speech.mp3", "image": "/path/to/face.jpg"}'
# Post tweet with local media
infsh app run x/post-create --input '{"text": "Check this out!", "media": "./screenshot.png"}'
Commands
| Task | Command |
|---|---|
| List all apps | infsh app list |
| Search apps | infsh app list --search "flux" |
| Filter by category | infsh app list --category image |
| Get app details | infsh app get google/veo-3-1-fast |
| Generate sample input | infsh app sample google/veo-3-1-fast --save input.json |
| Run app | infsh app run google/veo-3-1-fast --input input.json |
| Run without waiting | infsh app run <app> --input input.json --no-wait |
| Check task status | infsh task get <task-id> |
What's Available
| Category | Examples |
|---|---|
| Image | FLUX, Gemini 3 Pro, Grok Imagine, Seedream 4.5, Reve, Topaz Upscaler |
| Video | Veo 3.1, Seedance 1.5, Wan 2.5, OmniHuman, Fabric, HunyuanVideo Foley |
| LLMs | Claude Opus/Sonnet/Haiku, Gemini 3 Pro, Kimi K2, GLM-4, any OpenRouter model |
| Search | Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract |
| 3D | Rodin 3D Generator |
| Twitter/X | post-tweet, post-create, dm-send, user-follow, post-like, post-retweet |
| Utilities | Media merger, caption videos, image stitching, audio extraction |
Related Skills
# Image generation (FLUX, Gemini, Grok, Seedream)
npx skills add inference-sh/skills@ai-image-generation
# Video generation (Veo, Seedance, Wan, OmniHuman)
npx skills add inference-sh/skills@ai-video-generation
# LLMs (Claude, Gemini, Kimi, GLM via OpenRouter)
npx skills add inference-sh/skills@llm-models
# Web search (Tavily, Exa)
npx skills add inference-sh/skills@web-search
# AI avatars & lipsync (OmniHuman, Fabric, PixVerse)
npx skills add inference-sh/skills@ai-avatar-video
# Twitter/X automation
npx skills add inference-sh/skills@twitter-automation
# Model-specific
npx skills add inference-sh/skills@flux-image
npx skills add inference-sh/skills@google-veo
# Utilities
npx skills add inference-sh/skills@image-upscaling
npx skills add inference-sh/skills@background-removal
Reference Files
Documentation
- Agent Skills Overview - The open standard for AI capabilities
- Getting Started - Introduction to inference.sh
- What is inference.sh? - Platform overview
- Apps Overview - Understanding the app ecosystem
- CLI Setup - Installing the CLI
- Workflows vs Agents - When to use each
- Why Agent Runtimes Matter - Runtime benefits
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- AAditi Khanna★★★★★Dec 20, 2024
Solid pick for teams standardizing on skills: infsh-cli is focused, and the summary matches what you get after install.
- KKofi Smith★★★★★Dec 16, 2024
Useful defaults in infsh-cli — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- GGanesh Mohane★★★★★Dec 8, 2024
I recommend infsh-cli for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- HHarper Chawla★★★★★Dec 4, 2024
infsh-cli is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- SSakshi Patil★★★★★Nov 27, 2024
Solid pick for teams standardizing on skills: infsh-cli is focused, and the summary matches what you get after install.
- LLuis Thomas★★★★★Nov 23, 2024
Keeps context tight: infsh-cli is the kind of skill you can hand to a new teammate without a long onboarding doc.
- YYash Thakker★★★★★Nov 19, 2024
Useful defaults in infsh-cli — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- KKaira Desai★★★★★Nov 7, 2024
I recommend infsh-cli for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- BBenjamin Khan★★★★★Oct 26, 2024
Keeps context tight: infsh-cli is the kind of skill you can hand to a new teammate without a long onboarding doc.
- CChaitanya Patil★★★★★Oct 18, 2024
infsh-cli is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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