Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add chemany/Mente --skill cligit clone --depth 1 https://github.com/chemany/MenteWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chemany/mente/cli)<a href="https://agentmods.dev/skills/chemany/mente/cli"><img src="https://agentmods.dev/badge/skills/chemany/mente/cli/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/chemany/mente/cli"><img src="https://agentmods.dev/badge/skills/chemany/mente/cli.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.01291 |
| Opus 5.5 | $0.00030 | $0.00516 |
| Sonnet 5.5 | $0.00015 | $0.00258 |
| Haiku 4.5 | $0.00007 | $0.00129 |
Grade C, and why
inference-sh-cli scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -fsSL https://cli.inference.sh | sh Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://cli.inference.sh | sh This is a copy
92% identical to inference-sh-cli — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 156 lines · 74 tokens per session scan C 696c22c0b96a
inference-sh-cli is a skill published in the GitHub repository chemany/Mente (11 stars, last pushed 5mo ago), licensed MIT. It adds 74 tokens to every session and 1,291 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). It is 92% identical to inference-sh-cli, differing in 3 lines, and is treated as a copy.
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