MCPInstall

llm-cli

Process textual and multimedia files with various LLM providers using the llm CLI. Supports both non-interactive and interactive modes with model selection, config persistence, and file input handling.

Installs
230
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/glebis/claude-skills --skill llm-cli
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads llm-cli automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

LLM CLI Skill

Purpose

This skill enables seamless interaction with multiple LLM providers (OpenAI, Anthropic, Google Gemini, Ollama) through the llm CLI tool. It processes textual and multimedia information with support for both one-off executions and interactive conversation modes.

When to Use This Skill

Trigger this skill when:

  • User wants to process text/files with an LLM
  • User needs to choose between multiple available LLMs
  • User wants interactive conversation with an LLM
  • User needs to pipe content through an LLM for processing
  • User wants to use specific model aliases (e.g., "claude-opus", "gpt-4o")

Example user requests:

  • "Process this file with Claude"
  • "Analyze this text with the fastest available model"
  • "Start an interactive chat with OpenAI"
  • "Use Gemini to summarize this document"
  • "Chat mode with my local Ollama instance"

Supported Providers & Models

OpenAI

  • Latest Models (2025):
    • gpt-5 - Most advanced model
    • gpt-4-1 / gpt-4.1 - Latest high-performance
    • gpt-4-1-mini / gpt-4.1-mini - Smaller, faster version
    • gpt-4o - Multimodal omni model
    • gpt-4o-mini - Lightweight multimodal
    • o3 - Advanced reasoning
    • o3-mini / o3-mini-high - Reasoning variants

Aliases: openai, gpt

Anthropic

  • Latest Models (2025):
    • claude-sonnet-4.5 - Latest flagship model
    • claude-opus-4.1 - Complex task specialist
    • claude-opus-4 - Coding specialist
    • claude-sonnet-4 - Balanced performance
    • claude-3.5-sonnet - Previous generation
    • claude-3.5-haiku - Fast & efficient

Aliases: anthropic, claude

Google Gemini

  • Latest Models (2025):
    • gemini-2.5-pro - Most advanced
    • gemini-2.5-flash - Default fast model
    • gemini-2.5-flash-lite - Speed optimized
    • gemini-2.0-flash - Previous generation
    • gemini-2.5-computer-use - UI interaction

Aliases: google, gemini

Ollama (Local)

  • Popular Models:
    • llama3.1 - Meta's latest (8b, 70b, 405b)
    • llama3.2 - Compact versions (1b, 3b)
    • mistral-large-2 - Mistral flagship
    • deepseek-coder - Code specialist
    • starcode2 - Code models

Aliases: ollama, local

Workflow Overview

User Input (with optional model)
    ↓
Check Available Providers (env vars)
    ↓
Determine Model to Use:
  - If specified: Use provided model
  - If ambiguous: Show selection menu
  - Otherwise: Use last remembered choice
    ↓
Load/Create Config (~/.claude/llm-skill-config.json)
    ↓
Detect Input Type:
  - stdin/piped
  - file path
  - inline text
    ↓
Execute llm CLI:
  - Non-interactive: Process & return
  - Interactive: Keep conversation loop
    ↓
Save Model Choice to Config

Features

1. Provider Detection

  • Checks environment variables for API keys
  • Suggests available LLM providers on first run
  • Detects: OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, OLLAMA_BASE_URL

2. Model Selection

  • Accept model aliases (gpt-4o, claude-opus, gemini-2.5-pro)
  • Accept provider aliases (openai, anthropic, google, ollama)
  • Interactive menu when selection is ambiguous
  • Remembers last used model in ~/.claude/llm-skill-config.json

3. Input Processing

  • Accepts stdin/piped input
  • Processes file paths (detects: .txt, .md, .json, .pdf, images)
  • Handles inline text prompts
  • Supports multimedia files with appropriate encoding

4. Execution Modes

Non-Interactive (Default)

llm "Your prompt here"
llm --model gpt-4o "Process this text"
llm < file.txt
cat document.md | llm "Summarize"

Interactive Mode

llm --interactive
llm -i
llm --model claude-opus --interactive

5. Configuration

Persistent config location: ~/.claude/llm-skill-config.json

{
  "last_model": "claude-sonnet-4.5",
  "default_provider": "anthropic",
  "available_providers": ["openai", "anthropic", "google", "ollama"]
}

Implementation Details

Core Files

  • llm_skill.py - Main skill orchestration
  • providers.py - Provider detection & config
  • models.py - Model definitions & aliases
  • executor.py - Execution logic (interactive/non-interactive)
  • input_handler.py - Input type detection

Key Functions

detect_providers()

  • Scans environment for provider API keys
  • Returns dict of available providers

get_model_selector(input_text, provider=None)

  • Returns selected model, showing menu if needed
  • Respects last_model config preference

load_input(input_source)

  • Handles stdin, file paths, or inline text
  • Returns content string

execute_llm(content, model, interactive=False)

  • Calls llm CLI with appropriate parameters
  • Manages stdin/stdout for interactive mode

Usage in Claude Code

When user invokes this skill, Claude should:

  1. Parse input for model specification (e.g., --model gpt-4o)
  2. Call skill with content and optional model parameter
  3. Wait for provider/model selection if needed
  4. Execute and return results
  5. For interactive mode, maintain conversation loop

Error Handling

  • If no providers available: Suggest installing API keys
  • If model not found: Show available models for chosen provider
  • If llm CLI not installed: Suggest installation via pip install llm
  • If file not readable: Fall back to treating as inline text

Configuration

Users can pre-configure preferences:

{
  "last_model": "claude-sonnet-4.5",
  "default_provider": "anthropic",
  "interactive_mode": false,
  "available_providers": ["openai", "anthropic"]
}

Slash Command Integration

Support /llm command:

/llm process this text
/llm --interactive
/llm --model gpt-4o analyze this

FAQ

What does the llm-cli skill do?

Process textual and multimedia files with various LLM providers using the llm CLI. Supports both non-interactive and interactive modes with model selection, config persistence, and file input handling.

How do I install llm-cli?

Run npx skills add https://github.com/glebis/claude-skills --skill llm-cli in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/llm-cli/ (personal) or .claude/skills/llm-cli/ (project).

Which AI agents can use llm-cli?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is llm-cli safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains llm-cli?

It is published by glebis in glebis/claude-skills. It has 230 recorded installs.

More from glebis/claude-skills

View all →

google-image-search

glebis/claude-skills

Search and download images via Google Custom Search API with LLM-powered selection. This skill should be used when finding images for articles, presentations, research documents, or enriching Obsidian notes with relevant visuals. Supports simple queries, batch processing from JSON config, automatic config generation from terms, and full note enrichment with automatic image insertion below headings.

Media & Creative643 installs

pdf-generation

glebis/claude-skills

Professional PDF generation from markdown using Pandoc with Eisvogel template and EB Garamond fonts. Use when converting markdown to PDF, creating white papers, research documents, marketing materials, or technical documentation. Supports both English and Russian documents with professional typography and color-coded themes. Mobile-optimized layout (6x9) by default for Telegram bot context, desktop/print layout (A4) for other contexts.

Docs & Writing482 installs

health-data

glebis/claude-skills

Open the skill to read its SKILL.md instructions.

firecrawl-research

glebis/claude-skills

Open the skill to read its SKILL.md instructions.

deep-research

glebis/claude-skills

Open the skill to read its SKILL.md instructions.

presentation-generator

glebis/claude-skills

Generate interactive HTML presentations with neobrutalism styling, ASCII art decorations, and Agency brand colors. Outputs HTML (interactive with navigation), PNG (individual slides via Playwright), and PDF. References brand-agency skill for colors and typography. Use when creating presentations, slide decks, pitch materials, or visual summaries.

Docs & Writing320 installs

Popular ai & agents skills

View category →

google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs