skills/mmx-cli/SKILL.md
Generate text, images, video, speech, and music via the MiniMax AI platform. Covers text generation (MiniMax-M3 model), image generation (image-01), video generation (Hailuo-2.3), speech synthesis (speech-2.8-hd, 300+ voices), music generation (music-2.6 with lyrics, cover, and instrumental), and web search. Use when the user needs to create AI-generated multimedia content, produce narrated audio from text, compose music, or search the web through MiniMax AI services.
npx skillsauth add agentscope-ai/openjudge mmx-cliInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Generate multimedia content (text, images, video, speech, music) via the MiniMax AI platform.
npx skills add MiniMax-AI/cli@skill -g -y
Or manually:
npm install -g @minimax-ai/cli
Set your API key:
export MINIMAX_API_KEY=your_api_key_here
| Capability | Command | Model |
|------------|---------|-------|
| Text generation | mmx text generate | MiniMax-M3 |
| Image generation | mmx image generate | image-01 |
| Video generation | mmx video generate | Hailuo-2.3 |
| Speech synthesis | mmx speech generate | speech-2.8-hd |
| Music generation | mmx music generate | music-2.6 |
| Web search | mmx search | — |
# Generate text
mmx text generate "Summarize the key benefits of reinforcement learning"
# Generate an image
mmx image generate "A futuristic city skyline at sunset, photorealistic"
# Generate a short video clip
mmx video generate "A golden retriever playing in autumn leaves"
# Synthesize speech from text
mmx speech generate --text "Hello, welcome to the demo." --voice Calm_Woman
# Generate music with lyrics
mmx music generate --lyrics "Rise and shine, a brand new day" --style "pop upbeat"
# Web search
mmx search "latest advances in LLM evaluation"
| Info | Required? | Notes |
|------|-----------|-------|
| MINIMAX_API_KEY | Yes | From MiniMax platform |
| Prompt or text | Yes | Describe what to generate |
| Voice name | No | For speech; run mmx speech list-voices to browse 300+ options |
| Output path | No | Default saves to current directory |
--output json flag for structured output suitable for pipelines.--non-interactive and --quiet flags in automated / agent workflows.mmx --help or mmx <subcommand> --help to see all options.development
Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.
tools
Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.
testing
Review academic papers for correctness, quality, and novelty using OpenJudge's multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.zip). Covers 10 disciplines: cs, medicine, physics, chemistry, biology, economics, psychology, environmental_science, mathematics, social_sciences. Use when the user asks to review, evaluate, critique, or assess a research paper, check references, or verify a BibTeX file.
development
Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.