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llm
Call LLM via CLI for transcription, vision, speech/image generation, piping prompts, sub-agents, ...
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Call LLM via CLI for transcription, vision, speech/image generation, piping prompts, sub-agents, ...
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
ALWAYS follow this style when writing Python / JavaScript code
To select, design, annotate, and verify a data visualization (chart, map, or table).
ALWAYS follow this design guide for any front-end work
Use this for non-trivial analysis, design, diagnosis, review, strategy, or judgment. Skip it for simple lookups, mechanical rewrites, and pure tone tasks.
For CloudFlare development, deployment, e.g. Python CloudFlare Workers
Use when creating demos or POCs
SOC 職業分類に基づく
| name | llm |
| description | Call LLM via CLI for transcription, vision, speech/image generation, piping prompts, sub-agents, ... |
llm --help
llm '2 + 2 = ?'
cat prompt.txt | llm
llm --fragment a.txt --fragment https://example.org/ --fragment "User prompt fragment" --system-fragment system-prompt.txt
llm --system 'Speak German' 'Hi'
llm models list
llm --model gpt-5-nano 'Hi'
llm --query 5-nano 'Hi' # pick first model matching query
llm --attachment audio.opus --model gemini-3-flash-preview 'Transcribe audio'
llm -a a.png -a b.png -s "OCR images"
llm --schema "{... JSON schema ...}" '...' # use JSON schema
llm --usage 'Hi' # show token usage
cat image.jpg | llm 'describe' --attachment - # describe image
llm --option reasoning_effort minimal 'Hi'
llm --extract 'List files by size' # extract first code block
llm cmd 'List files by size' # extract and run first code block
llm embed -c 'Hi' -m 3-small -f base64 # get embedding as base64 (or json) using text-embedding-3-small
Docs: https://llm.datasette.io/en/stable/usage.html
Preferred models:
gpt-5.4-mini: default gpt-5.4-nano: cheapest gemini-2.5-flash: cheap transcription gemini-3.5-flash: best for transcription
Generate speech:
curl https://api.openai.com/v1/audio/speech -H "Authorization: Bearer $OPENAI_API_KEY" -H "Content-Type: application/json" \
-d '{"model": "tts-1", "input": "Hello", "voice": "alloy", "response_format": "mp3"}' --output hello.mp3
voice: alloy, ash, ballad, coral, echo, fable, onyx, nova, sage, shimmer, and verse response_format: mp3, opus, wav Add "instructions": with style / tone
Generate images:
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent?key=$GEMINI_API_KEY" \
-H "Content-Type: application/json" \
-d '{"contents": [{"parts": [{"text": "Cat"}]}], "generationConfig": {"responseModalities": ["IMAGE"], "imageConfig": {"aspectRatio": "16:9"}}}' \
| jq -r '.candidates[0].content.parts[0].inlineData.data' | base64 -d > cat.png
aspectRatio: "1:1", "4:3", "16:9", "3:4", "9:16", "3:2", "2:3", "5:4", "4:5", "21:9" Specify style, layout, font, scene, lighting, ... clearly
Edit images / provide reference images:
IMG=$(base64 -w0 cat.png)
echo '{"contents": [{"parts": [{"text": "Edit image: add bold caption `CAT`"}, {"inlineData": {"mime_type": "image/png", "data": "'"$IMG"'"}}]}], "generationConfig": {"responseModalities": ["IMAGE"]}}' > payload.json
curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.1-flash-image-preview:generateContent?key=$GEMINI_API_KEY" \
-H "Content-Type: application/json" -d @payload.json \
| jq -r '.candidates[0].content.parts[0].inlineData.data' | base64 -d > captioned.png
To write/run tasks as a sub-agent (e.g., code review, code generation), use either Codex or Claude Code:
codex exec 'Review uncommitted changes for errors and style'
codex exec --image screenshot.png 'Compare code with UI and suggest improvements'
npx -y @anthropic-ai/claude-code -p 'Write a web app ...'