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Fix dogfooding files
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dogfooding/README.md

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# AMD Skills Dogfooding: `local-ai-use`
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# AMD Skills Dogfooding
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The goal of this skill is to teach your AI agent to use image generation, text generation, and text to speech locally.
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Welcome to AMD Skills Dogfooding.
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## Requirements
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## Step 1 - Understanding which skills are available
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This dogfooding program assumes you already have `claude code` setup on your machine.
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* Run `claude "Which skills can you see?" --model sonnet`. You should see a list of skills that should not include anythink related to local LLM usage.
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* Make sure there is no `AFENTS.md` file on your local folder.
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Participatns using other are still encouraged to participate. Just please note that you may have to adapt some of the instructions provided below to your specific agent.
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## Step 2 - Enabling claude to see `local-ai-use`
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In the future this will be enabled directly through claude's marketplace. For now, we have to manually add it.
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Please choose a skill to get started.
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* Clone `https://github.com/amd/skills`
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* Move the `local-ai-use` skill from the repo to `.claude/skills/`
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* Run `claude "Which skills can you see?" --model sonnet`. You should see a list of skills that includes `local-ai-use`.
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## Step 3 - Running the skill
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Open Claude and run the prompt:
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```
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Learn how to do image generation locally
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```
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Followed by
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```
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Generate the image of a cat
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```
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Claude should install Lemonade locally on your device and allow you to generate images locally after the first setup run.
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## Step 4 - (Optional) Going beyond
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The `local-ai-use` can also help you with text to speech and speech to text locally. Simply ask claude for help there.
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## Step 5 - (Optional) Try to get things done without AMD Skills
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Remove the added skills from `.claude/skills/` and rerun the experiment above. This should lead to a high variance in execution length and token usage.
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* Model being successful after significant token usage.
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* Model providing a knowledge article instead of actually learning how to do it.
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* Model attempting to come up with a custom strategy to generate images locally, resulting in very low-quality assets.
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* [local-ai-use](./local-ai-use.md): Teach your agent how to run image generation locally.

dogfooding/local-ai-use.md

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# AMD Skills Dogfooding
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# AMD Skills Dogfooding: `local-ai-use`
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Welcome to AMD Skills Dogfooding.
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The goal of this skill is to teach your AI agent to use image generation, text generation, and text to speech locally.
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## Requirements
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This dogfooding program assumes you already have `claude code` setup on your machine.
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## Step 1 - Understanding which skills are available
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Participatns using other are still encouraged to participate. Just please note that you may have to adapt some of the instructions provided below to your specific agent.
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* Run `claude "Which skills can you see?" --model sonnet`. You should see a list of skills that should not include anythink related to local LLM usage.
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* Make sure there is no `AFENTS.md` file on your local folder.
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## Step 2 - Enabling claude to see `local-ai-use`
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Please choose a skill to get started.
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In the future this will be enabled directly through claude's marketplace. For now, we have to manually add it.
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* [local-ai-use](./local-ai-use.md): Teach your agent how to run image generation locally.
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* Clone `https://github.com/amd/skills`
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* Move the `local-ai-use` skill from the repo to `.claude/skills/`
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* Run `claude "Which skills can you see?" --model sonnet`. You should see a list of skills that includes `local-ai-use`.
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## Step 3 - Running the skill
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Open Claude and run the prompt:
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```
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Learn how to do image generation locally
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```
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Followed by
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```
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Generate the image of a cat
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```
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Claude should install Lemonade locally on your device and allow you to generate images locally after the first setup run.
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## Step 4 - (Optional) Going beyond
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The `local-ai-use` can also help you with text to speech and speech to text locally. Simply ask claude for help there.
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## Step 5 - (Optional) Try to get things done without AMD Skills
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Remove the added skills from `.claude/skills/` and rerun the experiment above. This should lead to a high variance in execution length and token usage.
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* Model being successful after significant token usage.
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* Model providing a knowledge article instead of actually learning how to do it.
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* Model attempting to come up with a custom strategy to generate images locally, resulting in very low-quality assets.

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