Default meta prompt collection: https://gist.github.com/pyros-projects/c77402249b5b45f0a501998870766ae9
Meta prompt collection with creating summaries and context sync (use them when using Cline or other coding assistants): https://gist.github.com/pyros-projects/f6430df8ac6f1ac37e5cfb6a8302edcf
You are an expert software architect and technical project planner. Your task is to create a comprehensive technical implementation plan for a software project based on the provided inputs.
You are an expert developer capable of building a repository analysis application. Your goal is to create a comprehensive plan for implementing a web application that retrieves a GitHub repository identifier (e.g., 127.0.0.1:5001/kohya-ss/sd-scripts) and produces an interactive report usable as context for LLMs.
The web application should accept a GitHub repository ID in the specified URL format and generate an interactive repository report. This report should provide details such as code file descriptions and allow filtering by file extension. The app should collect all relevant information from the repository that an LLM would typically need to make informed decisions. It is up to you to decide what type of information to include, while also offering users the
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| 2 |
| # ----------------------------------------------------------------------------- | |
| # AI-powered Git Commit Function | |
| # Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It: | |
| # 1) gets the current staged changed diff | |
| # 2) sends them to an LLM to write the git commit message | |
| # 3) allows you to easily accept, edit, regenerate, cancel | |
| # But - just read and edit the code however you like | |
| # the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/ | |
| gcm() { |
| # install | |
| pip install shell-gpt | |
| sgpt "hi" (and enter your API key) | |
| # commit the current folder with the generated message | |
| git commit . -m "$(git diff | sgpt --model gpt-4o --code 'Write concise, informative commit messages: Start with a summary in imperative mood, explain the 'why' behind changes, keep the summary under 50 characters, use bullet points for multiple changes, avoid using the word refactor, instead explain what was done, and reference related issues or tickets. What you write will be passed to git commit -m "[message]"')" | |
| # you could assign an alias now if you like it |
| You will now act as a prompt generator. | |
| I will describe an image to you, and you will create a prompt that could be used for image-generation. | |
| Once I described the image, give a 5-word summary and then include the following markdown. | |
|  | |
| where {description} is: | |
| {sceneDetailed}%20{adjective}%20{charactersDetailed}%20{visualStyle}%20{genre}%20{artistReference} | |
| Make sure the prompts in the URL are encoded. Don't quote the generated markdown or put any code box around it. |
| # %% | |
| import replicate | |
| model = replicate.models.get("prompthero/openjourney") | |
| version = model.versions.get("9936c2001faa2194a261c01381f90e65261879985476014a0a37a334593a05eb") | |
| PROMPT = "mdjrny-v4 style 360 degree equirectangular panorama photograph, Alps, giant mountains, meadows, rivers, rolling hills, trending on artstation, cinematic composition, beautiful lighting, hyper detailed, 8 k, photo, photography" | |
| output = version.predict(prompt=PROMPT, width=1024, height=512) | |
| # %% | |
| # download the iamge from the url at output[0] | |
| import requests |
| import torch | |
| import numpy as np | |
| import k_diffusion as K | |
| from PIL import Image | |
| from torch import autocast | |
| from einops import rearrange, repeat | |
| def pil_img_to_torch(pil_img, half=False): | |
| image = np.array(pil_img).astype(np.float32) / 255.0 |
