Instructions
During you interaction with the user, if you find anything reusable in this project (e.g. version of a library, model name), especially about a fix to a mistake you made or a correction you received, you should take note in the Lessons section in the scratchpad.md file so you will not make the same mistake again.
You should also use the scratchpad.md file as a scratchpad to organize your thoughts. Especially when you receive a new task, you should first review the content of the scratchpad, clear old different task if necessary, first explain the task, and plan the steps you need to take to complete the task. You can use todo markers to indicate the progress, e.g.
[X] Task 1
[ ] Task 2
Also update the progress of the task in the Scratchpad when you finish a subtask.
Especially when you finished a milestone, it will help to improve your depth of task accomplishment to use the scratchpad to reflect and plan.
The goal is to help you maintain a big picture as well as the progress of the task. Always refer to the Scratchpad when you plan the next step.
Note all the tools are in python. So in the case you need to do batch processing, you can always consult the python files and write your own script.
Screenshot Verification
The screenshot verification workflow allows you to capture screenshots of web pages and verify their appearance using LLMs. The following tools are available:
- Screenshot Capture:
venv/bin/python tools/screenshot_utils.py URL [--output OUTPUT] [--width WIDTH] [--height HEIGHT]
- LLM Verification with Images:
venv/bin/python tools/llm_api.py --prompt "Your verification question" --provider {openai|anthropic} --image path/to/screenshot.png
Example workflow:
from screenshot_utils import take_screenshot_sync
from llm_api import query_llm
# Take a screenshot
screenshot_path = take_screenshot_sync('https://example.com', 'screenshot.png')
# Verify with LLM
response = query_llm(
"What is the background color and title of this webpage?",
provider="openai", # or "anthropic"
image_path=screenshot_path
)
print(response)
LLM
You always have an LLM at your side to help you with the task. For simple tasks, you could invoke the LLM by running the following command:
venv/bin/python ./tools/llm_api.py --prompt "What is the capital of France?"
But usually it's a better idea to check the content of the file and use the APIs in the tools/llm_api.py file to invoke the LLM if needed.
Web browser
You could use the tools/web_scraper.py file to scrape the web.
venv/bin/python ./tools/web_scraper.py --max-concurrent 3 URL1 URL2 URL3
This will output the content of the web pages.
Search engine
You could use the tools/search_engine.py file to search the web.
venv/bin/python ./tools/search_engine.py "your search keywords"
This will output the search results in the following format:
URL: https://example.com
Title: This is the title of the search result
Snippet: This is a snippet of the search result
Lessons
User Specified Lessons
- You have a python venv in ./venv.
- Include info useful for debugging in the program output.
- Read the file before you try to edit it.
- Use LLM to perform flexible text understanding tasks. First test on a few files. After success, make it parallel.
Windsurf learned
Consult the scratchpad.md file for lessons.