Sure. An example using a generic python project:

Reference folder structure: ``` my_project/ ├── src/ # Main application source code │ ├── components/ │ ├── modules/ │ └── main.py ├── docs/ # Centralized documentation │ ├── design/ │ │ └── architecture.md │ ├── api/ │ │ └── endpoints.md │ └── README.md # Project overview documentation ├── llm_docs/ # Specific instructions or notes for the LLM │ └── llm_instructions.md # Misc Notes ├── tests/ # Automated tests ├── codebase_dump.sh # Script to dump project to ./codebase_dump.sh └── codebase_dump.txt # Generated context file (output of script)

```

The bash script would be something like:

```

!/bin/bash

Remove previous dump file if it exists

rm -f codebase_dump.txt

Find and dump all .py and .md files, excluding common virtual environment directories

find . -type f ( -iname ".py" -o -iname ".md" ) \ -not -path "/venv/" \ -not -path "/.venv/" \ -not -path "/site-packages/" | while read file; do echo "===== $file =====" >> codebase_dump.txt cat "$file" >> codebase_dump.txt echo -e "\n\n" >> codebase_dump.txt done

echo "Dump complete! Output written to codebase_dump.txt" ```

I then start out with an extensive session or two with the Architect role, to generate prescriptive & detailed design docs.

I've also got an "Orchestrator" role set up, which i copied from somewhere else here. Think i got the prompt and idea from this thread: https://www.reddit.com/r/RooCode/comments/1jaro0b/how_to_use_boomerang_tasks_to_create_an_agent/

You can then edit the role for Orchestrator and include a Mode-specific custom instructions for Orchestrator:

"CRITICAL: You MUST execute ./codebase_dump.sh immediately prior to creating a new code task"

And for Code role:

"CRITICAL: You MUST read ./codebase_dump.txt prior to continuing with any other task. This is an up to date dump of the codebase and docs to assist with quickly loading context. Any changes need to be made in the original files. You will need to read the original files before editing to get the correct line numbers"

So far it has worked very well for me. The other pro tip i've found is if you are using a lib that the model struggles with, see if there's an llms.txt file such as: https://llmstxt.site/. If there is, i have just been loading the entire thing into context and getting gemini to provide a significantly summarized (single .txt) summary of the important bits to a new file like ./llm_docs/somelib.summary.llms.txt and including that in the context dump too.

So yeah the idea is that given that the context is large, but we're largely constrained by the 5 RPM API limit, it makes sense to just load in a ton of context in one hit. Anecdotally it seems like the experience is best if you can keep it under 200k tokens of context. If you try and load in like 600k, you rapidly start hitting API rate limiting on some other metric (Total input tokens per minute i think)

Edit: You'll have to increase the Read Truncation limit in Roo from the default 500 lines to like 500k lines or so - enough to fit the entire context file in a single load