It doesn't work so well in my experience. I am currently wrapping (or asking the LLM to wrap) the commit message prompt in a script call.
1. the LLM is instructed on how to write a commit message and never include co-authorship
2. the LLM is asked to produce a commit message
3. the LLM output is parsed by a script which removes co-authorship if the LLM chooses to include it nevertheless
Also for future reference, Copilot - specifically - includes a configuration flag to toggle the co-authorship (see `copilot help config`):
> `includeCoAuthoredBy`: whether to instruct the agent to add a Co-authored-by trailer to git commits; defaults to `true`.
This means that, if you don't explicitely configure otherwise, the LLM is specifically instructed to include co-authorship in its higher level instructions.
Actually what it seems to tackle at its core is discoverability. Which should be built in in each MCP server as it's not that difficult, instead, we see MCP servers with 50+ methods.
The issue here is the row based format. You simply can't filter on arbitrary columns with that. Either use an external warehouse or a columnar plug-in like Timescale.
I am looking to that exact concept - for a different mean - to develop my agent orchestration hobby project.
LLM working in a «feature folder» where it stores change-logs, documentation, summaries, requirements, attachments, and so on. What I will be looking into very soon, is also storing the session id, for summarisation, history context, and so on.
I will definitely steal some concept from your project.
Glad it might be of use to you, but for your project, that case, a project from someone else (https://github.com/matt1398/claude-devtools) might be able to give you even more for inspiration :). Good luck with your project!
For Claude the most pollution usually comes from Claude itself.
It's worth noting thet just by setting the right tone of voice, choosing the right words, and instructing it to be concise, surgical in what it says and writes, things change drastically - like night and day.
It then starts obeying, CRITICALs are barely needed anymore and the docs it produces are tidy and pretty.
The LLM instructed to run the initialization script as the first thing, before reasoning about the use request (this proved tricky to achieve). The scripts greps the content matter out of the skill files, along with the file path.
I have no clue if this outperforms an embedded index.
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