OpenClaw adapter was straightforward since it uses the same JSON session format.
For agent-generated prompts, I haven't specifically benchmarked agentic workflows yet. The repetition metric detects n-gram repetition within a single prompt, not across prompts. Agent scaffolding tends to inject the same system prefix into every call, which would get flagged if the agent concatenates it into the user message. reprompt currently treats each user turn as a separate prompt, so the system prefix isn't in scope otherwise.
Repetition is weighted 0-15 out of 100, so not dominant. But for heavily templated agent prompts it could actually be the most informative signal. If everything else is boilerplate, the repetition score would separate the prompts where the agent actually varied its approach. Could be an interesting lens for comparing agent frameworks too.
If you have OpenClaw agent sessions I'd be curious what the distribution looks like.
Nice that OpenClaw is already supported. Curious how the scoring holds up against auto-generated agent prompts — the repetition metric especially.
OpenClaw adapter was straightforward since it uses the same JSON session format.
For agent-generated prompts, I haven't specifically benchmarked agentic workflows yet. The repetition metric detects n-gram repetition within a single prompt, not across prompts. Agent scaffolding tends to inject the same system prefix into every call, which would get flagged if the agent concatenates it into the user message. reprompt currently treats each user turn as a separate prompt, so the system prefix isn't in scope otherwise.
Repetition is weighted 0-15 out of 100, so not dominant. But for heavily templated agent prompts it could actually be the most informative signal. If everything else is boilerplate, the repetition score would separate the prompts where the agent actually varied its approach. Could be an interesting lens for comparing agent frameworks too.
If you have OpenClaw agent sessions I'd be curious what the distribution looks like.