Who it is forA researcher or AI evaluator who wants to see how well LLMs perform on an esoteric language task.
The problemJudging how LLMs handle an unfamiliar language needs the language rules and published results side by side.
The outcomeYou have the spec's rules in hand and the model results from the benchmarks site for comparison.
What you need first
- A Rust toolchain with cargo installed
- A terminal with access to the repo's files (clone of github.com/dom96/KillSwitch)
- A web browser for reading SPEC.md and the benchmarks site, if you use those use cases
Steps
- Open SPEC.md in the repo to read the language specification.
- Visit https://killswitch-lang.org for the official project information.
- Visit https://bench.killswitch-lang.org to see LLM model benchmarks for Kill Switch tasks.
- Note which spec rules the benchmark tasks depend on before you draw any conclusions.
Prompt for your agent
Read SPEC.md in the dom96/KillSwitch repo and the benchmark results at https://bench.killswitch-lang.org. Summarize the main language rules and list the models and scores the benchmark shows for <task type>. Verify that each claim you report is written on the spec or benchmark pages.
Caveat. The benchmark site is linked from the README, but the README does not describe how the benchmarks were run, so treat results as published by the project.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for dom96/KillSwitch and "Read the Kill Switch spec and LLM benchmark results"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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