This skill as a file
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- Paste it into ChatGPT, Claude, Gemini or any AI chat, free plans included
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Work below the application: read the IR and the disassembly, prove where the time or the bug really is, and tune the runtime.
Complete skill package instant downloadptolemy-compiler-runtime-engineer.md
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Ask Ptolemy something hard.
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Questions before you buy? A person answers, usually the same day: hello@kissmyskills.com
// the file itself
The first lines of the file your AI will read, exactly as delivered. Nothing rewritten for the page.
You are Ptolemy, a Compiler and Language Runtime Engineer who reads the LLVM IR and the disassembly, and runs UBSan and TSan before calling anything a compiler bug. You have been activated to answer what the profiler stopped explaining.
// what's inside
Teams blaming the compiler or the runtime for a bug or a regression they cannot explain at source level.
// two ways to get it
$7 one payment, yours forever
$9/mo cancel anytime, or $59 a year
Subscribers can still buy single files and keep them after they cancel.
// ask your assistant
Go to kissmyskills.com/pages/for-ai-agents and set up the "Ptolemy - Compiler Engineer AI Skill" skill (handle: ptolemy-compiler-runtime-engineer) for me
Paste it into Claude, ChatGPT, Claude Code, Cursor or Codex. Your AI walks you through connecting KissMySkills (about 3 minutes, 7-day free trial) and loads the skill. What your AI will read →
Drop Ptolemy into Claude and get a compiler engineer who reads the generated assembly before agreeing that the compiler is at fault, and is usually able to show it is not.
Ptolemy works the layer underneath everyone else's code: lexing, parsing and AST design, type checking and inference, intermediate representations and SSA form, LLVM and MLIR, optimization passes from inlining and loop transforms to escape analysis and vectorization along with why some of them do not pay, code generation, register allocation and calling conventions, JIT versus ahead-of-time and tiered compilation, garbage collector design across generational, concurrent and region-based collectors and tuning for pause time versus throughput, memory models and atomics, interpreter and bytecode design, profile-guided optimization, ABI stability, foreign function interfaces, and reading a suspected miscompilation from a disassembly.
What you get
How to install
Download the .skill package → open Claude → paste SKILL.md into your Project Instructions or system prompt → describe your requirement → Ptolemy builds the answer. Includes a full worked example so you see exactly what you get.
After checkout, the download link lands in your inbox. Save the file anywhere on your device.
Claude, ChatGPT, Gemini, Grok, or Copilot - whichever one you already use.
Drop it into the system prompt, Project instructions, or custom instructions field.
Your AI is now configured as a specialist. Ask it anything inside its domain.
No technical knowledge required.
With Unlimited there is nothing to download. Connect your AI once, then just ask: "Load Ptolemy from KissMySkills."
Work below the application: read the IR and the disassembly, prove where the time or the bug really is, and tune the runtime. Load it once into Claude Projects and you get a configured Compiler & Language Runtime Engineer without re-explaining context at the start of every session. Measure the system before changing it, prove the change on a real workload in a staging environment, and ship it behind a rollback you have actually tested rather than one you assume works.
Download the .skill package (it contains SKILL.md), paste the contents into Claude Projects Instructions or your AI's system prompt, add your own context and start your first session. Works with Claude, ChatGPT, or any AI chat that accepts system prompts.
Works with Claude (recommended), ChatGPT, Gemini, Perplexity and Copilot, and any AI chat that accepts system prompts. Claude Projects gives the best results.
Without a skill file your AI starts every session as a general assistant. With Ptolemy loaded it applies Compiler & Language Runtime Engineer methodology from the first message, with consistent quality every time. Measure the system before changing it, prove the change on a real workload in a staging environment, and ship it behind a rollback you have actually tested rather than one you assume works.