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antonykamp AI Skills

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Browse published AI skills maintained by antonykamp. Compare skill descriptions and follow each source repository for setup instructions.

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Indexed skills

14

Repository stars across indexed skills

0

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0

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Repository stars may repeat across skills from the same repository.

14 skills

Browse skills

GitHub

tracing-inlining-decisions

antonykamp

Shows inlining decisions during compilation with call tree and reasons for inline/don't-inline. Use to verify critical calls are inlined, understand why inlining failed (too large, recursive, boundary), and opt...

GitHub

detecting-deoptimizations

antonykamp

Traces deoptimization events where execution falls back from compiled code to interpreter. Use to detect deoptimization loops, unstable assumptions, and type instability. Zero transfers in steady-state is the g...

GitHub

tracing-execution-counts

antonykamp

Counts exact execution frequencies (not time) at function/statement level. Shows interpreted vs compiled execution split. Use to verify functions compile (>95% compiled), understand control flow patterns, and v...

GitHub

fetching-truffle-documentation

antonykamp

Fetches information from official GraalVM, Truffle, and Graal compiler documentation for API guidance, performance optimization, compiler flags, profiling, specializations, Bytecode DSL, Truffle DSL, partial ev...

GitHub

profiling-with-cpu-sampler

antonykamp

Time-based sampling profiler showing WHERE execution time is spent (wall-clock time, not frequency). Provides histogram with self/total time, compilation tiers (T0/T1/T2), and flame graphs. Use as FIRST step to...

GitHub

implementing-performance-fixes

antonykamp

[PHASE 4] Implements and validates fixes for verified theories. Entry: PERFORMANCE_THEORIES.md has a verified theory. Outputs: Code fix, BENCHMARK_BASELINE.md new column, theory marked fixed/fix-failed. Next: L...

GitHub

optimizing-truffle-performance

antonykamp

[ORCHESTRATOR] Runs full performance optimization loop for Truffle languages. Entry: User wants to optimize performance. Process: Determines current state, executes appropriate phase (1→2→3→4), loops until perf...

GitHub

profiling-memory-allocations

antonykamp

Experimental allocation profiler tracking memory allocations at guest-language level. Shows allocation sites, object types, and memory pressure patterns. Use to identify allocation-heavy locations, find unneces...

GitHub

detecting-performance-warnings

antonykamp

Detects optimization barriers during Truffle compilation. Identifies virtual calls, non-constant stores, unresolved type checks, and TruffleBoundary issues that prevent peak performance. Reports exact source lo...

GitHub

analyzing-compiler-graphs

antonykamp

Dumps and analyzes Graal IR compiler graphs showing optimization decisions. Use BGV format with bgv2json/seafoam to inspect escape analysis, boxing removal, inlining decisions, and call node types. Reveals what...

GitHub

generating-performance-theories

antonykamp

[PHASE 2] Analyzes code to identify optimization opportunities. Entry: BENCHMARK_BASELINE.md exists AND (no PERFORMANCE_THEORIES.md OR all theories fixed). Outputs: PERFORMANCE_THEORIES.md. Next: PHASE 3 (verif...

GitHub

verifying-performance-theories

antonykamp

[PHASE 3] Proves theories with profiling tools. Entry: PERFORMANCE_THEORIES.md has unverified theories. Outputs: Theory marked verified/falsified. Next: PHASE 4 (implementing-performance-fixes) when one theory...

GitHub

tracing-compilation-events

antonykamp

Logs every compilation event with timing, tier (T1/T2), success/failure, and invalidation reasons. Use to verify hot code is compiling, diagnose compilation failures/bailouts, track recompilation cycles, and un...

GitHub

establishing-benchmark-baseline

antonykamp

[PHASE 1] Creates benchmark suite and performance expectations. Entry: No BENCHMARK_BASELINE.md exists. Outputs: BENCHMARK_BASELINE.md, benchmark files. Next: PHASE 2 (generating-performance-theories).

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.