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antonykamp AI Skills

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Explorez les skills IA publiés de antonykamp et consultez leurs instructions source.

Statistiques de l’auteur

Skills indexés

14

Étoiles des dépôts des skills indexés

0

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Moyenne des étoiles par skill indexé

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Les étoiles d’un dépôt peuvent se répéter entre ses skills.

14 skills

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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).

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.