Apple Silicon skill for M-series development and profiling. An agent skill from mohitmishra786/low-level-dev-skills.

MITAuto-check passedGame Development

Install Apple Silicon

skills CLI
$ npx skills add mohitmishra786/low-level-dev-skills --skill apple-silicon -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mohitmishra786/low-level-dev-skills apple-silicon --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mohitmishra786/low-level-dev-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/platform/apple-silicon .claude/skills/apple-silicon && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
apple-silicon
GitHub stars
252
Token cost
~1.6k tokens
SKILL.md length
417 words
Files
1
Skills in repo
138
Repo updated
First seen
Licence
MIT

At a glance

Apple Silicon skill for M-series development and profiling. An agent skill from mohitmishra786/low-level-dev-skills.

  • Works in 10 steps: Unified memory architecture → Hardware information → 16KB page size considerations → …
  • Leveraging unified memory
  • SKILL.md covers Purpose, When to Use, Workflow and Common Problems, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Apple Silicon is an agent skill from mohitmishra786/low-level-dev-skills. Apple Silicon skill for M-series development and profiling. Use when leveraging unified memory, Metal Performance Shaders, Instruments profiling, sysctl hardware queries, Rosetta 2 behavior, or 16KB page size considerations. Activates on queries about Apple Silicon, unified memory, AMX, MPS, Instruments, Rosetta, or M-series page size.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Game Development, covering Shaders and Performance optimization. The repository describes itself as: A curated suite of AI agent skills for systems and low-level programming with C/C++, Rust, and Zig toolchains, covering compilers, debuggers, profilers, build systems…. The licence is MIT.

When your agent uses it

  • Leveraging unified memory
  • Metal Performance Shaders
  • Instruments profiling
  • Sysctl hardware queries

Example prompts

  • “/apple-silicon”

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Unified memory architecture
  2. Hardware information
  3. 16KB page size considerations
  4. AMX (Apple Matrix Coprocessor)
  5. Metal Performance Shaders (MPS)
  6. Instruments profiling
  7. Command-line debugging tools
  8. Rosetta 2 translation
  9. Memory tagging (ARM MTE)
  10. Build and perf tips

What it can do on your machine

Read from SKILL.md and the folder at commit bdc5847. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash, c and objc).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Apple Silicon loads about 1.6k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 417 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from mohitmishra786/low-level-dev-skills at commit bdc5847, republished under its MIT licence (© mohitmishra786). 417 words, ~1,635 tokens.

Download SKILL.mdSave it as .claude/skills/apple-silicon/SKILL.md (or your agent's skills folder).
name
apple-silicon
description
Apple Silicon skill for M-series development and profiling. Use when leveraging unified memory, Metal Performance Shaders, Instruments profiling, sysctl hardware queries, Rosetta 2 behavior, or 16KB page size considerations. Activates on queries about Apple Silicon, unified memory, AMX, MPS, Instruments, Rosetta, or M-series page size.

Apple Silicon

Purpose

Guide agents through Apple Silicon (M-series) development: unified memory architecture, AMX matrix coprocessor access via Accelerate, Metal Performance Shaders for GPU compute, sysctl hardware queries, Instruments profiling, command-line leak tools, Rosetta 2 translation behavior, and 16KB page size implications.

When to Use

  • Optimizing native ARM64 apps on macOS for M1/M2/M3/M4
  • Using GPU/NPU compute without discrete GPU PCIe transfers
  • Profiling memory and CPU with Instruments or command-line tools
  • Understanding Rosetta 2 compatibility for x86 binaries
  • Adapting code for 16KB page size on Apple Silicon
  • Accessing matrix acceleration via Accelerate/vDSP/BLAS

Workflow

1. Unified memory architecture
Apple Silicon SoC
├── CPU cores (P + E cores)
├── GPU cores
├── Neural Engine (NPU)
└── Unified DRAM — single address space, no PCIe copy

Implications:

  • cudaMemcpy equivalent is unnecessary for CPU↔GPU on Metal
  • Memory bandwidth shared across agents — profile holistically
  • Process memory includes all unified allocations
2. Hardware information
bash
# CPU and chip info
sysctl -n machdep.cpu.brand_string
sysctl hw.physicalcpu hw.logicalcpu
sysctl hw.memsize

# ARM64 features (keys vary by chip — grep if specific FEAT_* is missing)
sysctl -a hw.optional.arm 2>/dev/null | grep -iE 'sve|bf16|mte'

# Cache line size
sysctl hw.cachelinesize

# Page size (16KB on macOS Apple Silicon)
sysctl hw.pagesize    # 16384
getconf PAGESIZE
3. 16KB page size considerations

macOS on Apple Silicon uses 16KB pages (not 4KB):

c
// Align hot buffers to page size
size_t page = sysconf(_SC_PAGESIZE);  // 16384
void *buf = aligned_alloc(page, size);

// mmap alignment must be page-aligned
mmap(NULL, size, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0);

Impact:

  • posix_memalign minimum alignment often 16KB for large allocs
  • JVM/Go runtimes auto-tune; custom allocators must adapt
  • Test on device — x86 CI may use 4KB pages
4. AMX (Apple Matrix Coprocessor)

AMX is undocumented at ISA level; access through frameworks:

c
// Accelerate framework — uses AMX internally for matrix ops
#include <Accelerate/Accelerate.h>

void matrix_multiply(const float *A, const float *B, float *C,
                     int M, int N, int K) {
    cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
                M, N, K, 1.0f, A, K, B, N, 0.0f, C, N);
}
bash
# Link Accelerate (default on macOS)
clang -framework Accelerate -o gemm gemm.c -lcblas

For custom AMX kernels: study community reverse engineering or use Metal Performance Shaders as supported path.

5. Metal Performance Shaders (MPS)
objc
// Objective-C / Swift — GPU compute via MPS
#import <Metal/Metal.h>
#import <MetalPerformanceShaders/MetalPerformanceShaders.h>

id<MTLDevice> device = MTLCreateSystemDefaultDevice();
id<MTLCommandQueue> queue = [device newCommandQueue];

MPSMatrixMultiplication *gemm = [[MPSMatrixMultiplication alloc]
    initWithDevice:device transposeLeft:NO transposeRight:NO
    resultRows:M columns:N interiorColumns:K alpha:1.0 beta:0.0];

Metal provides unified memory path to GPU — no explicit copy for buffers allocated with MTLResourceStorageModeShared.

6. Instruments profiling
bash
# Command-line Instruments (xctrace)
xctrace record --template 'Time Profiler' --launch -- /path/to/app
xctrace record --template 'Allocations' --launch -- /path/to/app
xctrace record --template 'Leaks' --launch -- /path/to/app
xctrace export --input trace.trace --toc
TemplateUse
Time ProfilerCPU hotspots, P/E core usage
AllocationsHeap growth, allocation call trees
LeaksRetained memory
System TraceThread scheduling, syscalls

GUI: Xcode → Product → Profile (⌘I)

7. Command-line debugging tools
bash
# Process memory map
vmmap <pid>

# Heap analysis
heap <pid>
heap <pid> -addresses all  # all allocations

# Leak detection
leaks <pid>
leaks --list <pid>

# Sample call stacks
sample <pid> 5 -file sample.txt
Show full SKILL.md (172 more words)Show less
8. Rosetta 2 translation
bash
# Check if process runs under Rosetta
sysctl sysctl.proc_translated   # 1 = translated x86

# Force arch
arch -arm64 ./native_binary
arch -x86_64 ./x86_binary

# Universal binary info
lipo -info myapp
file myapp
Runs native ARM64Runs under Rosetta
ARM64 buildx86_64-only binary
-arch arm64 compileDownloaded Intel-only app

Rosetta 2: translates x86_64 to ARM64 with JIT cache. AVX/AVX2 translated but may be slower. Not for kernel extensions or VM guests.

9. Memory tagging (ARM MTE)

Future Apple hardware may expose MTE — monitor via:

bash
sysctl hw.optional.arm.FEAT_MTE  # when available

Prepare with pointer authentication already on ARM64e Apple platforms.

10. Build and perf tips
bash
# Native optimized build
clang -arch arm64 -O3 -mcpu=apple-m1 -o app app.c
# Use -mcpu matching target: apple-m1, apple-m2, apple-m3, apple-m4

# P/E core awareness — dispatch heavy work to performance cores
# pthread_set_qos_class_self_np(QOS_CLASS_USER_INITIATED, 0);

Common Problems

SymptomCauseFix
mmap fails with EINVAL4KB alignment on 16KB systemAlign to sysconf(_SC_PAGESIZE)
Slow x86 binaryRosetta overheadShip universal or arm64-only build
Metal buffer nilSimulator vs deviceTest GPU on real hardware
Accelerate wrong resultsRow/column major mismatchCheck BLAS leading dimensions
Instruments empty traceSandbox/permissionsRun from Xcode or sign app
sysctl not foundWrong key name`sysctl -a
  • skills/low-level-programming/assembly-arm — Darwin ABI, AArch64
  • skills/platform/arm-sve — SVE2 on M4+
  • skills/gpu/cuda — NVIDIA not on Apple Silicon; use Metal instead
  • skills/profilers/heaptrack — cross-platform heap profiling concepts
  • skills/compilers/clang — Apple Clang flags
  • skills/low-level-programming/cpu-cache-opt — cache optimization on unified memory

© mohitmishra786, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/platform/apple-silicon of mohitmishra786/low-level-dev-skills.

Open the folder on GitHubat commit bdc5847

Compare with similar skills

Apple Silicon next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Apple Silicon compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Apple Silicon this skillmohitmishra786/low-level-dev-skills252—~1.6kAutomated safety check: PassMIT
Game DeveloperJeffallan/claude-skills12k—~1.5kAutomated safety check: PassMIT
Unity Developeraiskillstore/marketplace4337 repos~2.7kAutomated safety check: PassNone
Sokol Netelix22/Sokol.NET154—~2.8kAutomated safety check: PassMIT
Pixijs Performancepixijs/pixijs-skills351—~4.6kAutomated safety check: PassMIT
Animationskingstinct/react-native-healthkit716—~937Automated safety check: PassMIT

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Questions about Apple Silicon

What does Apple Silicon do?

Apple Silicon skill for M-series development and profiling. An agent skill from mohitmishra786/low-level-dev-skills. Apple Silicon is an agent skill from mohitmishra786/low-level-dev-skills. Apple Silicon skill for M-series development and profiling.

When should I use Apple Silicon?

Apple Silicon fits situations like: leveraging unified memory; metal Performance Shaders; instruments profiling; sysctl hardware queries.

How do I install Apple Silicon in Claude Code?

Run `npx skills add mohitmishra786/low-level-dev-skills --skill apple-silicon -a claude-code`. Or copy the skill folder (skills/platform/apple-silicon in mohitmishra786/low-level-dev-skills) into .claude/skills/apple-silicon in your project. Claude Code loads it when a task matches its description.

How do I install Apple Silicon in Codex?

Run `npx skills add mohitmishra786/low-level-dev-skills --skill apple-silicon -a codex`. Or copy the skill folder (skills/platform/apple-silicon in mohitmishra786/low-level-dev-skills) into .agents/skills/apple-silicon in your project. Codex loads it when a task matches its description.

Can I use Apple Silicon in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mohitmishra786/low-level-dev-skills --skill apple-silicon -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/apple-silicon, .gemini/skills/apple-silicon, .github/skills/apple-silicon and .opencode/skills/apple-silicon in your project.

What does Apple Silicon need to run?

SKILL.md names no scripts, command-line tools or credentials: Apple Silicon is instructions for the agent only.

Does Apple Silicon access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Apple Silicon safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Apple Silicon use?

Apple Silicon is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Apple Silicon use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Apple Silicon?

Skills that share tags, products or a category with Apple Silicon: Game Developer (Jeffallan/claude-skills, 12k stars), Unity Developer (aiskillstore/marketplace, 433 stars), Sokol Net (elix22/Sokol.NET, 154 stars) and Pixijs Performance (pixijs/pixijs-skills, 351 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Apple Silicon?

mohitmishra786 (a GitHub user) maintains it in mohitmishra786/low-level-dev-skills, which has 252 GitHub stars. The repository holds 138 skills in this directory. The repository was last updated on June 27, 2026.

Source: mohitmishra786/low-level-dev-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.