Agent skill

Llvm Optimization

by aftermathlabs in aftermathlabs/llvm-msvc

Expertise in LLVM optimization passes, performance tuning, and code transformation techniques.

AGPL-3.0Auto-check passedDevelopment

Install Llvm Optimization

skills CLI
$ npx skills add aftermathlabs/llvm-msvc --skill llvm-optimization -a claude-code

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

GitHub CLI
$ gh skill install aftermathlabs/llvm-msvc llvm-optimization --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/aftermathlabs/llvm-msvc.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/llvm-optimization .claude/skills/llvm-optimization && 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
llvm-optimization
GitHub stars
438
Token cost
~2.5k tokens
SKILL.md length
264 words
Files
1
Skills in repo
8
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Expertise in LLVM optimization passes, performance tuning, and code transformation techniques.

  • Implementing custom optimizations
  • SKILL.md covers Optimization Pipeline Overview, Core Optimization Passes, Writing Custom Optimization… and Instruction Patterns, plus 6 more sections
  • Reaches raw.githubusercontent.com
  • Analyzing pass behavior

What it does

Llvm Optimization is an agent skill from aftermathlabs/llvm-msvc. Expertise in LLVM optimization passes, performance tuning, and code transformation techniques. Use this skill when implementing custom optimizations, analyzing pass behavior, improving generated code quality, or understanding LLVM's optimization pipeline.

Its SKILL.md is about 2.5k 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 Development, covering Code quality. The repository describes itself as: LLVM fork with explicit compatibility with MSVC 2022 features. The licence is AGPL-3.0.

When your agent uses it

  • Implementing custom optimizations
  • Analyzing pass behavior
  • Improving generated code quality
  • Understanding LLVMs optimization pipeline

Example prompts

  • “/llvm-optimization”

What it can do on your machine

Read from SKILL.md and the folder at commit bfc7254. 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 cpp and bash).

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • raw.githubusercontent.com

    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

Llvm Optimization loads about 2.5k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 264 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

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 aftermathlabs/llvm-msvc at commit bfc7254, republished under its AGPL-3.0 licence (© aftermathlabs). 264 words, ~2,460 tokens.

Download SKILL.mdSave it as .claude/skills/llvm-optimization/SKILL.md (or your agent's skills folder).
name
llvm-optimization
description
Expertise in LLVM optimization passes, performance tuning, and code transformation techniques. Use this skill when implementing custom optimizations, analyzing pass behavior, improving generated code quality, or understanding LLVM's optimization pipeline.

LLVM Optimization Skill

This skill covers LLVM optimization infrastructure, pass development, and performance tuning techniques.

Optimization Pipeline Overview

Pipeline Stages
Source → Frontend → LLVM IR → Optimization Passes → CodeGen → Machine Code
                        ↓
                 [Transform Passes]
                 [Analysis Passes]
Optimization Levels
bash
# No optimization
clang -O0 source.c

# Basic optimization (most optimizations enabled)
clang -O1 source.c

# Full optimization (aggressive inlining, vectorization)
clang -O2 source.c

# Maximum optimization (may increase code size)
clang -O3 source.c

# Size optimization
clang -Os source.c  # Optimize for size
clang -Oz source.c  # Aggressive size optimization

Core Optimization Passes

Scalar Optimizations
  • Constant Propagation: Replace variables with known constant values
  • Dead Code Elimination (DCE): Remove unreachable or unused code
  • Common Subexpression Elimination (CSE): Avoid redundant computations
  • Instruction Combining: Merge multiple instructions into simpler forms
  • Scalar Replacement of Aggregates (SROA): Break up aggregate allocations
Loop Optimizations
  • Loop Invariant Code Motion (LICM): Hoist invariant computations
  • Loop Unrolling: Duplicate loop body to reduce overhead
  • Loop Vectorization: Convert scalar loops to vector operations
  • Loop Fusion/Fission: Combine or split loops
  • Induction Variable Simplification: Optimize loop counters
Interprocedural Optimizations
  • Inlining: Replace call sites with function body
  • Dead Argument Elimination: Remove unused function parameters
  • Interprocedural Constant Propagation: Propagate constants across functions
  • Link-Time Optimization (LTO): Whole-program optimization

Writing Custom Optimization Passes

New Pass Manager (LLVM 13+)
cpp
#include "llvm/IR/PassManager.h"
#include "llvm/Passes/PassBuilder.h"
#include "llvm/Passes/PassPlugin.h"

struct MyOptimizationPass : public llvm::PassInfoMixin<MyOptimizationPass> {
    llvm::PreservedAnalyses run(llvm::Function &F,
                                 llvm::FunctionAnalysisManager &FAM) {
        bool Changed = false;
        
        for (auto &BB : F) {
            for (auto &I : BB) {
                // Implement optimization logic
                if (optimizeInstruction(I)) {
                    Changed = true;
                }
            }
        }
        
        if (Changed)
            return llvm::PreservedAnalyses::none();
        return llvm::PreservedAnalyses::all();
    }
    
private:
    bool optimizeInstruction(llvm::Instruction &I) {
        // Example: Replace add x, 0 with x
        if (auto *BinOp = llvm::dyn_cast<llvm::BinaryOperator>(&I)) {
            if (BinOp->getOpcode() == llvm::Instruction::Add) {
                if (auto *C = llvm::dyn_cast<llvm::ConstantInt>(BinOp->getOperand(1))) {
                    if (C->isZero()) {
                        I.replaceAllUsesWith(BinOp->getOperand(0));
                        return true;
                    }
                }
            }
        }
        return false;
    }
};

// Plugin registration
extern "C" LLVM_ATTRIBUTE_WEAK ::llvm::PassPluginLibraryInfo
llvmGetPassPluginInfo() {
    return {LLVM_PLUGIN_API_VERSION, "MyOptPass", LLVM_VERSION_STRING,
            [](llvm::PassBuilder &PB) {
                PB.registerPipelineParsingCallback(
                    [](llvm::StringRef Name, llvm::FunctionPassManager &FPM,
                       llvm::ArrayRef<llvm::PassBuilder::PipelineElement>) {
                        if (Name == "my-opt") {
                            FPM.addPass(MyOptimizationPass());
                            return true;
                        }
                        return false;
                    });
            }};
}
Analysis Dependencies
cpp
struct MyAnalysis : public llvm::AnalysisInfoMixin<MyAnalysis> {
    using Result = MyAnalysisResult;
    
    Result run(llvm::Function &F, llvm::FunctionAnalysisManager &FAM) {
        // Compute analysis result
        return Result();
    }
    
    static llvm::AnalysisKey Key;
};

// Using analysis in a pass
llvm::PreservedAnalyses run(llvm::Function &F,
                             llvm::FunctionAnalysisManager &FAM) {
    auto &DT = FAM.getResult<llvm::DominatorTreeAnalysis>(F);
    auto &LI = FAM.getResult<llvm::LoopAnalysis>(F);
    auto &AA = FAM.getResult<llvm::AAManager>(F);
    
    // Use analysis results...
}

Instruction Patterns

Strength Reduction
cpp
// Replace expensive operations with cheaper ones
// x * 2  →  x << 1
// x / 4  →  x >> 2
// x % 8  →  x & 7

bool reduceStrength(llvm::BinaryOperator *BO) {
    if (BO->getOpcode() == llvm::Instruction::Mul) {
        if (auto *C = llvm::dyn_cast<llvm::ConstantInt>(BO->getOperand(1))) {
            if (C->getValue().isPowerOf2()) {
                unsigned Shift = C->getValue().exactLogBase2();
                auto *Shl = llvm::BinaryOperator::CreateShl(
                    BO->getOperand(0),
                    llvm::ConstantInt::get(C->getType(), Shift));
                BO->replaceAllUsesWith(Shl);
                return true;
            }
        }
    }
    return false;
}
Algebraic Simplification
cpp
// x + 0 → x
// x * 1 → x
// x * 0 → 0
// x - x → 0
// x | x → x
// x & 0 → 0

Dominator Tree Usage

Finding Optimization Opportunities
cpp
void optimizeWithDominators(llvm::Function &F,
                             llvm::DominatorTree &DT) {
    // Use dominance for safe code motion
    for (auto &BB : F) {
        for (auto &I : BB) {
            if (auto *Load = llvm::dyn_cast<llvm::LoadInst>(&I)) {
                // Check if we can hoist this load
                if (canHoist(Load, DT)) {
                    hoistInstruction(Load, DT);
                }
            }
        }
    }
}

bool canHoist(llvm::Instruction *I, llvm::DominatorTree &DT) {
    llvm::BasicBlock *DefBB = I->getParent();
    
    // Check all uses are dominated
    for (auto *U : I->users()) {
        if (auto *UI = llvm::dyn_cast<llvm::Instruction>(U)) {
            if (!DT.dominates(DefBB, UI->getParent())) {
                return false;
            }
        }
    }
    return true;
}

Loop Optimization Techniques

Loop Analysis
cpp
void analyzeLoops(llvm::Function &F, llvm::LoopInfo &LI) {
    for (auto *L : LI) {
        // Get loop trip count
        if (auto *TC = L->getTripCount()) {
            llvm::errs() << "Trip count: " << *TC << "\n";
        }
        
        // Check if loop is simple
        if (L->isLoopSimplifyForm()) {
            llvm::BasicBlock *Header = L->getHeader();
            llvm::BasicBlock *Latch = L->getLoopLatch();
            llvm::BasicBlock *Exit = L->getExitBlock();
        }
        
        // Get induction variables
        llvm::PHINode *IV = L->getCanonicalInductionVariable();
    }
}
Loop Unrolling
cpp
// Manually trigger loop unrolling
#pragma unroll 4
for (int i = 0; i < N; i++) {
    // Loop body will be unrolled 4x
}

// LLVM unroll metadata
!llvm.loop.unroll.count = !{i32 4}

Vectorization

Auto-Vectorization Hints
cpp
// Enable vectorization
#pragma clang loop vectorize(enable)
for (int i = 0; i < N; i++) {
    a[i] = b[i] + c[i];
}

// Specify vector width
#pragma clang loop vectorize_width(8)
for (int i = 0; i < N; i++) {
    a[i] = b[i] * c[i];
}
SLP Vectorization

Superword Level Parallelism - vectorize straight-line code:

cpp
// Before SLP
a[0] = b[0] + c[0];
a[1] = b[1] + c[1];
a[2] = b[2] + c[2];
a[3] = b[3] + c[3];

// After SLP (conceptual)
<4 x float> tmp = load <4 x float> b
<4 x float> tmp2 = load <4 x float> c
<4 x float> result = fadd tmp, tmp2
store result to a

Debugging Optimizations

Viewing Pass Execution
bash
# Print passes being run
opt -debug-pass-manager input.ll -O2

# Print IR after each pass
opt -print-after-all input.ll -O2

# Print specific pass output
opt -print-after=instcombine input.ll -O2

# Statistics
opt -stats input.ll -O2
Optimization Remarks
bash
# Enable all optimization remarks
clang -Rpass=.* source.c

# Specific remarks
clang -Rpass=loop-vectorize source.c
clang -Rpass-missed=inline source.c
clang -Rpass-analysis=loop-vectorize source.c
Enabling LTO
bash
# Full LTO
clang -flto source1.c source2.c -o program

# Thin LTO (faster, parallel)
clang -flto=thin source1.c source2.c -o program
LTO Benefits
  • Whole-program dead code elimination
  • Interprocedural constant propagation
  • Cross-module inlining
  • Better devirtualization

Correctness Verification

Alive2

Automatic verification of LLVM optimizations:

bash
# Verify transformation correctness
alive-tv before.ll after.ll

# Check specific optimization
opt -instcombine input.ll | alive-tv input.ll -

Resources

See Optimization section in README.md for specific commits and optimization-related projects.

Getting Detailed Information

When you need detailed and up-to-date resource links, tool lists, or project references, fetch the latest data from:

https://raw.githubusercontent.com/gmh5225/awesome-llvm-security/refs/heads/main/README.md

This README contains comprehensive curated lists of:

  • LLVM optimization commits and patches (Optimization section)
  • Alive2 and verification tools
  • Optimization courses and tutorials (CSCD70)

© aftermathlabs, AGPL-3.0. 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 .agents/skills/llvm-optimization of aftermathlabs/llvm-msvc.

Open the folder on GitHubat commit bfc7254

Compare with similar skills

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Categories

Questions about Llvm Optimization

What does Llvm Optimization do?

Expertise in LLVM optimization passes, performance tuning, and code transformation techniques. Llvm Optimization is an agent skill from aftermathlabs/llvm-msvc. Expertise in LLVM optimization passes, performance tuning, and code transformation techniques.

When should I use Llvm Optimization?

Llvm Optimization fits situations like: implementing custom optimizations; analyzing pass behavior; improving generated code quality; understanding LLVMs optimization pipeline.

How do I install Llvm Optimization in Claude Code?

Run `npx skills add aftermathlabs/llvm-msvc --skill llvm-optimization -a claude-code`. Or copy the skill folder (.agents/skills/llvm-optimization in aftermathlabs/llvm-msvc) into .claude/skills/llvm-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Llvm Optimization in Codex?

Run `npx skills add aftermathlabs/llvm-msvc --skill llvm-optimization -a codex`. Or copy the skill folder (.agents/skills/llvm-optimization in aftermathlabs/llvm-msvc) into .agents/skills/llvm-optimization in your project. Codex loads it when a task matches its description.

Can I use Llvm Optimization 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 aftermathlabs/llvm-msvc --skill llvm-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/llvm-optimization, .gemini/skills/llvm-optimization, .github/skills/llvm-optimization and .opencode/skills/llvm-optimization in your project.

What does Llvm Optimization need to run?

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

Does Llvm Optimization access the network?

SKILL.md names 1 domain. In commands or code: raw.githubusercontent.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Llvm Optimization 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 Llvm Optimization use?

Llvm Optimization is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Llvm Optimization use?

About 2.5k tokens (SKILL.md is roughly 9.8k 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 Llvm Optimization?

Skills that share tags, products or a category with Llvm Optimization: WooCommerce Code Review (woocommerce/woocommerce, 11k stars), Systematic Code Refactoring (luongnv89/claude-howto, 42k stars), Install Anti-Slop Oxlint Rules (dmmulroy/anti-slop, 5.2k stars) and Constraint-Driven Development (addyosmani/agent-skills, 102k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Llvm Optimization?

aftermathlabs (a GitHub organization) maintains it in aftermathlabs/llvm-msvc, which has 438 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on October 3, 2026.

Source: aftermathlabs/llvm-msvc on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.