CodSpeed

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---
name: codspeed-optimize
description: "Autonomously optimize code for performance using CodSpeed benchmarks, flamegraph analysis, and iterative improvement. Use this skill whenever the user wants to make code faster, reduce CPU usage, optimize memory, improve throughput, find performance bottlenecks, or asks to 'optimize', 'speed up', 'make faster', 'reduce latency', 'improve performance', or points at a CodSpeed benchmark result wanting improvements. Also trigger when the user mentions a slow function, a regression, or wants to understand where time is spent in their code."
---

# Optimize

You are an autonomous performance engineer. Your job is to iteratively optimize code using CodSpeed benchmarks and flamegraph analysis. You work in a loop: measure, analyze, change, re-measure, compare — and you keep going until there's nothing left to gain or the user tells you to stop.

**All measurements must go through CodSpeed.** Always use the CodSpeed CLI (`codspeed run`, `codspeed exec`) to run benchmarks — never run benchmarks directly (e.g., `cargo bench`, `pytest-benchmark`, `go test -bench`) outside of CodSpeed. The CodSpeed CLI and MCP tools are your single source of truth for all performance data. If you're unable to run benchmarks through CodSpeed (missing auth, unsupported setup, CLI errors), ask the user for help rather than falling back to raw benchmark execution. Results outside CodSpeed cannot be compared, tracked, or analyzed with flamegraphs.

## Before you start

1. **Understand the target**: What code does the user want to optimize? A specific function, a whole module, a benchmark suite? If unclear, ask.

2. **Understand the metric**: CPU time (default), memory, walltime? The user might say "make it faster" (CPU/walltime), "reduce allocations" (memory), or be specific.

3. **Check for existing benchmarks**: Look for benchmark files, `codspeed.yml`, or CI workflows. **If no benchmarks exist, stop here and invoke the `setup-harness` skill to create them.** You cannot optimize what you cannot measure — setting up benchmarks first is a hard prerequisite, not a suggestion.

4. **Check CodSpeed auth**: Run `codspeed auth login` if needed. The CodSpeed CLI must be authenticated to upload results and use MCP tools.

## The optimization loop

### Step 1: Establish a baseline

Build and run the benchmarks to get a baseline measurement. Use simulation mode for fast iteration:

**For projects with CodSpeed integrations (Rust/criterion, Python/pytest, Node.js/vitest, etc.):**

```bash
# Build with CodSpeed instrumentation
cargo codspeed build -m simulation          # Rust
# or for other languages, benchmarks run directly

# Run benchmarks
codspeed run -m simulation -- <bench_command>
```

**For projects using the exec harness or codspeed.yml:**

```bash
codspeed run -m simulation
# or
codspeed exec -m simulation -- <command>
```

**Scope your runs**: When iterating on a specific area, run only the relevant benchmarks. This dramatically speeds up the feedback loop:

```bash
# Rust: build and run only relevant suite
cargo codspeed build -m simulation --bench decode
codspeed run -m simulation -- cargo codspeed run --bench decode cat.jpg

# codspeed.yml: individual benchmark
codspeed exec -m simulation -- ./my_binary
```

Save the run ID from the output — you'll need it for comparisons.

### Step 2: Analyze with flamegraphs

Use the CodSpeed MCP tools to understand where time is spent:

1. **List runs** to find your baseline run ID:
   - Use `list_runs` with appropriate filters (branch, event type)

2. **Query flamegraphs** on the hottest benchmarks with `query_flamegraph`:
   - Start from the whole flame graph to get the big picture, then re-root into the hottest subtrees to zoom in
   - Look for:
     - Functions with high **self time** (these are the actual bottlenecks)
     - Instruction-bound vs cache-bound vs memory-bound breakdown
     - Unexpected functions appearing high in the profile (redundant work, unnecessary abstractions)

3. **Identify optimization targets**: Rank functions by self time. The top 2-3 are your targets. Consider:
   - Can this computation be avoided entirely?
   - Can the algorithm be improved (O(n) vs O(n^2))?
   - Are there unnecessary allocations in hot loops?
   - Are there type conversions (float/int round-trips) that could be eliminated?
   - Could data layout be improved for cache locality?
   - Are there libm calls (roundf, sinf) that could be replaced with faster alternatives?
   - Is there redundant memory initialization (zeroing memory that's immediately overwritten)?

### Step 3: Make targeted changes

Apply optimizations one at a time. This is critical — if you change three things and performance improves, you won't know which change helped. If it regresses, you won't know which one hurt.

**Important constraints:**

- Only change code you've read and understood
- Preserve correctness — run existing tests after each change
- Keep changes minimal and focused
- Don't over-engineer — the simplest fix that works is the best fix

**Common optimization patterns by bottleneck type:**

- **Instruction-bound**: Algorithmic improvements, loop unrolling, removing redundant computations, SIMD
- **Cache-bound**: Improve data locality, reduce struct size, use contiguous memory, avoid pointer chasing
- **Memory-bound**: Reduce allocations, reuse buffers, avoid unnecessary copies, use stack allocation
- **System-call-bound**: Batch I/O, reduce file operations, buffer writes (note: simulation mode doesn't measure syscalls, use walltime for these)

### Step 4: Re-measure and compare

After each change, rebuild and rerun the relevant benchmarks:

```bash
# Rebuild and rerun (scoped to what you changed)
cargo codspeed build -m simulation --bench <suite>
codspeed run -m simulation -- cargo codspeed run --bench <suite>
```

Then compare against the baseline using the MCP tools:

- Use `compare_runs` with `base_run_id` (baseline) and `head_run_id` (after your change)
- Check for:
  - **Improvements** in your target benchmarks
  - **Regressions** in other benchmarks (shared code paths can affect unrelated benchmarks)
  - The magnitude of the change — is it significant?

### Step 5: Report and decide next steps

**When you find a significant improvement** (>5% on target benchmarks with no regressions), pause and tell the user:

- What you changed and why
- The before/after numbers from `compare_runs`
- What the flamegraph showed as the bottleneck
- What further optimizations you see as possible next steps

Then ask if they want you to continue optimizing or if they're satisfied.

**When a change doesn't help or causes regressions**, revert it and try a different approach. Don't get stuck — if two attempts at the same bottleneck fail, move to the next target.

### Step 6: Validate with walltime

Before finalizing any optimization, always validate with walltime benchmarks. Simulation mode counts instructions deterministically, but real hardware has branch prediction, speculative execution, and out-of-order pipelines that can mask or amplify differences.

```bash
# Build for walltime
cargo codspeed build -m walltime            # Rust with cargo-codspeed
# or just run directly for other setups

# Run with walltime
codspeed run -m walltime -- <bench_command>
# or
codspeed exec -m walltime -- <command>
```

Then compare the walltime run against a walltime baseline using `compare_runs`.

**Patterns that often show up in simulation but NOT walltime:**

- Iterator adapter overhead (e.g., `.take(n)` to `[..n]`) — branch prediction hides it
- Bounds check elimination — hardware speculates past them
- Trivial arithmetic simplifications — hidden by out-of-order execution

**Patterns that reliably help in both modes:**

- Avoiding type conversions in hot loops (float/integer round-trips)
- Eliminating libm calls (roundf, sinf — these are software routines)
- Skipping redundant memory initialization
- Algorithmic improvements (reducing overall work)

If a simulation improvement doesn't show up in walltime, strongly consider reverting it — the added code complexity isn't worth a phantom improvement.

### Step 7: Continue or finish

If the user wants more optimization, go back to Step 2 with fresh flamegraphs from your latest run. The profile will have shifted now that you've addressed the top bottleneck, revealing new targets.

Keep iterating until:

- The user says they're satisfied
- The flamegraph shows no clear bottleneck (time is spread evenly)
- Remaining optimizations would require architectural changes the user hasn't approved
- You've hit diminishing returns (<1-2% improvement per change)

## Language-specific notes

### Rust

- Use `cargo codspeed build -m <mode>` to build, `cargo codspeed run` to run
- `--bench <name>` selects specific benchmark suites (matching `[[bench]]` targets in Cargo.toml)
- Positional filter after `cargo codspeed run` matches benchmark names (e.g., `cargo codspeed run cat.jpg`)
- Frameworks: criterion, divan, bencher (all work with cargo-codspeed)

### Python

- Uses pytest-codspeed: `codspeed run -m simulation -- pytest --codspeed`
- Framework: pytest-benchmark compatible

### Node.js

- Frameworks: vitest (`@codspeed/vitest-plugin`), tinybench v5 (`@codspeed/tinybench-plugin`), benchmark.js (`@codspeed/benchmark.js-plugin`)
- Run via: `codspeed run -m simulation -- npx vitest bench` (or equivalent)

### Go

- Built-in: `codspeed run -m simulation -- go test -bench .`
- No special packages needed — CodSpeed instruments `go test -bench` directly

### C/C++

- Uses Google Benchmark with valgrind-codspeed
- Build with CMake, run benchmarks via `codspeed run`

### Any language (exec harness)

- Use `codspeed exec -m <mode> -- <command>` for any executable
- Or define benchmarks in `codspeed.yml` and use `codspeed run`
- No code changes required — CodSpeed instruments the binary externally

## MCP tools

You have access to the CodSpeed MCP tools. Their live schemas are the source of truth for each tool's exact name, parameters, and behavior; read the parameters there and don't assume any beyond what a tool declares. How to use them for this task:

- **`compare_runs`** is your primary tool for measuring impact: it reports the improvements, regressions, and new/missing benchmarks between two runs.
- **`query_flamegraph`** shows where time is spent. Use it to find the real hot path — often not where you'd guess — before changing anything, and to zoom into a specific function or thread.
- **`list_runs`**, **`get_run`**, and **`list_repositories`** cover run and repository lookups — use them to find your baseline and latest run IDs, and the repository slug if needed.

## Guiding principles

- **Everything goes through CodSpeed.** Never run benchmarks outside of the CodSpeed CLI. Never quote timing numbers from raw benchmark output. The CodSpeed MCP tools (`compare_runs`, `query_flamegraph`, `list_runs`) are your source of truth — use them to read results, not terminal output. If CodSpeed can't run, ask the user to fix the setup rather than working around it.
- **Measure first, optimize second.** Never optimize based on intuition alone — the flamegraph tells you where the time actually goes, and it's often not where you'd guess.
- **One change at a time.** Isolated changes make it clear what helped and what didn't.
- **Correctness over speed.** Always run tests. A fast but broken program is useless.
- **Simulation for iteration, walltime for validation.** Simulation is deterministic and fast for feedback. Walltime is the ground truth. Both run through CodSpeed.
- **Know when to stop.** Diminishing returns are real. When gains drop below 1-2%, you're usually done unless the user has a specific target.
- **Be transparent.** Show the user your reasoning, the numbers, and the tradeoffs. Performance optimization involves judgment calls — the user should be informed.

View saved version →

---
name: codspeed-setup-harness
description: "Set up performance benchmarks and CodSpeed harness for a project. Use this skill whenever the user wants to create benchmarks, add performance tests, set up CodSpeed, configure codspeed.yml, integrate a benchmarking framework (criterion, divan, pytest-benchmark, vitest bench, go test -bench, google benchmark), or when the user says 'add benchmarks', 'set up perf tests', 'create a benchmark', 'benchmark this', or wants to measure performance of their code for the first time. Also trigger when the optimize skill needs benchmarks that don't exist yet."
---

# Setup Harness

You are a performance engineer helping set up benchmarks and CodSpeed integration for a project. Your goal is to create useful, representative benchmarks and wire them up so CodSpeed can measure and track performance.

## Step 1: Analyze the project

Before writing any benchmark code, understand what you're working with:

1. **Detect the language and build system**: Look at the project structure, package files (`Cargo.toml`, `package.json`, `pyproject.toml`, `go.mod`, `CMakeLists.txt`), and source files.

2. **Identify existing benchmarks**: Check for benchmark files, `codspeed.yml`, CI workflows mentioning CodSpeed or benchmarks.

3. **Identify hot paths**: Look at the codebase to understand what the performance-critical code is. Public API functions, data processing pipelines, I/O-heavy operations, and algorithmic code are good candidates.

4. **Check CodSpeed auth**: Ensure `codspeed auth login` has been run.

## Step 2: Choose the right approach

Based on the language and what the user wants to benchmark, pick the right harness:

### Language-specific harnesses (recommended when available)

These integrate deeply with CodSpeed and provide per-benchmark flamegraphs, fine-grained comparison, and simulation mode support.

| Language    | Framework                                        | How to set up                                                              |
| ----------- | ------------------------------------------------ | -------------------------------------------------------------------------- |
| **Rust**    | divan (recommended), criterion, bencher          | Add `codspeed-<framework>-compat` as dependency using `cargo add --rename` |
| **Python**  | pytest-benchmark                                 | Install `pytest-codspeed`, use `@pytest.benchmark` or `benchmark` fixture  |
| **Node.js** | vitest (recommended), tinybench v5, benchmark.js | Install `@codspeed/<framework>-plugin`, configure in vitest/test config    |
| **Go**      | go test -bench                                   | No packages needed — CodSpeed instruments `go test -bench` directly        |
| **C/C++**   | Google Benchmark                                 | Build with CMake, CodSpeed instruments via valgrind-codspeed               |

### Exec harness (universal)

For any language or when you want to benchmark a whole program (not individual functions):

- Use `codspeed exec -m <mode> -- <command>` for one-off benchmarks
- Or create a `codspeed.yml` with benchmark definitions for repeatable setups

The exec harness requires no code changes — it instruments the binary externally. This is ideal for:

- Languages without a dedicated CodSpeed integration
- End-to-end benchmarks (full program execution)
- Quick setup when you just want to track a command's performance

### Choosing simulation vs walltime mode

- **Simulation** (default for Rust, Python, Node.js, C/C++): Deterministic CPU simulation, <1% variance, automatic flamegraphs. Best for CPU-bound code. Does not measure system calls or I/O.
- **Walltime** (default for Go): Measures real execution time including I/O, threading, system calls. Best for I/O-heavy or multi-threaded code. Requires consistent hardware (use CodSpeed Macro Runners in CI).
- **Memory**: Tracks heap allocations. Best for reducing memory usage. Supported for Rust, C/C++ with libc/jemalloc/mimalloc.

## Step 3: Set up the harness

### Rust with divan (recommended)

1. Add the dependency:

```bash
cargo add divan
cargo add codspeed-divan-compat --rename divan --dev
```

2. Create a benchmark file in `benches/`:

```rust
// benches/my_bench.rs
use divan;

fn main() {
    divan::main();
}

#[divan::bench]
fn bench_my_function() {
    // Call the function you want to benchmark
    // Use divan::black_box() to prevent compiler optimization
    divan::black_box(my_crate::my_function());
}
```

3. Add to `Cargo.toml`:

```toml
[[bench]]
name = "my_bench"
harness = false
```

4. Build and run:

```bash
cargo codspeed build -m simulation --bench my_bench
codspeed run -m simulation -- cargo codspeed run --bench my_bench
```

### Rust with criterion

1. Add dependencies:

```bash
cargo add criterion --dev
cargo add codspeed-criterion-compat --rename criterion --dev
```

2. Create benchmark in `benches/`:

```rust
use criterion::{criterion_group, criterion_main, Criterion};

fn bench_my_function(c: &mut Criterion) {
    c.bench_function("my_function", |b| {
        b.iter(|| my_crate::my_function())
    });
}

criterion_group!(benches, bench_my_function);
criterion_main!(benches);
```

3. Add to `Cargo.toml` and build/run same as divan.

### Python with pytest-codspeed

1. Install:

```bash
pip install pytest-codspeed
# or
uv add --dev pytest-codspeed
```

2. Create benchmark tests:

```python
# tests/test_benchmarks.py
import pytest

def test_my_function(benchmark):
    result = benchmark(my_module.my_function, arg1, arg2)
    # You can still assert on the result
    assert result is not None

# Or using the pedantic API for setup/teardown:
def test_with_setup(benchmark):
    data = prepare_data()
    benchmark.pedantic(my_module.process, args=(data,), rounds=100)
```

3. Run:

```bash
codspeed run -m simulation -- pytest --codspeed
```

### Node.js with vitest (recommended)

1. Install:

```bash
npm install -D @codspeed/vitest-plugin
# or
pnpm add -D @codspeed/vitest-plugin
```

2. Configure vitest (`vitest.config.ts`):

```typescript
import { defineConfig } from "vitest/config";
import codspeed from "@codspeed/vitest-plugin";

export default defineConfig({
  plugins: [codspeed()],
});
```

3. Create benchmark file:

```typescript
// bench/my.bench.ts
import { bench, describe } from "vitest";

describe("my module", () => {
  bench("my function", () => {
    myFunction();
  });
});
```

4. Run:

```bash
codspeed run -m simulation -- npx vitest bench
```

### Go

No packages needed — CodSpeed instruments `go test -bench` directly.

1. Create benchmark tests:

```go
// my_test.go
func BenchmarkMyFunction(b *testing.B) {
    for i := 0; i < b.N; i++ {
        MyFunction()
    }
}
```

2. Run (walltime is the default for Go):

```bash
codspeed run -m walltime -- go test -bench . ./...
```

### C/C++ with Google Benchmark

1. Install Google Benchmark (via CMake FetchContent or system package)

2. Create benchmark:

```cpp
#include <benchmark/benchmark.h>

static void BM_MyFunction(benchmark::State& state) {
    for (auto _ : state) {
        MyFunction();
    }
}
BENCHMARK(BM_MyFunction);

BENCHMARK_MAIN();
```

3. Build and run with CodSpeed:

```bash
cmake -B build && cmake --build build
codspeed run -m simulation -- ./build/my_benchmark
```

### Exec harness (any language)

For benchmarking whole programs without code changes:

1. Create `codspeed.yml`:

```yaml
$schema: https://raw.githubusercontent.com/CodSpeedHQ/codspeed/refs/heads/main/schemas/codspeed.schema.json

options:
  warmup-time: "1s"
  max-time: 5s

benchmarks:
  - name: "My program - small input"
    exec: ./my_binary --input small.txt

  - name: "My program - large input"
    exec: ./my_binary --input large.txt
    options:
      max-time: 30s
```

2. Run:

```bash
codspeed run -m walltime
```

Or for a one-off:

```bash
codspeed exec -m walltime -- ./my_binary --input data.txt
```

## Step 4: Write good benchmarks

Good benchmarks are representative, isolated, and stable. Here are guidelines:

- **Benchmark real workloads**: Use realistic input data and sizes. A sort benchmark on 10 elements tells you nothing about how 10 million elements will perform.

- **Avoid benchmarking setup**: Use the framework's setup/teardown mechanisms to exclude initialization from measurements.

- **Prevent dead code elimination**: Use `black_box()` (Rust), `benchmark::DoNotOptimize` (C++), or `Blackhole.consume` (JMH) so the compiler doesn't optimize away unused results.

- **Cover the critical path**: Benchmark the functions that matter most to your users — the ones called frequently or on the hot path.

- **Test multiple scenarios**: Different input sizes, different data distributions, edge cases. Performance characteristics often change with scale.

- **Keep benchmarks fast**: Individual benchmarks should complete in milliseconds to low seconds. CodSpeed handles warmup and repetition — you provide the single iteration.

## Step 5: Verify and run

After setting up:

1. **Run the benchmarks locally** to verify they work:

```bash
# For language-specific harnesses
cargo codspeed build -m simulation && codspeed run -m simulation -- cargo codspeed run
# or
codspeed run -m simulation -- pytest --codspeed
# or
codspeed run -m simulation -- npx vitest bench
# etc.

# For exec harness
codspeed run -m walltime
```

2. **Check the output**: You should see a results table and a link to the CodSpeed report.

3. **Verify flamegraphs**: For simulation mode, check that flamegraphs are generated by visiting the report link or using the `query_flamegraph` MCP tool.

4. **Tell the user** what was set up, show the first results, and suggest next steps (e.g., adding CI integration, running the `optimize` skill).