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adding-benchmarks

Automates the integration of performance benchmarks into the CI/CD pipeline.

Install

mkdir -p .claude/skills/adding-benchmarks && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/5688" && unzip -o skill.zip -d .claude/skills/adding-benchmarks && rm skill.zip

Installs to .claude/skills/adding-benchmarks

Activation

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Add new benchmarks to the CI pipeline. Guides through creating benchmark JSON files, integrating with bootstrap.sh, and ensuring proper CI upload via ci3.yml workflow.
167 charsno explicit “when” trigger
Beginner

Key capabilities

  • Define benchmark metric JSON schemas
  • Configure bootstrap.sh merge behavior
  • Map package hierarchies to dashboard paths
  • Automate performance regression tracking
  • Structure metric groups with forward-slash nesting

How it works

Processes benchmark JSON files through a central merge utility that normalizes names based on file system paths for display grouping.

Inputs & outputs

You give it
Benchmark JSON files and directory path
You get back
Aggregated CI metrics uploaded to dashboard

When to use adding-benchmarks

  • Adding performance metrics to a package
  • Creating new benchmark tests
  • Integrating benchmarks into CI
  • Configuring CI benchmark uploads

About this skill

Adding Benchmarks

When to Use

Use this skill when:

  • Adding new performance benchmarks to a package
  • Creating benchmark tests that should be tracked over time
  • Integrating existing benchmarks into the CI pipeline

Benchmark System Overview

Benchmarks flow through the system as follows:

  1. Generation: Each package produces bench-out/*.bench.json files
  2. Aggregation: bench_merge in root bootstrap.sh combines all files, prefixing names with the package path
  3. Upload: CI caches the merged JSON and GitHub Action uploads to the benchmark dashboard
  4. Display: Results appear at the dashboard with historical tracking

Live dashboard: https://aztecprotocol.github.io/benchmark-page-data/bench/?branch=next

How Benchmark Names Work

Name Construction

The final benchmark name combines two parts:

  1. Package prefix (added automatically by bench_merge): Based on where the file lives
  2. Local name (what you write in JSON): Your metric identifier

Dashboard Grouping

The dashboard splits names by / to create a collapsible tree. The last segment becomes the chart name, everything before it becomes the group hierarchy.

Full NameGroup PathChart Name
yarn-project/stdlib/Tx/private/getTxHash/avgyarn-project/stdlib/Tx/private/getTxHashavg
yarn-project/kv-store/Map/Individual insertionyarn-project/kv-store/MapIndividual insertion
barretenberg/sol/Add2HonkVerifierbarretenberg/solAdd2HonkVerifier

Naming Best Practices

Use / to create logical groupings:

[
  {"name": "Tx/private/getTxHash/avg", "value": 1.2, "unit": "ms"},
  {"name": "Tx/private/getTxHash/p50", "value": 1.1, "unit": "ms"},
  {"name": "Tx/public/getTxHash/avg", "value": 2.3, "unit": "ms"}
]

Avoid flat names - they create no hierarchy and are hard to navigate:

[
  {"name": "tx_private_gettxhash_avg", "value": 1.2, "unit": "ms"}
]

Common suffixes:

  • Timing: avg, p50, p95, p99, min, max, total
  • Size: _opcodes, _gates, memory
  • Rate: gasPerSecond, jobs_per_sec

Required JSON Format

All benchmark files must be arrays using the customSmallerIsBetter format:

[
  {"name": "category/metric_name", "value": 12345, "unit": "gas"},
  {"name": "category/another_metric", "value": 100.5, "unit": "ms"}
]

Rules:

  • Must be a JSON array [...], not an object
  • Each entry needs name, value, unit
  • value must be numeric (lower is better)
  • File must end with .bench.json

Optional fields (preserved by benchmark-action):

  • range (string): Variance info (e.g., "± 5%")
  • extra (string): Metadata — used for stacked chart grouping (see below)

Stacked Charts

To render multiple metrics as a single stacked area chart (e.g., component breakdowns), add an extra field with a stacked:GROUP_NAME value. Entries sharing the same GROUP_NAME are overlaid on one chart.

[
  {"name": "proving/cpus-8/total_ms", "value": 31663, "unit": "ms"},
  {"name": "proving/cpus-8/oink_prove_ms", "value": 4992, "unit": "ms", "extra": "stacked:proving/cpus-8/components"},
  {"name": "proving/cpus-8/sumcheck_ms", "value": 3318, "unit": "ms", "extra": "stacked:proving/cpus-8/components"},
  {"name": "proving/cpus-8/circuit_ms", "value": 4642, "unit": "ms", "extra": "stacked:proving/cpus-8/components"}
]

How it works:

  • extra: "stacked:GROUP_NAME" → entries with the same GROUP_NAME are rendered as one stacked chart
  • No extra field → individual line chart (default behavior)
  • Stacked entries still appear as individual charts on the main benchmark-action dashboard; the stacked view is rendered by a custom dashboard page
  • The GROUP_NAME becomes the chart title (after bench_merge prefixing, same as name)
  • The extra field is one of the 5 fields preserved by the benchmark-action Zod schema (name, value, unit, range, extra); any other custom fields will be stripped

When to use stacked charts:

  • Component-level timing breakdowns (e.g., sumcheck, PCS, circuit construction)
  • Resource allocation views (e.g., memory by subsystem)
  • Any case where you want to see how a total decomposes into parts over time

Adding a New Benchmark

Step 1: Create the Benchmark

TypeScript (most common):

// my_bench.test.ts
import { Timer } from '@aztec/foundation/timer';
import { writeFile, mkdir } from 'fs/promises';

describe('MyComponent benchmarks', () => {
  const results: { name: string; value: number; unit: string }[] = [];

  afterAll(async () => {
    if (process.env.BENCH_OUTPUT) {
      await mkdir(path.dirname(process.env.BENCH_OUTPUT), { recursive: true });
      await writeFile(process.env.BENCH_OUTPUT, JSON.stringify(results));
    }
  });

  it('benchmark operation', async () => {
    const timer = new Timer();
    // ... operation to benchmark ...
    results.push({ name: 'MyComponent/operation/avg', value: timer.ms(), unit: 'ms' });
  });
});

Shell (jq-based):

mkdir -p bench-out
jq -n '[
  {name: "metric1", value: '$VALUE1', unit: "ms"},
  {name: "metric2", value: '$VALUE2', unit: "gas"}
]' > bench-out/my-component.bench.json

Python:

import json
benchmark_list = [{"name": "category/metric", "value": 12345, "unit": "gas"}]
with open("bench-out/my-component.bench.json", "w") as f:
    json.dump(benchmark_list, f)

Step 2: Register in bootstrap.sh

Add to the package's bench_cmds function:

function bench_cmds {
  local hash=$(hash)
  echo "$hash BENCH_OUTPUT=bench-out/my_component.bench.json yarn-project/scripts/run_test.sh <package>/src/my_bench.test.ts"
}

Options: :ISOLATE=1, :CPUS=8, :MEM=16g, :TIMEOUT=7200

CPUS Suggestion: For long running or compute-heavy benchmarks allocate CPUs (:CPUS=N). Benchmarks have strict scheduling, so if you request X CPUs, you'll have them available for consistent results.

ISOLATE Suggestion: Use :ISOLATE=1 when your benchmark needs a clean, isolated environment with no network access and pinned resources. This runs the test in a Docker container, ensuring reproducible results without interference from other processes.

MEM Suggestion: Use :MEM=Xg (e.g., :MEM=16g) for memory-intensive benchmarks that may exceed the default allocation (CPUS × 4GB). Pair with :ISOLATE=1 since memory limits are enforced via Docker.

TIMEOUT Suggestion: Use :TIMEOUT=N (in seconds) for benchmarks that take longer than the default timeout. For example, :TIMEOUT=1800 for 30 minutes, :TIMEOUT=7200 for 2 hours.

Important naming gotcha: Benchmark test files must use .bench.test.ts (with a dot before bench), NOT _bench.test.ts. The test discovery pattern [[ "$test" =~ \.bench\.test\.ts$ ]] specifically looks for .bench.test.ts.

Step 3: Verify

# Run locally
BENCH_OUTPUT=bench-out/test.bench.json yarn test src/my_bench.test.ts

# Validate JSON
jq . bench-out/test.bench.json
jq 'all(has("name") and has("value") and has("unit"))' bench-out/test.bench.json

CI Details

Benchmarks upload when:

  • PR has label: ci-merge-queue, ci-full, or ci-full-no-test-cache (publishes to target branch, i.e. next or a merge-train branch)
  • Running on merge queue (publishes with next)

10-commit visibility window: The dashboard only shows benchmarks that ran in the last 10 commits. If a benchmark stops running, it disappears after ~10 merges.

Reference Implementations

  • TypeScript: yarn-project/stdlib/src/tx/tx_bench.test.ts
  • Python: l1-contracts/scripts/generate_benchmark_json.py
  • Shell: yarn-project/p2p/testbench/consolidate_benchmarks.sh
  • Circuits: noir-projects/fnd/noir-protocol-circuits/scripts/run_bench.sh

When not to use it

  • One-off performance tests
  • Tests requiring complex runtime state not serializable to JSON

Prerequisites

Access to CI repositoryBenchmark dashboard environment

Limitations

  • Naming conflicts occur if path hierarchy is not strictly maintained
  • Historical tracking relies on consistent ID naming
  • Requires manual cleanup of stale benchmark entries

How it compares

It replaces flat, non-navigable logs with a structured, hierarchical dashboard display derived automatically from project directory organization.

Compared to similar skills

adding-benchmarks side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
adding-benchmarks (this skill)16moReviewBeginner
perf-lighthouse135moReviewIntermediate
moai-workflow-testing12moReviewIntermediate
wrk-ci01moReviewBeginner

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