establishing-benchmark-baseline

Automate language performance baseline setup and generate BENCHMARK_BASELINE.md.

Updated Aug 27, 2026

Search Tags:#benchmarking #workflow-orchestration #language-analysis #performance-baseline #are-we-fast-yet #benchmark-harness

Ready to run

npx skills add https://github.com/antonykamp/cc-truffle-performance-plugin --skill establishing-benchmark-baseline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: establishing-benchmark-baseline
Source: https://github.com/antonykamp/cc-truffle-performance-plugin/tree/main/skills/establishing-benchmark-baseline
Command: npx skills add https://github.com/antonykamp/cc-truffle-performance-plugin --skill establishing-benchmark-baseline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Establishing performance baselines for a language implementation is complex and time-consuming; this skill automates discovery of benchmark data, creation of local benchmarks, and generation of a comprehensive baseline report to guide optimization.

Core Features & Use Cases

Analyzes language characteristics to determine type system, execution model, platform, and complexity.
Identifies 1-2 comparable languages for meaningful benchmark comparisons.
Discovers and fetches AreWeFastYet data to guide micro-benchmark selection.
Implements and translates AreWeFastYet benchmarks locally with verification.
Generates BENCHMARK_BASELINE.md containing performance expectations and analysis.
Orchestrates the full workflow from phase analysis to final baseline generation.

Quick Start

Run this skill to generate BENCHMARK_BASELINE.md by applying Phase 1 and then proceed with the workflow.

Frequently Asked Questions about establishing-benchmark-baseline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I establish a performance baseline for a language implementation?▼

To establish a performance baseline for a language implementation, you can automate the discovery of benchmark data, fetch AreWeFastYet data, and generate a comprehensive BENCHMARK_BASELINE.md report to guide optimization.

What is the best way to compare my language's performance against comparable languages?▼

The best way to compare your language's performance is to analyze language characteristics like type system and execution model, identify 1-2 comparable languages, and translate benchmarks locally for meaningful performance comparisons.

Can I use AreWeFastYet data to guide micro-benchmark selection for my project?▼

Yes, you can use AreWeFastYet data to guide micro-benchmark selection by discovering and fetching relevant performance data, which is then implemented and translated locally with verification for your baseline report.

How does benchmark translation and verification work when setting up a performance baseline?▼

Benchmark translation and verification works by fetching AreWeFastYet data, translating the benchmarks to your local language implementation, verifying their execution, and appending the reproducible results into BENCHMARK_BASELINE.md.

Do I need to manually orchestrate the workflow phases when generating a BENCHMARK_BASELINE.md file?▼

No, you do not need to manually orchestrate the workflow phases because the process automates the full workflow from phase-driven analysis and benchmark translation to final baseline generation in BENCHMARK_BASELINE.md.

What language characteristics are analyzed to determine comparables for benchmarking?▼

The language characteristics analyzed to determine comparables for benchmarking include the type system, execution model, platform, and complexity of the language implementation.