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Performance Benchmarking and Regression Detection

A comprehensive tool for establishing performance baselines across multiple dimensions. It measures Core Web Vitals (LCP, CLS, INP), API latency (p50, p95, p99), and development build efficiency. Essential for detecting performance regressions before merging code, ensuring stability in CI/CD pipelines, and comparing alternative stack implementations.

Benchmark Methodology For Competitive Analysis

benchmark-methodology

This methodology provides a rigorous framework for scoring competitors across nine weighted dimensions. By using explicit 1-5 rubrics and focusing intensely on the client's strategic tension, it moves beyond subjective gut-feel rankings to create defensible, comparable, and evidence-anchored scores. It is essential for developing a detailed, structured competitive report.

Systematic Performance Optimization Loop

benchmark-optimization-loop

A rigorous, data-driven methodology for improving system performance. Use this skill when aiming to significantly increase speed, reduce latency, or lower operational cost. It establishes a measurable baseline, generates testable variants based on hypotheses, runs controlled benchmarks, and validates the best performing solution through iterative testing and code promotion gates.

Competitive Benchmarking Scope Definition

competitive-platform-analysis

This skill guides the definition of a defensible and decision-relevant competitor set. It is the foundational step before any benchmarking, helping users identify, categorize, and scope competitors (Direct, Adjacent, Aspirational) based on criteria like size, niche, market reach, and positioning. It ensures that strategic analysis focuses only on the most relevant rivals.

Cost Benchmark for LLM Performance

This skill runs comprehensive benchmarks against structural and adversarial corpora. It measures the performance and cost efficiency of a booster model compared to leading LLM baselines (Gemini, Anthropic). Results are persisted in verifiable JSON reports, crucial for auditing claims and verifying booster win rates before publishing.

Analyzing Benchmark Performance Drift

This skill analyzes historical benchmark run data stored in JSON files. It surfaces key performance drift metrics over time, including win rate, average latency, p99 latency, and LLM-baseline cost. It is crucial for detecting subtle performance degradations or regressions that standard smoke tests might miss, ensuring stability across releases or corpus expansions.

Compare GAIA Benchmark Architectures

gaia-architecture-comparison

A comprehensive skill for comparing the GAIA benchmark harness across various implementations, such as ruflo and Princeton HAL. It analyzes capability gaps (e.g., real code execution, full question sets, browser automation) and provides a detailed improvement roadmap for enhancing AI evaluation benchmarks.

GAIA Benchmark Failure Diagnosis Tool

A comprehensive skill designed to systematically diagnose why an AI agent fails a specific task within the GAIA benchmark. It guides the user through loading the question trace, classifying the failure mode (e.g., Reasoning Miss, Tool Gap), and proposing targeted fixes. Use this tool when you need to root-cause a performance regression or an incorrect answer to resubmit a corrected agent.

GAIA Benchmark Submission Workflow

gaia-submission

Guides the user through the complete, multi-phase GAIA benchmark process. This workflow covers environment validation, cost estimation, running the benchmark, and generating a highly integrity-checked, HAL-compatible submission package required for leaderboards. It ensures both transport-integrity and earning-integrity.

Go Testing Patterns and Best Practices

A comprehensive guide to writing reliable and maintainable tests in Go. This skill covers the TDD methodology (Red-Green-Refactor cycle), advanced patterns like table-driven tests, subtests, benchmarks, and fuzzing. Learn how to ensure high code quality and test coverage in idiomatic Go projects.

Manage Benchmarking Suites For Evolution

A utility for scaffolding and verifying fixed evaluation corpora (bench suites). It generates deterministic test suites from existing repositories, decoupling the evaluation process from the repo's natural test suite. This ensures consistent fitness scoring when running evolution pipelines across different commits or forks.

AI Security Detection Benchmarking

harness-security-bench

This tool executes the advanced `metaharness/darwin` security benchmark (ADR-155). It rigorously evaluates the security detection capabilities of AI systems against a controlled ground-truth corpus, providing critical metrics like TPR, FPR, and patch-pass rates. It compares the champion model against multiple baselines, offering empirical data crucial for iterative security improvement and drift detection.

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