Does this skill provide code fixes or optimizations?

No, the skill is constrained to only provide hypotheses and evidence. It purposefully avoids making optimization suggestions to maintain focus on accurate diagnosis.

How many hypotheses will it generate at once?

The skill is limited to a maximum of five hypotheses per session to ensure the most relevant and high-confidence issues are prioritized.

What is the primary purpose of the Performance Theory Gatherer?

It is designed to generate evidence-based theories regarding performance bottlenecks by correlating git history with current code structures.

How does it determine which code paths to analyze?

The skill uses repository mapping and grep commands to identify the specific paths involved in a given performance scenario.

Why does the skill require git history access?

Analyzing git history allows the skill to identify recent changes that may have introduced performance regressions, providing a temporal context for the hypotheses.

Performance Hypothesis Gatherer

Name: Performance Hypothesis Gatherer
Author: avifenesh

byavifenesh

•

311

분석 및 모니터링

Generates evidence-based performance hypotheses by analyzing git history and specific execution paths.

소개

The Performance Hypothesis Gatherer is a diagnostic tool for Claude Code designed to identify the root causes of performance regressions. By systematically reviewing recent git history and mapping relevant code paths, the skill produces up to five data-backed hypotheses. Each hypothesis includes specific evidence from the repository and a confidence rating, allowing developers to understand why performance issues are occurring before attempting to optimize. This skill enforces a strict separation between diagnosis and implementation to ensure rigorous debugging.

주요 기능

Evidence-linked hypothesis generation with confidence scoring
311 GitHub stars
Automated git history analysis for performance regression tracking
Targeted code path identification via repo-mapping and grep
Strict diagnostic constraints to prevent premature optimization
Standardized YAML-style output for easy integration into dev workflows

사용 사례

Identifying the specific commit responsible for a recent latency spike
Mapping complex execution paths to find hidden bottlenecks in legacy code
Creating a data-driven debugging roadmap for reported performance issues

소개

주요 기능

Evidence-linked hypothesis generation with confidence scoring
311 GitHub stars
Automated git history analysis for performance regression tracking
Targeted code path identification via repo-mapping and grep
Strict diagnostic constraints to prevent premature optimization
Standardized YAML-style output for easy integration into dev workflows

사용 사례

Identifying the specific commit responsible for a recent latency spike
Mapping complex execution paths to find hidden bottlenecks in legacy code
Creating a data-driven debugging roadmap for reported performance issues