本文へ移動
cccskills
無料GitHub で公開

performance-optimization

Diagnoses and improves measured latency, throughput, memory, CPU, bundle, rendering, query, or resource performance by defining a target, profiling a representative workload, locating the bottleneck, making one change at a time, and comparing before/after evidence. Use for performance work or regressions. Not for guessing optimizations without a reproducible workload or for generic refactoring.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md1.7 KB
  • references/measurement.md338 B

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Performance Optimization

Measure the bottleneck before optimizing it.

Workflow

  1. Define the user or system target: p50/p95/p99 latency, throughput, startup, memory ceiling, frame budget, bundle size, query cost, or error budget.
  2. Build a representative and repeatable workload with realistic data, concurrency, cache state, network, and device assumptions.
  3. Capture a baseline with profiler, trace, query plan, browser performance panel, bundle analyzer, or resource metrics. Record variance and warm/cold state.
  4. Rank bottlenecks by user impact and cost. Form one falsifiable hypothesis.
  5. Make the smallest change, rerun the same workload, and compare effect and regressions. Keep only improvements that meet the target without violating correctness, accessibility, cost, or operability.
  6. Add a regression budget/check where the risk is likely to recur.

Read measurement.md. Do not optimize a benchmark that does not represent the user path, hide work by weakening correctness, or claim improvement from one noisy run.

Completion condition

The bottleneck, baseline, change, before/after result, variance, trade-offs, and remaining limits are documented.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Designs and runs reproducible evaluations for AI agents, prompts, tools, skills, and model-backed workflows using realistic datasets, isolated baselines, objective assertions, rubric grading, trajectory analysis, cost/latency tracking, and regression comparison. Use when measuring agent quality, optimizing skill triggering, comparing prompts or models, or gating an AI feature release. Not for ordinary deterministic unit tests.

日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

Designs or reviews HTTP, REST, GraphQL, RPC, CLI, webhook, event, and service interfaces with explicit inputs, outputs, errors, compatibility, idempotency, pagination, authentication, versioning, and observability. Use when introducing or changing an API or cross-component contract. Not for internal implementation details with no boundary or for debugging one API failure; use root-cause-debugging there.

日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

Reviews an existing codebase for structural friction, unclear ownership, leaky or shallow interfaces, excessive coupling, misplaced state, poor testability, and risky dependency direction, then prioritizes evidence-backed improvement candidates. Use for architecture audits, modularization, modernization, or recurring cross-cutting change pain. Not for designing one new interface, simplifying a local function, or fixing a reproduced bug.

日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

Validates a running application, CLI, API, service, or generated artifact as a user or operator against a prewritten observable behavior contract while remaining source-blind. Use for acceptance checks, runtime proof, anti-fake probes, release smoke tests, or an independent companion to code review. Not for source-quality findings, root-cause diagnosis, or visual design judgment outside the contract.

日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

Closes a completed development branch by checking the final diff and proof, presenting merge, pull-request, keep, or discard options, and cleaning up only after the user or repository workflow chooses a path. Use when feature work is complete and the branch must be integrated or retired. Not for claiming a feature is complete before verification.

日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

Tests a real web user flow with a browser by asserting semantic behavior, network and loading states, keyboard access, responsive layouts, and stable visual evidence. Use for browser bugs, end-to-end UI behavior, responsive or accessibility checks, and screenshot baselines. Not for static source review without a browser or for backend-only tests.

日本語の概要は準備中です。原文の説明を表示しています。

thiientv/godmode962026年8月26日 更新

thiientv のスキルをすべて見る

このスキルの問題を報告する