「lua」の検索結果

Expert-level chemical process design covering process flow diagrams, mass and energy balances, equipment sizing, economic evaluation, and sustainable process design.

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。

Address GitHub PR review comments at line, file, and PR level by fetching, evaluating, fixing, committing, and resolving feedback. Use when invoked as `$pr-resolve-review-feedback` or when the user asks to resolve PR feedback.

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。

Address GitHub PR review feedback at line, file, and PR level — fetch, evaluate, fix, commit, and resolve. Use when given a PR URL, a review-comment URL, or a PR-level comment URL. Triggers: GitHub discussion URLs, "fix this review comment", "address PR feedback", "resolve PR comments", PR URLs with #discussion_, #issuecomment-, or #pullrequestreview- fragments.

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。

Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.

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。

Test coverage quality analysis — behavioral coverage, critical gap identification, test resilience evaluation.

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。

[Workflow] Use when activating the Research & Synthesis workflow — research a topic via web sources, then synthesize per --output={synthesis|business-eval|marketing|course} (knowledge report, business evaluation, marketing strategy, or course material).

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。

[Utilities] Use when initializing or re-evaluating portable project context: project-config, project-reference docs, CLAUDE.md, AGENTS.md, universal agent rules, hookless setup. Triggers on: project init, init project, missing project-config, missing project docs, missing CLAUDE.md, missing AGENTS.md.

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。

[Code Quality] Use when evaluating review feedback, requesting targeted code-quality review, or verifying completion claims.

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。

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

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。

Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure

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。

Explore open-source GitHub repository source trees via web browsing to analyze and compare feature implementations at the code level. Supports two modes: cross-project comparison and single-project deep dive. Use when evaluating how OSS projects implement a specific feature, choosing architecture patterns, or benchmarking implementation strategies.

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。

Generate architecture and design documents for implemented code changes with risk-based depth selection. Automatically evaluates risk signals, layer spread, and change magnitude to choose documentation level (A/B/C). Use after completing a feature, before merging a PR that touches multiple layers or sensitive areas (auth, infra, migrations, payments), or when an implemented change needs a design record or ADR.

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。

Use when building an MCP server in Python (FastMCP) or Node/TypeScript (MCP SDK) — agent-centric tool design, input schemas, error handling, and the 10-question evaluation harness.

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。

Wing-4 valuation cognition for a CFO / finance-partner. Use when a deal, internal investment, or board ask names DCF, intrinsic value, WACC, terminal value, or 'what's it worth on a 5-year hold'.

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。

Agents work in parallel on the same task; judges evaluate and select the winner.

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。

Harness-first workflow for evaluating and improving terminal UIs through real interaction. Use this whenever the user wants to review a TUI experience, compare before/after behavior, identify UX friction, validate navigation or filtering flows, inspect visual stability, or turn observed interaction problems into concrete follow-up tests and improvement recommendations. Prefer this skill any time a TUI should be exercised through a reproducible PTY/session harness instead of only reading code.

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。

Crie e evolua sistemas de design com design tokens, arquitetura de componentes, diretrizes de acessibilidade e templates de documentação. Garante UI consistente, escalável e acessível em todos os produtos.

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。

Domine estratégias abrangentes de avaliação para aplicações LLM, desde métricas automatizadas até avaliação humana e testes A/B.

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。

Plataforma de observabilidade de IA de código aberto para rastreamento de LLM, avaliação e monitoramento. Use ao debugar aplicações de LLM com rastreamentos detalhados, executar avaliações em datasets ou monitorar sistemas de IA em produção com insights em tempo real.

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。

Plataforma de observabilidade para LLM com rastreamento, avaliação e monitoramento. Use ao depurar aplicações LLM, avaliar saídas de modelos em datasets, monitorar sistemas em produção ou construir pipelines de testes sistemáticos para aplicações de IA.

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。

Testes e benchmarking de agentes LLM incluindo testes comportamentais, avaliação de capacidades, métricas de confiabilidade e monitoramento em produção—onde até os melhores agentes alcançam menos de 50% em benchmarks do mundo real. Use quando: testes de agentes, avaliação de agentes, benchmark de agentes, confiabilidade de agentes, teste de agentes.

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。

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy. Triggers: create a skill, new skill, build a skill, skill creation, write a SKILL.md, evaluate skill, test skill, skill eval, benchmark skill, optimize skill description.

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。

Load this skill when building any web feature, reviewing architecture decisions, or evaluating JavaScript dependencies. Under no circumstances build features that break completely when JavaScript is unavailable or fails. Absolutely always start with semantic HTML, layer CSS enhancements, and add JavaScript as the final, optional layer. Prioritize resilience and universal access over cutting-edge features.

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。

Evaluates potential channel, integration, affiliate, or strategic partners. Use when the user provides partner profiles plus partnership goals and wants fit scoring, partnership type, and outreach priority.

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。