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Use when the user has a RAG (Retrieval-Augmented Generation) system and wants to evaluate its quality — separating retrieval issues from generation issues. Also use when the user mentions RAG evaluation, faithfulness checking, hallucination detection in RAG, retrieval quality, chunking optimization, or "is my RAG pipeline working." Outputs a diagnostic matrix that pinpoints whether problems are in retrieval or generation.

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Design a critical thinking task targeting specific skills like evaluating evidence, identifying bias, or analysing arguments. Use when embedding critical analysis into subject lessons.

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。

Evaluate a teacher-drafted central historical question for its capacity to drive genuine historical inquiry. Use when assessing whether a question will generate real evidence-weighing or produce shallow responses.

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。

Risk-based vendor assessment framework for IT service providers, technology vendors, and third-party partners under DORA, NIS2, GDPR. Provides three-phase process (Initial Screening / Detailed Assessment / Final Evaluation), six-dimension risk scoring (Financial/Operational/Compliance/Security/Reputational/Strategic) with weighted matrices, full DORA Art. 28-30 contractual checklist, NIS2 Art. 21(2) security measures enumeration, GDPR Art. 28 documentation checks, red flags per dimension, trigger-based review criteria, and document templates. Use when: (1) Evaluating new vendors or technology providers, (2) Conducting critical ICT third-party due diligence under DORA, (3) Performing supply chain security assessment under NIS2, (4) Creating vendor onboarding documentation, (5) Establishing ongoing vendor monitoring, (6) Assessing concentration risk, or (7) Generating executive vendor risk reports.

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Assess compliance with the EU General-Purpose AI (GPAI) Code of Practice under the AI Act (Regulation (EU) 2024/1689). Use when asked to determine GPAI model provider obligations, assess Code of Practice compliance, evaluate transparency or copyright requirements for GPAI models, check systemic risk obligations, build a GPAI compliance roadmap, or prepare for AI Office requests. Covers all three Code chapters (Transparency, Copyright, Safety & Security), the 12 commitments and their measures, systemic risk classification (Article 51), provider obligations (Articles 53 and 55), open-source exemptions, enforcement timeline, Commission/AI Office supervision, and DACH-specific overlays such as BSI cybersecurity and works council implications. Triggers on phrases like "GPAI compliance", "Code of Practice assessment", "GPAI model obligations", "systemic risk evaluation", "AI Act transparency requirements", "GPAI copyright policy", "general-purpose AI compliance".

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。

Full brand naming workflow for founders, agencies, and businesses. Use this skill whenever the user says "help me name this brand", "brand naming", "I need a name for", "name ideas for", "what should I call my brand/company/product", "naming a startup", "brand name suggestions", "help with naming", or shares a brand brief and asks for name options. Also triggers when the user shares existing name options and asks for feedback, evaluation, ranking, or scoring of those names. Auto-detects whether to run the full generation workflow or the evaluation workflow based on what the user provides. Always use this skill for any brand or product naming task — even if the user just casually mentions needing a name.

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。

Negotiate a pre-code contract between generator and evaluator personas that defines what "done" means before any code is written. Turns fuzzy specs into a testable target the evaluator can hold the generator to.

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Score product remarkability 1-10 to decide if it's worth promoting. Seth Godin's Purple Cow test. Triggers on: "is this product worth promoting", "should I promote", "product audit", "purple cow", "remarkable product", "is it remarkable", "rate this product", "product quality check", "worth my reputation", "product evaluation", "would I recommend without commission", "product remarkability score", "evaluate this affiliate product", "quality gate for promotion".

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Research and evaluate affiliate programs to find the best ones to promote. Use this skill when the user asks anything about finding affiliate programs, comparing commission rates, evaluating affiliate opportunities, searching for products to promote, picking a niche, or mentions openaffiliate.dev. Also trigger for: "which SaaS should I promote", "best affiliate programs for X", "high commission programs", "recurring commission affiliate", "compare these affiliate programs", "is X affiliate program worth it", "find me something to promote", "what pays the most", "affiliate programs with long cookie duration".

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。

Cents-per-point (cpp) valuations across major loyalty programs from four publications. Floor/ceiling rules for deciding if a redemption is good or exceptional.

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Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a benchmark case from this paper", "reverse-engineer raw materials", or "evaluate my pipeline against PaperWritingBench".

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。

This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".

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。

Design taste evaluation framework — DVF primary filter, Apple/Google/Jobs design principles as explicit scoring criteria, weighted decision matrix, and option ranking for the DIVERGE wave

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Dung khi can test co ky luat de biet phuong an nao thang that — chon dung mot bien de test, viet gia thuyet, tinh sample size, setup tracking, doc y nghia thong ke va ghi test log cho ads, landing page, email hoac gia. Kich hoat khi user nhac 'A/B test', 'split test', 'test creative', 'test copy', 'test landing', 'chay bao lau moi du ket luan', 'ban nao thang', 'test xong khong biet doc ket qua', 'ket qua co dang tin khong'. Khong dung cho — so dang xau va can chan doan nguyen nhan thi dung skill 03-danh-gia-hieu-suat; nhan ngan sach cho creative da thang thi dung skill 55-scaling-ads; ra soat toan bo trang khong ra don thi dung skill 68-cro-audit-trang.

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。

编写Premake5的Lua函数，用于一次性自动配置静态库的头文件包含路径（包括第三方依赖）和链接设置，解决多路径包含问题。

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 new product opportunities through demand, channels and economics before committing to build.

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 observed product outcomes against a prior hypothesis; does not run experiments or change user treatment.

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。

Iterative skill tuning via execute-evaluate-improve feedback loop. Uses maestro delegate Claude to execute skill, Agy to evaluate quality, and Agent to apply improvements. Iterates until quality threshold or max iterations. Triggers on "skill iter tune", "iterative skill tuning", "tune skill".

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。

Evaluate LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.

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。

Audit and rewrite evaluation/numeric claims to ensure they carry minimal protocol context (task + metric + constraint) and avoid underspecified model naming.

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。

Diagnoses and fixes skills in the dotnet/skills repository that lose to their own baseline, fail to activate, time out, or return "no credible improvement". Use when an evaluation verdict is a regression or underpowered, when a skill regressed after a change, when /evaluate reports no results, or when deciding whether a weak skill should be strengthened or retired. Do not use for scaffolding a brand-new skill (use create-skill) or a brand-new eval (use create-skill-test).

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。

Scaffolds eval.yaml evaluation specs for skills, custom agents, and redistributable gh-aw workflow packages in the dotnet/skills repository. Use when creating skill or workflow-package tests, writing evaluation stimuli, defining graders and rubrics, sizing an eval for statistical power, or setting up test fixture files. Handles the Vally eval.yaml schema, fixture organization, and overfitting avoidance. Do not use for running or debugging existing evals (use improve-skill-quality) nor for skills authoring (use create-skill).

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Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and deployment review. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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。

Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.

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。