3,358 件 ・ 関連度順
概要と使いどころ
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
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。
Help users navigate complex product decisions by weighing competing priorities and calculating long-term value. This skill covers build-vs-buy logic, technical debt evaluation, and communicating tradeoffs to senior leadership.
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。
Run evaluations on Agent Skills to measure their effectiveness. USE FOR: "run skill evals", "evaluate my skill", "test skill quality", "check skill triggers", "skill compliance check", "measure skill performance", "run evals on [skill-name]", "grade skill execution". DO NOT USE FOR: writing skills (use skill-authoring), improving frontmatter (use sensei), or general testing unrelated to skills.
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 proactively verifying an installed BGS modpack batch before declaring it good. Triggers - "test the pack", "verification", "post-install check", "is it stable", "what should I test", "测试整合包", "验证安装". NOT for reactive crash/performance diagnosis after failure (use diagnosing-bgs-problems), pre-install mod evaluation (evaluating-bgs-mods), or defining batch boundaries/style (curating-bgs-modpack).
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 the credibility of the causal evaluation of a policy is the bottleneck for an AEJ: Economic Policy manuscript — DID/event study, IV, RDD/bunching, or RCT of a program. Stress-tests the quasi-experimental policy-evaluation design to the AEJ: Policy bar before exhibits are finalized; it does not build the welfare mapping or write exhibits.
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。
当你在为《自动化学报》(Acta Automatica Sinica, AAS) 稿件准备代码与数据可用性材料、判断本刊是否设有独立制品评审(artifact evaluation)徽章制度时调用。讲清本刊现状(以同行评议为主、无独立徽章制度的现况为待核实)、如何主动提供匿名可复现的代码/数据以增强说服力、控制仿真与实物实验制品的组织，帮助控制/自动化/模式识别方向的中文稿在 Acta Automatica Sinica 外审中用可用制品提升可信度。
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。
当你为《电子学报》(Acta Electronica Sinica, AES) 投稿准备代码、数据与硬件设计等研究制品(artifact)的可用性说明时使用。中文学报暂无独立的 artifact evaluation 徽章委员会，本技能说明本刊现状、如何自愿开放代码/数据以增强稿件可信度、如何在正文与附录中规范披露制品、如何在双盲阶段匿名托管仓库、以及电子/信号处理类工作(算法、电路、FPGA、数据集)的制品打包要点。帮助作者在没有强制徽章的前提下最大化《电子学报》稿件的可复现说服力。
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 designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.
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。
End-to-end news-triggered signal composite. Takes any piece of news — an article, LinkedIn post, tweet, announcement, event, trend, regulation, product launch, acquisition, layoff, expansion, or any other public event — and evaluates whether the companies or people mentioned are ICP fits. If yes, identifies the connection between the news and your product, finds the right people to contact, and drafts personalized outreach using the news as the hook. Tool-agnostic. Accepts both company-level and person-level news triggers. AUTO-TRIGGER: Load this composite whenever a user shares a URL (LinkedIn post, article, tweet, blog post) or mentions a company/person they "came across", "saw", or "found" from any external source and asks about relevance, fit, ICP match, or whether to reach out. The user does NOT need to explicitly say "outreach" — any signal evaluation request from an external source triggers this.
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 a user asks an agent to evaluate or tune text search similarity for an existing Viking AI Search application and dataset.
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。
Select and use Audio Toolbox acoustic, psychoacoustic, and speech quality metrics. Use when computing loudness, sharpness, roughness, fluctuation strength, noise criteria (NC/RNC/RC), speech transmission index (STI), speech intelligibility index (SII), speech quality (ViSQOL, STOI), SPL, reverberation time (RT60), tone-to-noise ratio, or prominence ratio. Also use when asked about PESQ, POLQA, calibration factor, EBU R 128, LUFS, HVAC noise, background noise evaluation, or how to measure audio quality or noise compliance. Covers calibration, standard selection, signal alignment, and the complete function inventory including what does NOT exist.
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。
Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.
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 exploring the ai-agent-skills catalog to find, compare, and evaluate skills before installing. Always use --fields to limit output size and --dry-run before committing to an install.
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, improve, and evaluate reusable agent skills with high-quality SKILL.md files, precise trigger descriptions, progressive disclosure, and testable behavior. This skill should be used when users ask to create a new skill, rewrite or review an existing skill, audit a skill collection such as `config/source/skills` for redundancy or overlap, improve skill trigger quality, organize skill references, or evaluate whether a skill should trigger and behave correctly.
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。
A platform decision framework for experimentation. When to use Statsig vs PostHog vs GrowthBook vs Optimizely vs Amplitude vs Eppo vs Kameleoon. How to migrate between them. How to coordinate when multi-platform is genuinely warranted. The decisions that compound for years and the ones you can defer. Triggers on which experimentation platform, choose Statsig vs PostHog, evaluate experimentation tools, switch experimentation platform, migrate from Optimizely, consolidate experimentation tools, multi-platform experimentation, experimentation platform decision, ab test platform selection, feature flag platform vs experiment platform, warehouse-native experiments, vendor lock-in experimentation. Also triggers when a team is asking about cost, governance, or migration cost across experimentation tools, or when an evaluation is starting.
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 or evaluate a brand visual identity system covering logo, color, typography, imagery direction, iconography, and motion principles. Use this skill whenever the user wants to design a logo, build a visual identity, define brand colors, choose brand typography, develop iconography, plan brand imagery, or evaluate an existing identity for cohesion. Triggers on logo design, brand identity, visual identity, brand mark, wordmark, monogram, color palette, brand colors, brand typography, type system, iconography, brand imagery, motion design, brand system, identity system. Also triggers when the user has a brand direction approved and now needs the visual artifacts that express it.
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 RAG pipelines: chunking, retrieval evaluation, and architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, or evaluating with RAGAS metrics.
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。
M&A strategy for acquiring companies or being acquired. Use when evaluating acquisitions, preparing to be acquired, conducting due diligence, planning integration, or negotiating deal terms (valuation, LOI, earnout, deal 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。
Designs multi-agent system architectures with orchestration patterns, tool schemas, and performance evaluation. Use when building AI agent systems, designing agent workflows, creating tool schemas, or evaluating agent performance.
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。
Writes, refactors, and evaluates prompts for LLMs generating optimized templates, structured output schemas, evaluation rubrics, and test suites.
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。
Comprehensive technology stack evaluation and comparison tool with TCO analysis, security assessment, and intelligent recommendations for engineering teams
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 the user wants to test their LLM/agent application for safety and security vulnerabilities — jailbreaks, prompt injection, PII extraction, harmful content generation, or evaluator gaming. Also use when the user mentions security testing, adversarial testing, red teaming, safety evaluation, ASR (Attack Success Rate), or "is my app safe to deploy." Outputs ASR paired with over-refusal rate and an audit document.
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 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.
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 the user has a judge/grader and human-labeled data, and wants to measure how well the judge agrees with humans, detect systematic biases, determine whether automatic evaluation can replace human review, or build a human-reduction roadmap. Also use when the user mentions calibration, TPR/TNR, judge validation, inter-rater agreement, Cohen's kappa, bias detection, or "is my automatic evaluation trustworthy." Merges the calibrate and align functions into one 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。