236 件（agentscope-ai のリポジトリ） ・ 人気順
概要と使いどころ
Use when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a labeled evaluation set. Also use when the user mentions test data design, eval coverage, difficulty stratification, synthetic data generation for eval, or "how to create good evaluation data." Outputs datasets in OpenJudge-compatible format.
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。
Build RL reward signals using the OpenJudge framework. Covers choosing between pointwise and pairwise reward strategies based on RL algorithm, task type, and cost; aggregating multi-dimensional pointwise scores into a scalar reward; pairwise tournament reward for GRPO on subjective tasks (net win rate across group rollouts); generating preference pairs for DPO/RLAIF; and normalizing scores for training stability. Use when building reward models, scoring rollouts for GRPO/REINFORCE, generating preference data for DPO, or doing Best-of-N selection.
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 evaluation principles or a dataset but needs help choosing the right graders, designing evaluation metrics, creating LLM-as-judge prompts, combining multiple metrics into a composite score, or building an automated evaluation pipeline. Also use when the user mentions grader selection, metric design, judge prompt engineering, rubric design, evaluation pipeline code, or "how to evaluate [X] automatically." Outputs executable OpenJudge pipeline 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。
Discover and recommend **combinations** of agent skills to complete complex, multi-faceted tasks. Provides two recommendation strategies — **Maximum Quality** (best skill per subtask) and **Minimum Dependencies** (fewest installs). Use this skill whenever the user wants to find skills, asks "how do I do X", "find a skill for X", or describes a task that likely requires multiple capabilities working together. Also use when the user mentions composing workflows, building pipelines, or needs help across several domains at once — even if they only say "find me a skill". This skill supersedes simple single-skill search by decomposing the task into subtasks and assembling an optimal skill portfolio.
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。
Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.
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。
Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style 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。
Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.
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。
Review academic papers for correctness, quality, and novelty using OpenJudge's multi-stage pipeline. Supports PDF files and LaTeX source packages (.tar.gz/.zip). Covers 10 disciplines: cs, medicine, physics, chemistry, biology, economics, psychology, environmental_science, mathematics, social_sciences. Use when the user asks to review, evaluate, critique, or assess a research paper, check references, or verify a BibTeX file.
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 text, images, video, speech, and music via the MiniMax AI platform. Covers text generation (MiniMax-M3 model), image generation (image-01), video generation (Hailuo-2.3), speech synthesis (speech-2.8-hd, 300+ voices), music generation (music-2.6 with lyrics, cover, and instrumental), and web search. Use when the user needs to create AI-generated multimedia content, produce narrated audio from text, compose music, or search the web through MiniMax AI services.
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。
Verify a BibTeX file for hallucinated or fabricated references by cross-checking every entry against CrossRef, arXiv, and DBLP. Reports each reference as verified, suspect, or not found, with field-level mismatch details (title, authors, year, DOI). Use when the user wants to check a .bib file for fake citations, validate references in a paper, or audit bibliography entries for accuracy.
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。
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
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。
Generates English pull request titles and bodies by comparing the current branch with main. Use when the user asks to draft PR content, summarize branch changes, or produce a PR title/body from git diff.
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。
Guidelines for handling text files
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。
Guildlines for handling json files
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。
Guidelines for handling CSV/Excel files
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。
Guidelines for handling image files
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。
Guidelines for handling databases
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。
分析观测数据在不同区间维度上的分布特征。触发条件：当任务强调数据分布特征分析时调用，如出现“数据分布”、“分布情况”等词时触发。
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。
转化漏斗分析，构建产品链路转化漏斗、计算每步转化率、识别卡点、对比行业基准。触发条件：当需要分析多段行为的转化情况时触发，如对话涉及“漏斗”、“转化漏斗”、“阶段A-阶段B-阶段C转化情况”等相近词语时触发。
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。
评估单个用户或用户群体在生命周期内创造的收益。触发条件：当需要用户生命周期价值计算与分析时触发，如对话中涉及“LTV”、“LTV 分析”、“用户生命周期价值”、“人均收益测算”等等相近词语时调用。
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。
获取并计算指标的基础观测数据，包括时间周期确定、指标聚合、衍生指标计算和维度交叉展示表构建。当需要对指标进行基础数据观测、获取当期值与变动幅度时调用。
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。
识别新增维度值并评估其在北极星指标上的表现。当归因维度出现新增值（如新渠道、新品类、新功能）需要评估效果时调用。
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。
将 BI 数据分析结果组织成可视化 HTML 报告。当分析完成、需要生成报告时调用。
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。