152 件 ・ 関連度順
概要と使いどころ
Comprehensive research corpus covering 40+ agentic coordination protocols and ecosystems (MCP, A2A, ANP, UCP, ACP, AGNTCY, NANDA, FIPA, WoT, etc.) with emphasis on identity architectures, governance bodies, and interoperability convergence patterns.
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。
OCRs scanned or image-only corpora with vision-language models in three phases. Evaluate selects a system by measured CER or WER against stratified human ground truth. Run builds the production pipeline. Clean corrects raw OCR with LLM and rule-based passes, quality diagnostics, and span-level provenance. Use when choosing an OCR model for a corpus, measuring OCR accuracy, transcribing scans at scale, or correcting raw OCR. Born-digital documents with text layers go to doc-to-markdown.
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。
Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.
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 RAG (Retrieval-Augmented Generation) agents with Google ADK and Vertex AI RAG Engine. Use when implementing document Q&A, knowledge base search, or citation-backed responses. Covers VertexAiRagRetrieval tool, corpus setup, and citation formatting.
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 an open source project's AGENTS.md and produce a corpus entry for the /agents-md directory. Use when adding a project to the directory, refreshing an existing entry, or running a batch of candidate repositories.
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 a learning retrospective on the Kai harness. Mines gate-failure logs and 30-day performance results into lessons, triages candidate lessons (promote/keep/retire), and graduates repeated lessons into enforced gate checks with golden corpus cases. Use when "retro", "what have we learned", "triage lessons", "promote lessons", "why does this keep failing", "harness retrospective", or monthly / after any heavy content sprint.
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 EnterpriseRAG-Bench to evaluate an enterprise RAG or knowledge-agent system against a realistic synthetic company corpus with answer, recall, and comparative scoring.
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 a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.
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。
Research, implement, modify, and audit behavior inside Bricks Builder sites using the bundled Bricks Academy corpus, stable development workflows, and version-aware verification against an authorized active Bricks installation. Use when the requested outcome concerns Bricks elements, controls, schema, hooks, templates, styling, responsive settings, dynamic data, queries, forms, interactions, custom elements, or page JSON. Do not use merely because Bricks Builder is mentioned, for generic WordPress, PHP, CSS, or JavaScript work, or for maintaining, naming, versioning, documenting, or releasing this Skill repository.
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。
Triage the content corpus against the latest GSC snapshot and produce a prioritized refresh report. Read-only: reports, never edits.
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。
Research UI component patterns across the component.gallery corpus — 60 canonical component types, ~95 production design systems, 2,600+ examples — plus an AI-era overlay (prompt inputs, tool-call cards, streaming status, citations) and composite patterns (command palette, app shell, agent console). Compare implementations, find alternative component names, ground frontend decisions in real-world precedent. Pairs with minoan-frontend-design for research-then-build workflows. Triggers on component patterns, how do others implement X, design system research, component comparison, AI chat components, agent UI patterns.
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。
Si attiva quando l'utente chiede di scrivere, redigere, tradurre o revisionare testi italiani per uso pubblico, editoriale, blog, social, newsletter, marketing, narrativa, saggistica o divulgazione. Si attiva anche quando l'utente chiede di umanizzare testi italiani, eliminare pattern AI, correggere lessico da chatbot, catturare o calibrare la voce di un autore su un corpus di suoi testi, rispondere a un rilevatore automatico che ha segnalato come generato un testo scritto da una persona, o quando rivede testi italiani generati da modelli linguistici.
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。
Find out whether a chemical series is already claimed, using SureChEMBL's patent-extracted compound corpus and, where a key is available, PatentsView for legal status and assignee history. Use this skill to trace a structure to the patent documents that disclose it, survey an assignee's filings around a target, and understand what the freedom-to-operate question requires that a structure search cannot answer. Also trigger on SureChEMBL, patent chemistry, Markush structure, freedom to operate, composition of matter, assignee, priority date, patent family, or PatentsView.
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 points to a study-material folder and asks to make it teachable. Not for remote, credential, publish, deploy, or irreversible changes.
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 needs to measure fuzz corpus coverage, explain a coverage plateau, or turn uncovered regions into campaign work. Not for harness creation: use fuzz-harness-writing.
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 planning an end-to-end fuzzing program for a project: engine and target selection, corpus management, and CI or nightly wiring. Not for harness design: use fuzz-harness-writing. Not for engine operation or triage: use libfuzzer. Not for sanitizer flag reference: use sanitizers.
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。
最强经理之争——把四位风格迥异的中国权益基金经理（易方达郑希、易方达张坤、易方达武阳、财通金梓才）放进同一个擂台， 用他们各自的全部公开观点原文 + 全部在管基金真实数据（季度持仓/净值/回撤/规模），做可溯源的对比与应用。能做： (1) 经理之争擂台——同口径比收益/最大回撤/回撤恢复/波动/Calmar/换手/规模，给"分场景谁更强、各自长短板"； (2) 行业/个股透视——给一个行业或股票，看四人谁持有/谁加减/谁更早布局，再用各自框架给"四个镜头"的综合看法； (3) 持仓/调仓追踪——逐季还原谁加仓、减仓、新进、清仓（季度颗粒度，公开数据能到的最细）； (4) 多视角参考——用四人的方法和真实操作，多角度帮你理解一只基金/一个行业、校准自己的判断（只呈现不同视角，不替你做决定、不构成建议）； (5) 溯源问答/方法讲解/风格化点评——引用某位经理本人原话答其观点，讲其投资框架，或用其口吻点评； (6) 言行对照——用真实持仓核对某位经理"说的"和"买的"。 (7) 进阶玩法——风格雷达+人设卡、本季调仓快报(共识/分歧)、言行一致性打分、观点时间机器(谁最早看到)、四人会诊(四种视角点评)、自定义擂台(把任意基金/经理拉进来比)。 When the user mentions 最强经理之争 / 经理对比 / 谁更强 / 回撤谁控得好 / 张坤/郑希/武阳/金梓才 / 景气轮动 vs 买入持有 / 风格雷达 / 人设 / 谁最早布局 / 言行一致 / 四人会诊 / 把某基金加进来比， asks who holds a sector or stock, wants 调仓追踪/调仓快报, a 风格化点评, words-vs-holdings, a style radar/persona, an opinion timeline, or to apply their methods to a fund/sector ——use this skill, even without the word "skill", even if the topic isn't in a corpus (fall back to that manager's method). 引用忠于原文、不杜撰；原话/推演/需核实分清；季度颗粒度非逐笔；研究学习辅助，非投资建议。
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 only for user-provided academic text, explicit scholarly claims, existing citations, or known bibliographies needing citations added, verified, replaced, extracted, or formatted. Supports claim-to-source matching, citation-support checks, replacements, and common academic citation formats. Do not use for general fact-checking, legal/journalistic citation work, open-ended 'find literature about X', topic-level exhaustive discovery, systematic-review corpus construction, PRISMA searches, or creating new research claims. Chinese: 为已有段落补参考文献、核验已有引用、替换错误引用、提取需引文论断、GB/T 7714 格式化.
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。
Built by DynG AI for PlatformPilot.ai-style Go agent runtimes. Use when the user wants to scaffold HandoffKit runtime primitives into a Go project: channel-backed mailboxes, select, router, broker, join/quorum barriers, trace, budget, corpus/compactor, supervisor/nursery, dead letters, and sketch interfaces. Trigger words include PlatformPilot scaffold, scaffold handoffkit, add the HandoffKit runtime, set up message-passing agents, or drop in CSP agent primitives.
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 planning a multi-article content strategy from a research corpus, creating domain maps for content topology, building production plans for pillar and cluster articles, or tracking cross-article 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。
Use when ingesting source material for a writing project, building a knowledge map from notes and documents, identifying research gaps, or preparing a research corpus for content creation.
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 asks about infostealer families (LummaC2, RedLine, Vidar, Stealc, Raccoon, Rhadamanthys, etc.), log marketplaces (Russian Market, Genesis successors, BidenCash, Hudson Rock corpus), or stealer-driven incidents and credential exposure. Self-updating knowledge cell.
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。
"Implement TF-IDF scoring to measure term importance relative to a document corpus. Use this skill when the user needs to rank documents by keyword relevance, extract important terms from text, or build a basic search relevance engine — even if they say 'find relevant documents', 'keyword extraction', or 'term importance'.".
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。
"Implement LDA topic modeling to discover latent topics in document collections. Use this skill when the user needs to extract topics from a text corpus, categorize documents by theme, or explore thematic structure — even if they say 'what are the main topics', 'topic extraction', or 'document clustering by theme'.".
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。