「models」の検索結果

Use CAMEL-AI to build, configure, tool, remember, evaluate, and troubleshoot multi-agent Python systems with self-contained routing across agents, models, tools, RAG, and evaluation workflows.

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。

Routes CogDL graph-learning workflows for experiments, graph data, models, training wrappers, and pipeline apps.

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 BentoML to author model-serving Services, build and containerize Bentos, run HTTP/gRPC servers and clients, manage models, operate the CLI and BentoCloud, and configure observability or production runtime behavior. Use when tasks mention BentoML, bentoml service.py, bentofile.yaml, Bento build/serve/deploy, BentoCloud, or BentoML model stores.

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Operate ASRT SpeechRecognition Chinese ASR data, acoustic models, pinyin language model, and serving clients.

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。

Routes AIX360 explainability tasks across local black-box attribution, counterfactuals and certification, interpretable models, time-series explanations, datasets, and explanation-quality metrics.

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Operate the Align-Anything multimodal alignment package, including training, serving, remote reward models, and satellite projects.

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Main app ViewModel patterns (ScopedViewModel, Hilt, StateFlow, triggerEvent, navArgs, savedState flows, events). Use when writing, editing, exploring, debugging, or reviewing ViewModels in the store management app. Covers navArgs, combine+asLiveData, sealed state hierarchies, custom events, init blocks, and companion object placement. NOT for POS (WooPos*) code — use the `pos` skill instead.

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Research why neural architectures and training methods work through forward computation, geometry, gradients, optimization dynamics, historical experiments, and competing explanations. Use for deep mechanism questions about neural networks, AI models, layers, representations, or training; explain the meaning in plain language with evidence and explicit limits.

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。

Optimal ViewModel and Wizard creation patterns for Avalonia using Zafiro and ReactiveUI.

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。

Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.

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Generates client SDK code, API wrapper libraries, request/response models, and language-specific usage patterns for any REST API.

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Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

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Use when the user needs engineering or research-paper figures: system architecture diagrams, algorithm workflows, hardware schematics, benchmark charts, ablation plots, figure briefs, redraws, image edits, or exact publication-style plots from numeric data. Covers Gemini, Nano Banana, OpenAI image models, and local plotting workflows.

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Assessment of pseudonymization techniques and re-identification risk. Covers tokenization, hashing, encryption-based pseudonymization, and hybrid approaches. Includes re-identification risk scoring using the motivated intruder test, quantitative metrics (marketer, journalist, prosecutor models), and linkage attack resilience evaluation. References ENISA 2019 pseudonymization report.

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Deploy differential privacy in production systems including epsilon selection strategies, noise calibration with Laplace and Gaussian mechanisms, privacy budget tracking, composition theorems, and Python implementation patterns. Covers both central and local differential privacy models.

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Conducts privacy auditing of AI models including training data extraction testing, membership inference attacks, model inversion testing, and attribute inference assessment. Uses ML Privacy Meter and related tools to quantify privacy leakage. Keywords: model audit, membership inference, privacy meter, model inversion, training data extraction.

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Use when designing PostgreSQL + Redis data models, indexes, caching strategies, JSONB usage, tiered storage, or cache consistency contracts.

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Master product strategy, market analysis, competitive positioning, and long-term product vision. Define business models and craft go-to-market strategies that drive success.

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Use when designing organizational structure (team topologies, Conway's Law alignment), mapping stakeholders by power-interest for change initiatives, defining team interface contracts (APIs, SLAs, decision rights, handoffs), assessing capability maturity (DORA, CMMC, agile maturity models), planning org restructures (functional to product teams, platform teams, shared services), or when user mentions "org design", "team structure", "stakeholder map", "team interfaces", "capability maturity", "Conway's Law", or "RACI".

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Research-first content creation optimized for both human readers and AI search engines (Claude, ChatGPT, Perplexity, Gemini). Creates authentic, authoritative content that becomes the go-to citation source for AI models answering user questions. Use this skill when: - Creating content that should appear in AI search results (Perplexity, ChatGPT, Claude) - Building topical authority to become THE source AI cites for a topic - Launching a new product and need compelling, citable content - Creating blog posts, articles, social media, or press releases - Need content that references real trends, people, and recent events - Want AI-assisted content that doesn't sound AI-generated - Creating thought leadership content in any industry Triggers: "create content for", "write about", "research and write", "find experts for", "content for launch", "blog post about", "article on", "press release for", "AI search", "show up in AI", "Perplexity", "be cited by AI"

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Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.

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Use this skill when reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.

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。

Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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Applies the reasoning, principles, and mental models of Sebastian Thrun (robotics and self-driving cars pioneer, founder of Google X, Waymo, Udacity, Stanford University). Reach for this skill whenever Claude is asked to advise on hardware/software systems engineering, autonomous vehicles, moonshot ideation, probabilistic robotics (SLAM), or leading high-stakes engineering teams. Trigger this skill for discussions on democratizing education, regulating AI, transitioning from academic research to product development, or managing technical teams with empathy. Use it to shift focus from incremental component debates to end-to-end execution and audacious goals.

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。