「vin」の検索結果

When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.

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When the user requests to add academic papers to a BibTeX bibliography file while maintaining format consistency and sourcing from appropriate repositories. This skill handles 1) Reading existing BibTeX files to understand formatting conventions, 2) Searching for academic papers across multiple sources (OpenReview for conference papers, arXiv for preprints), 3) Extracting proper BibTeX metadata from conference pages or arXiv entries, 4) Determining appropriate citation format (@article vs @inproceedings) based on publication venue, 5) Appending new entries while preserving existing file structure and formatting. Triggers include requests to 'add to ref.bib', 'update bibliography', 'cite papers', or when working with academic reference files.

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Use when explaining concepts at different expertise levels, moving between abstract principles and concrete implementation, identifying edge cases by testing ideas against scenarios, designing layered documentation, decomposing complex problems into actionable steps, or bridging strategy-execution gaps. Invoke when user mentions abstraction levels, making concepts concrete, or explaining at different depths.

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Manage persistent memory for autonomous coding. Use when storing/retrieving knowledge, managing Graphiti integration, persisting learnings, or accessing episodic memory.

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Interactive review sessions with academic PDFs (lectures, research papers, book chapters). Extract concepts, run Q&A sessions, generate quizzes with scoring. Preserves mathematical formulas in LaTeX format. Privacy-preserving local processing - PDFs never uploaded. Use when studying academic materials, reviewing research, or preparing for exams.

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[73] CLOSE. Record and maintain Lessons in MEMORY.md after a problem is solved or the user confirms success. Use when capturing a new lesson, moving lessons through the pipeline, or enhancing Project Architecture Quick Reference with new insights.

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[31] ANALYZE. Universal deep thinking methodology for any situation requiring quality reasoning. Use when solving problems, debugging, making decisions, analyzing code, planning, reviewing, or anytime you need thorough thinking instead of surface-level responses. Triggers on "think deeply", "analyze thoroughly", "reason carefully", "deep thinking", "understand completely", or any task requiring careful thought.

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Codified expertise for production scheduling, job sequencing, line balancing, changeover optimization, and bottleneck resolution in discrete and batch manufacturing. Informed by production schedulers with 15+ years experience. Includes TOC/drum-buffer-rope, SMED, OEE analysis, disruption response frameworks, and ERP/MES interaction patterns. Use when scheduling production, resolving bottlenecks, optimizing changeovers, responding to disruptions, or balancing manufacturing lines.

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Azure Queue Storage JavaScript/TypeScript SDK (@azure/storage-queue) for message queue operations. Use for sending, receiving, peeking, and deleting messages in queues.

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Manage secrets using Azure Key Vault Secrets SDK for JavaScript (@azure/keyvault-secrets). Use when storing and retrieving application secrets or configuration values.

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Azure Key Vault Secrets SDK for Rust. Use for storing and retrieving secrets, passwords, and API keys. Triggers: "keyvault secrets rust", "SecretClient rust", "get secret rust", "set secret rust".

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Azure Key Vault Secrets Java SDK for secret management. Use when storing, retrieving, or managing passwords, API keys, connection strings, or other sensitive configuration data.

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Azure Event Hubs SDK for Rust. Use for sending and receiving events, streaming data ingestion.

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Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.

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Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

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Normalizes GitHub issue and PR titles by removing any bracketed [NemoClaw] tag case-insensitively, even when the tag appears later in the title. Use when cleaning issue tags, bulk-renaming titles, or normalizing repo title hygiene.

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Reviews and approves blocked agent network requests in the TUI. Use when approving or denying sandbox egress requests, managing blocked network calls, or using the approval TUI. Trigger keywords - nemoclaw approve network requests, sandbox egress approval tui, customize nemoclaw network policy, sandbox egress policy configuration.

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Backs up and restores OpenClaw workspace files before destructive operations such as sandbox rebuilds. Use when downloading workspace files from a sandbox, uploading restored files into a new sandbox, or preserving sandbox state across rebuilds. Trigger keywords - nemoclaw backup, nemoclaw restore, workspace backup, openshell sandbox download upload, nemoclaw workspace files, soul.md, user.md, identity.md, agents.md, sandbox persistence.

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Sync delta specs from a change to main specs. Use when the user wants to update main specs with changes from a delta spec, without archiving the change.

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Verify implementation matches change artifacts. Use when the user wants to validate that implementation is complete, correct, and coherent before archiving.

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Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.

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Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

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Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".

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Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

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。