'nsf' 검색 결과

Guides implementation of UK international data transfer mechanisms post-Brexit including the International Data Transfer Agreement (IDTA), UK Addendum to EU SCCs, UK adequacy assessments, and ICO transfer risk assessment tool. Keywords: UK IDTA, UK addendum, ICO TRA, post-Brexit transfers, UK GDPR.

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After the learner demonstrates understanding of a concept, present near-transfer and far-transfer challenges. Use to test whether learning is portable or task-specific — this is what separates understanding from familiarity.

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"Apply the three-level framework of digital transformation — Digitization, Digitalization, and Digital Transformation — to diagnose and plan organizational change enabled by digital technologies. Use this skill when the user needs to assess an organization's digital maturity, distinguish between automating processes versus transforming business models, plan a DX roadmap, or when they ask 'where are we on digital transformation', 'is this digitization or real transformation', or 'how do we build a DX strategy'.".

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Use when generating sexy/NSFW or posed images with local Qwen-Image 2.1 — pose/NSFW LoRAs (tawsLoRa femaledVaginus, TheseAlpacas, posture-v3), pose transfer from an existing image, or exact pose control via VNCCS PoseStudio 3D pose library. Triggers: 性感姿势, 摆姿势, NSFW 出图, 换姿势, 姿势控制, pose transfer, pose lora, VNCCS, 人偶姿势, 姿势库. Independent of any general image-generation skill (which handles SFW text/edit/cutout work).

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Use when re-assigning record OwnerId across many records — territory realignment, employee departure, region split, integration cleanup. Triggers: 'mass transfer accounts', 'reassign opportunities to new owner', 'transfer all records on user deactivation', 'OwnerId migration', 'mass transfer records tool', 'bulk owner change', 'transfer before deactivating user', 'account team lost after owner change', 'assignment rule overrode my OwnerId column', 'defer sharing calculations', 'roll back an owner transfer'. NOT for auto-assigning new records — use admin/assignment-rules. NOT for territory assignment data — use data/territory-data-alignment.

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Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

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• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Prepare and review research funding proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Maps application requirements to draft sections, identifies gaps, and helps refine research aims, budgets, impact statements, and responses to reviewers.

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• Map funding-call requirements to proposal sections
• Review research aims against evaluation criteria
• Align budgets and timelines with the research plan

Quantize diffusion transformers with NVIDIA ModelOpt, using FP8 or NVFP4 lower-precision numbers, and prepare the saved weights for SGLang Diffusion. Follow a workflow for loading, checking output quality, and comparing speed under matching conditions.

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• Preparing ModelOpt FP8 weights for SGLang Diffusion
• Checking quality after diffusion model quantization
• Comparing BF16 and quantized generation speed

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

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• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Learn how GPT language models work by preparing text, training a small model, and generating samples. Covers CPU experiments, custom datasets, further training of pretrained GPT-2 models, and reproducing GPT-2 with multiple GPUs.

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• Train a small model on Shakespeare using a CPU
• Prepare custom text for model training
• Further train a pretrained GPT-2 model

Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.

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• Generate document embeddings for RAG
• Find sentences with similar meanings
• Group documents by topic

Apply HQQ quantization, which stores language model weights using fewer bits without calibration examples. Covers precision settings, inference backends, saving compressed models, deployment with HuggingFace Transformers or vLLM, and further training with LoRA.

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• Compressing a model without calibration data
• Comparing 4-bit and 2-bit quantization settings
• Running quantized models with HuggingFace Transformers or vLLM

Learn how GPT language models work by preparing text, training a small model, and generating samples. Covers CPU experiments, custom datasets, further training of pretrained GPT-2 models, and reproducing GPT-2 with multiple GPUs.

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• Train a small model on Shakespeare using a CPU
• Prepare custom text for model training
• Further train a pretrained GPT-2 model

Build Apache Beam connectors that read and write external data. This guide covers Java implementation, schema support for structured records, access from Python and Go, Managed API integration, and testing.

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• Add a connector for an external data system
• Make a Java connector available to Python and Go
• Integrate a connector with Beam YAML and the Managed API

Supports authorized security exercises that test data transfer outside a network. Organizes channel choices, compression, encryption, splitting, and receipt verification, alongside the monitoring signals defenders can use to identify different transfer methods.

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• Comparing data transfer channels for an authorized exercise
• Planning compression, encryption, and file splitting
• Verifying received files against SHA-256 records

Build stablecoin transfers where the amount sent equals the amount received using LI.FI Intents. The skill covers quotes, token approval, escrow deposits, and order tracking for transfers within or across supported blockchains.

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• Building a stablecoin swap interface with equal input and output amounts
• Adding cross-chain USDC or USDT transfers
• Implementing quotes, approvals, and escrow orders in a backend

Guides systematic mapping of international personal data flows across an organisation. Covers system-by-system inventory methodology, third-party identification, transfer mechanism assignment, gap analysis, and data flow visualisation. Keywords: data flow mapping, international transfers, data inventory, transfer register, cross-border data flows.

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Generate a state document (context transfer) that allows any fresh AI instance to resume work without conversation history. Use this skill whenever the user asks to checkpoint, create a state transfer, save state, generate a handoff document, create compaction insurance, or says anything like 'snapshot this', 'create a context transfer', 'checkpoint', 'save state for handoff', 'write a recovery doc', or 'transfer context'. Also trigger when the user mentions compaction concerns, session handoff, multi-session continuity, or wants to preserve the current conversation's state. Works for any conversation type: projects, research, debugging, investigations, design sessions, or long-running chats. This skill produces a structured markdown document optimized for AI cold-start recovery, not human notes.

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Navigates LP transfer requirements including GP consent, ROFR compliance, and partnership agreement transfer provisions. Use when managing LP transfers, obtaining GP consent, or navigating transfer restrictions.

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Processes LP interest transfer requests with GP consent evaluation, ROFR administration, and transfer documentation requirements. Use when managing LP transfers, evaluating transfer requests, or coordinating interest assignments.

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Expert-level heat transfer covering conduction, convection, radiation, heat exchangers, fins, transient heat transfer, and computational heat transfer.

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Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.

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Manage stored agent memories for backups, project transfers, and team knowledge sharing. Work with RVF, a portable memory format, to export and import records, upgrade formats, inspect storage statistics, and transfer patterns between local projects.

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• Back up stored agent memories
• Transfer memories and patterns between projects
• Share stored knowledge with a team

Guide market prediction workflows with LSTM, Transformer, and N-BEATS models using neural-trader commands and result storage. It explicitly identifies version 2.8.11 outputs as placeholders, so they cannot be treated as fitted models or evidence of prediction accuracy.

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• Preparing market prediction model commands
• Checking whether training outputs are placeholders
• Organizing model run records