'raise' 검색 결과

Use this skill when the user is building a fundraising / investor pitch deck — seed, Series A / B / C, convertible note, SAFE round, strategic raise. Trigger on: 'pitch deck', 'investor deck', 'Series A deck', 'Series B deck', 'Series C deck', 'fundraising deck', 'seed pitch', 'VC deck', 'raising capital', 'term sheet presentation'. Output is a single .pptx. This skill is a scene layer on top of officecli-pptx — inherits every pptx v2 rule (visual floor, grid, palettes, connector canon, Delivery Gate). DO NOT invoke for a generic board review, sales deck, all-hands, or product launch — route those to officecli-pptx base.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decode a counteroffer after you resign — what the raise, promotion promise, or title bump really signals, the statistics-informed risks of staying, and a clear-eyed decision framework. Use when asked my company countered my resignation, should I accept a counteroffer, they offered me more to stay, or decode this retention offer. Produces a component-by-component decode with 🔴🟡🟢 severity, the questions that expose which promises are real, and the stay/go decision sheet.

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Write a Chinese workplace weekly or monthly report (周报 / 月报) for a manager: results with numbers, progress against goals, risks raised early, next week's plan and the support needed, in the structure Chinese managers expect. Use when asked 帮我写周报, 写月报, 整理本周工作, weekly report in Chinese, or turn my notes into a 周报. Produces the report in Chinese with 本周完成, 关键数据, 问题与风险, 下周计划 and 需要的支持, plus a one-line summary for chat apps.

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Decode a care-home contract before a parent moves in — the fee-escalation clause with no cap, the needs-can-no-longer-be-met eviction trigger, the guarantor line that quietly makes you personally liable, and the after-death charges nobody mentions on the tour. Use when asked to review a care-home or nursing-home contract, what does this admission agreement mean, can they raise the fees, or can they make my parent leave. Produces a clause-by-clause decode ranked by financial and eviction risk, the questions to put in writing before signing, and the negotiate-or-verify list. Pairs with long-term-care-options for choosing the home; this is for the contract. Not legal advice.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Explain a cap table, dilution, SAFEs, option pools, and round mechanics in plain English with the actual math. Use when asked to explain dilution, model a SAFE or priced round, size an option pool, understand a term sheet's economics, or figure out who owns what after a raise. Produces a worked ownership breakdown before/after the round, the dilution math step by step, and the traps founders miss. Not legal or financial advice.

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Assess whether and how someone can safely stay in their own home as they age — the home hazards, the support gaps, and the modifications and services that make it work. Use when asked can my parent stay in their home safely, aging in place assessment, is it safe for them to live alone, or what do we need for them to stay home. Produces a room-by-room safety read (fall hazards, accessibility), an honest look at the daily-living and support gaps, the modifications and services that could close them, warning signs that home may no longer be safe, and how to raise it respectfully — helping a family make a clear-eyed, dignity-preserving decision. Not medical advice.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Learning capture system that extracts HIGH/MED/LOW confidence patterns from conversations to prevent repeating mistakes. Use after user corrections ("no", "wrong"), praise ("perfect", "exactly"), or when discovering edge cases. Complements .squad/agents/{agent}/history.md and .squad/decisions.md.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when the user is building a fundraising / investor pitch deck — seed, Series A / B / C, convertible note, SAFE round, strategic raise. Trigger on: 'pitch deck', 'investor deck', 'Series A deck', 'Series B deck', 'Series C deck', 'fundraising deck', 'seed pitch', 'VC deck', 'raising capital', 'term sheet presentation'. Output is a single .pptx. This skill is a scene layer on top of officecli-pptx — inherits every pptx v2 rule (visual floor, grid, palettes, connector canon, Delivery Gate). DO NOT invoke for a generic board review, sales deck, all-hands, or product launch — route those to officecli-pptx base.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Write publication-ready ML/AI/Systems papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM, OSDI, NSDI, ASPLOS, SOSP. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Produce an experiment blueprint from a research hypothesis

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Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.

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Guides researchers through structured ideation frameworks to discover high-impact research directions. Use when exploring new problem spaces, pivoting between projects, or seeking novel angles on existing work.

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Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Search academic literature and generate research hypotheses

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Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Draft a LaTeX research paper from all previous stage outputs

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Generate a Python code skeleton from an experiment blueprint

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