94건 · 관련도순
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
p5.js에서 계산 규칙과 난수로 생성 예술을 만듭니다. 난수의 초깃값을 고정해 같은 그림을 재현하며, 포스터와 동적인 느낌의 정지 이미지, 구도 시안 제작에 활용합니다.
Prepare GRPO reinforcement learning configurations for tasks with programmatically checkable answers, such as math, code, and structured output. Design and inspect reward functions, then select training variants when runs show instability or exploit flaws in scoring.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze DNS logs for signs of hidden command traffic and data theft. Use character randomness, query timing, TXT record inspection, and machine learning to identify suspicious domains and support security monitoring rules.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Analyze DNS query logs for signs of hidden data transfers and attacker command channels. This skill uses unusual name lengths, randomness, and traffic volumes, then correlates findings with endpoint processes and estimates possible data loss.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build Python applications that collect live cryptocurrency prices, trades, and order books with Cryptofeed. Covers consistent data formats across exchanges, price comparisons, and writing incoming data to storage systems for monitoring and analysis.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Develop cryptocurrency trading bots with Hummingbot documentation and code examples. Explore exchange connections, price and order book data, and strategies such as market making, which places buy and sell orders, or arbitrage across markets.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Assess API business workflows for flaws that automated scanners can miss. The skill examines skipped steps, simultaneous transactions, authentication tokens, GraphQL, and webhooks, helping testers document weaknesses and their financial or operational impact during authorized assessments.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review Google and Meta campaign structures against goals, budgets, and conversion data. Identify excessive segmentation, overlapping targeting, and fragmented budgets, then get consolidation recommendations, an explained account structure, and a migration plan.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Check and improve existing English LaTeX papers, from compilation errors and bibliography issues to grammar, figures, tables, and pseudocode. Receive focused review comments and suggested fixes, with checks tailored to specific sections or publication venues.
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Compile and improve existing English or Chinese academic papers written in Typst. Check references, formatting, prose, argument flow, abstracts, tables, and pseudocode, with source-level review comments and support for adapting papers to another publication venue.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
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Analyze a software codebase for algorithmic complexity and performance hotspots, then propose or implement safe optimizations without breaking behavior. Use when Codex is asked to scan many files, find inefficient loops, nested iteration, repeated scans, costly rendering/recomputation, N+1 queries, avoidable O(n^2) or O(n) operations, or reduce complexity such as O(n^2) to O(n log n) / O(n), while preserving tests, APIs, outputs, and maintainability.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Algorithmic and generative art with Canvas 2D - particles, flow fields, noise, fractals, L-systems.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Cryptofeed - Real-time cryptocurrency market data feeds from 40+ exchanges. WebSocket streaming, normalized data, order books, trades, tickers. Python library for algorithmic trading and market data analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Hummingbot trading bot framework - automated trading strategies, market making, arbitrage, connectors for crypto exchanges. Use when working with algorithmic trading, crypto trading bots, or exchange integrations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the security slice). Use when the user mentions 'AI risk,' 'AI governance,' 'NIST AI RMF,' 'AI compliance,' 'ML governance,' 'model risk management,' 'AI fairness,' 'AI bias,' 'algorithmic accountability,' 'AI Bill of Rights,' 'EU AI Act,' 'AI transparency,' 'model card,' 'AI red team,' 'AI safety,' 'responsible AI,' 'model drift,' 'concept drift,' 'AI monitoring,' 'AI incident,' or needs to assess or govern an AI / ML system.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Verifies math-heavy code for algorithmic correctness and numerical stability. Use when reviewing scientific algorithms, ML models, or numerical code.
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Addresses healthcare AI privacy at the intersection of HIPAA and the EU AI Act for clinical decision support systems. Covers training data PHI handling, model transparency and explainability, patient rights in algorithmic decisions, FDA/OCR regulatory coordination, and bias monitoring. Keywords: healthcare AI, HIPAA, AI Act, clinical decision support, PHI training data, model transparency.
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Applies the reasoning, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.