「learning」の検索結果

Reverse-engineer why a design decision was made. Move from observation to inference. Use when studying products you admire, learning from competitors, or building analytical design thinking.

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Study how products evolve over time using archived versions. The decisions that survive redesigns are the taste decisions. Use when understanding what's essential vs. cosmetic in a design, or learning from product evolution.

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Build taste through volume of output, deliberate failure, and speed. Taste cannot be developed by watching. It's sharpened by making, failing, and making again. Use when designing practice routines, breaking through creative blocks, or helping someone move from analysis to execution.

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Analyze yesterday's prompts to surface one technical concept worth understanding, delivered as a concise morning lesson with everyday analogy + engineer vocabulary. Use when "morning lesson", "prompt mirror", "today's learning", "what should I learn", or via scheduled automation each morning.

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Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.

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Use when turning something that happened into something that makes the next run better - "write a retro", "record this paper cut", "this bug escaped verification", "make this an eval", "why do we keep hitting this", "did that change actually help", "scorecard", "what did we learn from that run", "lessons keep getting re-learned". Covers the six-step loop (capture, classify, convert, change, measure, retro), the repo artifact each step produces, and how escaped bugs become eval cases. Do NOT use to design or judge a single workflow experiment (see `workflow-experimentation`, which this loop feeds), to decide where to run work (see `dogfooding`), or to debug one failed execution in the moment.

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Set up and run supervised fine-tuning (SFT) on instruction or chat datasets using the Tinker API. Use when the user wants to do instruction tuning, chat fine-tuning, or supervised learning.

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Set up and run reinforcement learning with verifiable rewards (RLVR/GRPO) for math, code, or custom environments using the Tinker API. Use when the user wants to do RL training, GRPO, reward-based optimization, or train with verifiable rewards.

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Use when: the agent captures a correction from the team lead, hits an unexpected error, discovers a capability gap, or finds a better practice. Logs to .learnings/ and promotes durable patterns to permanent project memory.

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Development skill from everything-claude-code

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AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g

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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.

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Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

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Implement Amazon Macie to automatically discover, classify, and protect sensitive data in S3 buckets using machine learning and pattern matching for PII, financial data, and credentials Tespit.

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Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapt

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Aşamalı CTF ipucu sistemi — takılan kullanıcıya seviyeli ipucu verir. Level 1 (nudge), Level 2 (direction), Level 3 (solution). Her challenge için hint seviyesi ~/.fetih/ctf_hints.json'da takip edilir.

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Reference and learning guide for the Model Context Protocol (MCP). Use when implementing clients/servers or debugging connection issues. For server listings, see mcp-server-registry.

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Implement User and Entity Behavior Analytics using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and detect insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access patterns.

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Detect anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.

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Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models. Supports batch analysis of audio files, generates confidence scores, and produces forensic reports. Activates for requests involving deepfake voice detection, vishing investigation, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.

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Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.

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。