1.7천건 · 관련도순
Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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Curate Claude Code's auto-memory into durable project knowledge. Analyze MEMORY.md for patterns, promote proven learnings to CLAUDE.md and .claude/rules/, extract recurring solutions into reusable skills. Use when: (1) reviewing what Claude has learned about your project, (2) graduating a pattern from notes to enforced rules, (3) turning a debugging solution into a skill, (4) checking memory health and capacity
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AID Memory System for session feedback, learning cycles, and skill improvement. Manages state, feedback collection, and improvement analysis. Load for /aid-init, /aid-start, /aid-status, /aid-end, /aid-improve, /aid-memory, /aid-reset.
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Decision transparency, feedback collection, and debate invitations for AID methodology. Active in ALL phases for ALL roles.
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Use when structuring a multi-session learning roadmap with milestones, gates, and pacing calibrated to learner level and goal.
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Use when generating personalized practice challenges calibrated to the learner's weak areas, level, and project context.
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Use when explaining a technical concept and adapting depth, pacing, and examples to the learner's readiness.
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Use when capturing or restoring a learner's persistent profile to personalize teaching across sessions.
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Use when evaluating a completed or in-progress project across scope, correctness, quality, testing, and learning outcomes using structured Praise-Critique-Grow feedback.
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Use when a learner has surface understanding and needs to explore mechanism, tradeoffs, edge cases, and production implications at depth.
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過去の仕訳日記帳から仕分けパターンを確認・更新し、wikiのパターンページを保守する手順
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Teach software development through active project work while still producing a working result, maintaining bilingual English and Chinese project documentation, protecting private context, and publishing reviewed updates to GitHub only after confirmation. Use when Codex helps the user create, build, extend, debug, refactor, document, publish, or understand a software project and the user's goal includes learning, mentorship, guided implementation, pair programming, understanding the code, or becoming able to do similar work independently.
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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).
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作業中に得た「次のセッションでも役立つ事実」を repo ごとの学びとして記録・参照・削除する。失敗から修正に至った手順、利用者の訂正、エラーになったコマンドと通ったコマンド、利用者が明言した方針を得たとき、または過去の学びを確認・削除したいときに使う。
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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
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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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Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo.
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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 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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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.
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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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Write and register a new Learning Hub article (Nuxt Content markdown under content/learn) with valid frontmatter, navigation entry, route list entry, and accurate product details. Use when adding or substantially rewriting a learn guide, reference, or tip.
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