“learning”的搜索结果

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

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Use when the user asks to "create a campaign report", "build an executive summary", or "deliver client results"; produces audience-tailored influencer marketing reports (executive, client, internal team) with data tables, narrative, key learnings, and recommendations. Not for raw metric computation — use performance-analyzer. 达人营销报告/结案汇报

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Use when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs target and benchmark, platform/influencer/content rankings, engagement-quality and sentiment reads, conversion-attribution breakdowns, and ranked learnings. Not for dollar-level return math — use roi-calculator. 达人营销效果分析/投放复盘

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Use when the user asks to "run a launch retro / post-mortem", "compare launch results vs targets by channel", or "decide what to keep or kill for the next launch"; produces a structured D1/W1/M1 retrospective — a per-channel actual-vs-target table (UTM-attributed own analytics as the truth column, platform self-reported numbers as reference, every figure labeled Measured / User-provided / Estimated), a 5-Whys chain on the single largest miss, keep / kill / change decisions per channel, 3-5 actionable learnings for the next launch, and an outcome snapshot submitted to the launch registry. Not for return math (CPA / ROI) — use roi-calculator; not for the stakeholder-facing report writeup — use report-generator; not for a metric deep-dive — use performance-analyzer. 发布复盘/渠道归因/5-Whys/keep-kill

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Use when the user asks to "check pacing", "am I over/under-spending", "is this campaign on track to hit budget", or "why did spend spike/stall mid-flight"; returns a spend-vs-target-curve read, learning-phase status, an over/under-delivery call, and a reallocation trigger. Not for initial budget allocation — use budget-optimizer; not for choosing the bid strategy — use bid-strategy-planner; not for the RQS gate — use ad-account-auditor. 付费广告预算节奏监控/跑量过快过慢/在途配速

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Portfolio building, technical interviews, job search strategies, and continuous learning

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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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Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.

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Expert in JAX for high-performance numerical computing and machine learning

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Best practices for scikit-learn machine learning, model development, evaluation, and deployment in Python

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Design and implement a complete ML pipeline for: $ARGUMENTS

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PyTorch deep learning development with transformers, diffusion models, and GPU optimization.

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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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Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

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Development skill from everything-claude-code

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

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Cross-session progress tracking, pattern extraction, and session resumption. Gives agents memory across sessions using markdown files in `.memory/pipeline/progress/`. Subsumes self-improving-agent — handles both implementation tracking AND experiential learning in one skill. Workflows invoke specific protocols by name.

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Use when working with Coderabbit — codeRabbit AI code review management including automated review comments, auto-suggestions, review customization, and learning from feedback. Use when configuring CodeRabbit for AI-assisted code review, custom review rules, and automated suggestion workflows.

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Use when working with Coveo — coveo enterprise search platform management including sources, indexes, query pipelines, machine learning models, usage analytics, and security providers. Covers indexing health, query performance, ML model status, content freshness, and license utilization.

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Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.

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Trigger when the user asks for explanations of web development concepts, code breakdowns, or learning guidance.

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Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.

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Activate for building or scaffolding web apps when the user is learning (e.g., todo apps, portfolios, dashboards).

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