数据分析、机器学习、LLM 应用、提示词设计（8882 个）
Add a model provider to OpenClaw and verify it with a real response. The skill validates configuration, keeps credentials in environment or file references, and reports test results and any default model changes.
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通过Gemini CLI单次执行问答、文本生成和文件内容摘要，可指定模型及JSON输出格式，并使用管理扩展、技能、钩子和MCP连接的命令。
Create an SVG usage card from local Claude Code, Codex, Gemini CLI, and OpenCode logs. It summarizes token counts, equivalent API cost, consecutive activity days, and each agent's share for inclusion in a README or documentation.
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Restructure repeated LLM API requests so shared instructions and reference material can be cached, meaning reused across calls. Track cached input, write and storage costs, and response times to assess whether the changes actually save money.
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Build Bengali search and AI features with guidance on consistent character encoding, word splitting, digit conversion, and safe text display. Includes patterns for cleaning training data, storing text, and handling Bengali alongside English.
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Extract reusable solutions and project conventions from completed Claude Code sessions and save them as learned skills. This deprecated version documents the older session-end workflow and explains how it differs from its successor.
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Design text extraction for forms, invoices, and quizzes using regex, rules that match text patterns. Check extraction confidence and send uncertain items to an LLM for review, with guidance on testing and tracking processing costs.
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Helps write and review PyTorch models, training loops, and data loading code. Covers repeatable experiments, saving training progress, and techniques for reducing GPU memory use or improving training speed, with practical code examples.
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Repeat candidate searches and comparisons while keeping an append-only decision log. Record evidence, compare results with earlier winners, and check replay, freshness, and approval requirements before moving to live trading or production actions.
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Design recommendation and ranking workflows that select the best items for a user or situation. Organize candidate retrieval, enrichment, filtering, scoring, selection, and background recording into six stages, then generate a runnable code scaffold.
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Turn a draft request into a ready-to-paste prompt by clarifying intent, missing context, and completion criteria. Recommend suitable ECC commands, skills, and agents, then provide full and compact versions with explanations for the changes.
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Design or review repeating agent workflows with machine-checkable goals, protected boundaries, independent verification, and retry limits. It helps identify endless retries, manipulated checks, and incorrect completion while keeping final judgment with a human.
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Add machine learning to an existing application through a structured workflow. The skill guides problem definition, data checks, separation from business logic, and training and evaluation of a simple initial model.
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Design an OpenClaw agent persona from preferences or random draws. Build a consistent background, personality, behavioral boundaries, name, SOUL.md and IDENTITY.md files, plus an avatar prompt, with optional image generation through an approved skill.
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Plan and review machine learning systems for production use, covering data requirements, repeatable training, quality checks, deployment, monitoring, and rollback. Produce concrete plans and review findings for moving model experiments into operational systems.
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Check whether machine learning training is available for a completed Itô GPU booking. Training is currently unavailable; the skill explains this limitation and documents the requirements for a future confirmed handoff of a training plan.
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Clarify the availability and requirements of model serving on booked Itô GPUs. The supplied implementation does not support serving, so it reports that limitation and documents future booking verification, configuration review, and explicit confirmation requirements.
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Gather relevant code and background for subagents, agents assigned part of a larger task. Search broadly, assess each file, identify missing information, and refine the search for up to three cycles to assemble context for bug fixes or feature work.
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Add on-device language model features to iOS 26+ apps with Apple's FoundationModels framework. Covers text generation, summarization, extracting data into Swift types, calling custom app functions, and progressively updating screens as responses are generated.
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Design an LLM API workflow that controls spending through task-based model selection, budget tracking, selective retries, and caching repeated prompts. Use the examples to route simpler tasks to cheaper models and track cumulative costs.
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Design continuous agent workflows with quality checks, evaluations, and recovery controls. Helps choose a loop pattern for the task and define how to pause, audit, narrow scope, and retry when work stalls or fails.
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Find bottlenecks in large data transfers, imports, transformations, and historical data catch-up. Benchmark alternative processing methods, turn the fastest correct approach into a repeatable workflow, and verify file counts, row counts, and timestamps after execution.
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Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.
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Helps design ClickHouse tables, write analytical queries, and improve slow queries. Covers loading data in batches, preparing reusable summaries, and monitoring performance for large datasets, real-time dashboards, and time-series analysis.
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