p988744-nlp-skills

Plugin p988744-nlp-skills from marketplace cascade-ai-ml-engineering in joshuarweaver/cascade-ai-ml-engineering. Commands read from the repo's manifest — 1 month ago.

Skills (7)

• data-pipeline

  This skill should be used when the user asks to "configure data source", "data from database", "fetch data from API", "scrape web data", "generate training data with LLM", "regenerate data", "data pipeline", "where does data come from", or needs to set up reproducible data collection. Provides data

• executing-plans

  This skill should be used when the user asks to "execute plan", "run the plan", "start executing", "continue plan execution", or has a plan file ready to execute. Manages phased implementation with batch execution and checkpoint reviews.

• finetune-llm

  LLM fine-tuning 教練式引導工作流程 v2。 核心功能：主動探索使用者痛點、引導明確目標、多任務管理、資料來源追蹤、完整版本 lineage。 支援：LoRA/QLoRA/DoRA 微調、SFT/ORPO/DPO 對齊、資料準備、Benchmark 評估、HuggingFace 部署。 特色：教練式引導、可重現的資料管線、多任務版本追蹤。 觸發詞：「訓練模型」「fine-tune」「微調」「LoRA」「建立新任務」「改善模型」「優化準確率」「資料管線」「任務管理」

• llm-coach

  This skill should be used when the user asks to "train a model", "fine-tune", "build NLP model", "create training task", "optimize model performance", "improve accuracy", "what model should I use", or expresses vague training needs like "I want to do sentiment analysis" or "help me with NER". Provid

• llm-knowledge

  This skill should be used when the user asks "what is LoRA", "compare models", "which model is best for Chinese", "SFT vs DPO", "how to handle overfitting", "class imbalance solution", "model architecture", "training method comparison", or needs reference information about LLM fine-tuning. Provides

• task-manager

  This skill should be used when the user asks to "list tasks", "show all tasks", "compare versions", "switch to task X", "task status", "version history", "what tasks do I have", or needs to manage multiple training tasks. Provides multi-task management, version tracking, and performance comparison c

• writing-plans

  This skill should be used when the user asks to "write a plan", "create training plan", "plan my fine-tuning", "make a plan for model training", or when starting a new training task that requires multiple steps. Creates detailed implementation plans with bite-sized tasks for LLM fine-tuning workflow

Slash commands (9)

• /p988744-nlp-skills:coach

  啟動教練式對話，引導釐清 LLM 訓練目標

• /p988744-nlp-skills:data-source

  配置資料來源

• /p988744-nlp-skills:deploy

  部署模型

• /p988744-nlp-skills:evaluate

  執行模型評估

• /p988744-nlp-skills:execute-plan

  執行訓練計畫

• /p988744-nlp-skills:generate

  生成專案結構

• /p988744-nlp-skills:new-task

  建立新的訓練任務

• /p988744-nlp-skills:tasks

  列出所有訓練任務狀態

• /p988744-nlp-skills:write-plan

  撰寫訓練計畫

Subagents (4)

• data-source-advisor

  Use this agent when the user needs help configuring data sources for training. This agent advises on data collection strategies and generates data_source.yaml configuration. Examples: <example> Context: User asks about getting training data user: "資料從哪裡來？" assistant: "[Uses Task tool to launch data-

• goal-clarifier

  Use this agent when the user expresses vague or unclear training needs. This agent proactively asks questions to clarify goals, pain points, and constraints. Examples: <example> Context: User mentions wanting to train a model without specific details user: "我想訓練一個模型" assistant: "[Uses Task tool to l

• problem-diagnoser

  Use this agent when the user reports performance issues or asks for help improving model quality. This agent diagnoses problems and recommends solutions. Examples: <example> Context: User reports low performance metric user: "F1 只有 72%，目標是 80%" assistant: "[Uses Task tool to launch problem-diagnoser

• result-analyzer

  Use this agent after training or evaluation completes to analyze results, compare versions, and provide decision recommendations. Examples: <example> Context: Training just completed user: "訓練完成了，結果怎麼樣？" assistant: "[Uses Task tool to launch result-analyzer agent to analyze the training results]" <c

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