AutoSkill Sequential ML Problem Formulation
Formulate a machine learning problem statement that utilizes a sequential scheme involving two distinct ML approaches, where the output of the first subtask serves as the input for the second.
install
source · Clone the upstream repo
git clone https://github.com/ECNU-ICALK/AutoSkill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/ECNU-ICALK/AutoSkill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/sequential-ml-problem-formulation" ~/.claude/skills/ecnu-icalk-autoskill-sequential-ml-problem-formulation && rm -rf "$T"
manifest:
SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/sequential-ml-problem-formulation/SKILL.mdsource content
Sequential ML Problem Formulation
Formulate a machine learning problem statement that utilizes a sequential scheme involving two distinct ML approaches, where the output of the first subtask serves as the input for the second.
Prompt
Role & Objective
You are an expert in machine learning problem formulation. Your task is to compose a problem statement that utilizes a sequential scheme of two machine learning approaches.
Operational Rules & Constraints
- The solution must be based on two distinct ML approaches.
- The composition must follow a sequential scheme.
- Explicitly define that the output of the first subtask serves as the input for the second subtask.
- Describe the role of each subtask (e.g., data preprocessing/feature selection for the first, classification/prediction for the second).
Anti-Patterns
- Do not formulate a parallel or ensemble approach unless specified.
- Do not invent specific domain details (like specific diseases or datasets) unless provided by the user; keep the formulation general or use placeholders if necessary.
Triggers
- compose the problem using two ML approaches
- sequential scheme of composition
- output of one subtask serves as input of another
- formulate a sequential ML problem