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Deep Learning Prediction with CHAID and Time-Series Splitting

100 / 100 · Low risk

Fine to try.

By ECNU-ICALK/AutoSkill (GitHub) · data · 613 stars · scanned at commit 94c47ca

Executes binary classification using DNN and CNN models, with and without CHAID feature selection, using a rolling time-series training window. Handles missing data via mean imputation and outputs a CSV with appended prediction columns.

Works with any agent that supports SKILL.md skills.

What this skill can do

We found nothing in the files we read that matches our rules. That does not prove the skill is safe: see the limits of static scanning.

Install

Paste this into your terminal. It downloads exactly the version we scanned, not whatever is newest.

if [ -e ~/.agents/skills/'deep-learning-prediction-with-chaid-and-time-series-splitting' ] || [ -L ~/.agents/skills/'deep-learning-prediction-with-chaid-and-time-series-splitting' ]; then echo 'A skill folder named deep-learning-prediction-with-chaid-and-time-series-splitting already exists — not replacing it' >&2; false; else d=$(mktemp -d) && git clone --quiet --filter=blob:none --no-checkout 'https://github.com/ECNU-ICALK/AutoSkill' "$d" && git -C "$d" checkout --quiet '94c47ca488d4ba4117d20272e66d49b9877e68cf' -- 'SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/deep-learning-prediction-with-chaid-and-time-series-splitting' && mkdir -p ~/.agents/skills && cp -R "$d"/'SkillBank/ConvSkill/english_gpt3.5_8_GLM4.7/deep-learning-prediction-with-chaid-and-time-series-splitting' ~/.agents/skills/'deep-learning-prediction-with-chaid-and-time-series-splitting' && rm -rf "$d"; fi

Codex reads ~/.agents/skills directly. For Claude Code, Cursor, Copilot, Gemini CLI, Antigravity or OpenCode, also link it. For Claude Code: ln -s ~/.agents/skills/'deep-learning-prediction-with-chaid-and-time-series-splitting' ~/.claude/skills/'deep-learning-prediction-with-chaid-and-time-series-splitting' (other agents: ~/.cursor/skills, ~/.copilot/skills, ~/.gemini/skills, ~/.gemini/config/skills, ~/.config/opencode/skills). Or install through the finder skill, which does both.

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