원클릭으로 Manus에서 모든 스킬 실행

lemma-eda

스타4

포크1

업데이트2026년 7월 15일 00:47

Explore a fresh dataset when the analytical direction is open; use for orientation, pattern discovery, and deciding what analysis is worth pursuing.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

tkpratardan

tkpratardan/lemma

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Exploratory data analysis

Deliver

Provide a trustworthy dataset orientation, a short ranked set of findings, important limitations, and the next analytical question or baseline suggested by the evidence.

Check

Establish observation unit, source identity, coverage, candidate identifiers or targets, and a seed for sampled work.
Inspect shape, types, ranges, duplicates, missingness, time coverage, and group rates. scripts/profile_table.py is available for a deterministic first pass.
Explore only patterns that could change a decision or next step.
Investigate suspicious quality, leakage, subgroup, or temporal patterns before recommending modeling.
State a conclusion for each explored branch.

Do not generate an indiscriminate chart catalog, silently drop missing rows, infer causality, or turn a bounded question into open-ended EDA.

For visualization and chapter patterns, read references/deep-guide.md.

이 저장소의 다른 Skills

같은 저장소

lemma-baseline

tkpratardan/lemma

Establish a dumb baseline and an honest validation harness before any real model, so every later number means something.

2026-07-154

lemma-causal

tkpratardan/lemma

Rigor for causal questions and A/B tests (the effect of acting on X): confounding, post-treatment bias, valid control groups.

2026-07-154

lemma-describe

tkpratardan/lemma

Rigor for descriptive and diagnostic analytics (what happened and why): denominators, grain, and confounded slices, not model leakage.

2026-07-154

lemma-eda

tkpratardan/lemma

EDA kickoff for a fresh dataset: fixed opening scaffold (goal, imports, load, sanity), then chapters derived from the data; scan leakage, land a baseline.

2026-07-154

lemma-inference

tkpratardan/lemma

Rigor for statistical inference (is the difference real): hypothesis tests, power, multiple comparisons, effect size over p-value.

2026-07-154

lemma-leakage

tkpratardan/lemma

Audit a dataset or pipeline for the five leakages that inflate a metric: target, preprocessing, temporal, group, and sampling.

2026-07-154

name	lemma-eda
description	Explore a fresh dataset when the analytical direction is open; use for orientation, pattern discovery, and deciding what analysis is worth pursuing.

Exploratory data analysis

Deliver

Provide a trustworthy dataset orientation, a short ranked set of findings, important limitations, and the next analytical question or baseline suggested by the evidence.

Check

Establish observation unit, source identity, coverage, candidate identifiers or targets, and a seed for sampled work.
Inspect shape, types, ranges, duplicates, missingness, time coverage, and group rates. scripts/profile_table.py is available for a deterministic first pass.
Explore only patterns that could change a decision or next step.
Investigate suspicious quality, leakage, subgroup, or temporal patterns before recommending modeling.
State a conclusion for each explored branch.

Do not generate an indiscriminate chart catalog, silently drop missing rows, infer causality, or turn a bounded question into open-ended EDA.

For visualization and chapter patterns, read references/deep-guide.md.