원클릭으로 Manus에서 모든 스킬 실행

analysis-assumptions-log

스타3

포크3

업데이트2026년 5월 31일 05:25

Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.

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

ryudkiss-hue

ryudkiss-hue/nyc_data

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Analysis Assumptions Log

When to use

Starting an analysis with significant scope, method, or data quality choices
Preparing work for peer review or stakeholder sign-off
Returning to an old analysis and needing to understand prior decisions
Working in a regulated environment where auditability is required
Handing off an analysis to another analyst

Process

Initialize the log — create a log entry for the analysis with its name, date, analyst, and the decision it informs. Use scripts/assumptions_tracker.py to initialise a structured JSON log.
Enumerate data assumptions — document representativeness, completeness, how missing values are handled, and any known quality issues. For each assumption, record the rationale and confidence level (high/medium/low). See references/assumption_categories.md for the full taxonomy.
Enumerate business logic assumptions — record metric definitions, time windows, inclusion/exclusion rules, and any definitions provided by stakeholders. Note alternatives considered.
Enumerate statistical assumptions — record distribution assumptions, independence claims, stationarity, or model assumptions relevant to the methods used.
Assess impact and flag critical assumptions — for each low-confidence assumption with high impact if wrong, create a validation plan. Run scripts/assumptions_tracker.py --report to surface the critical list.
Validate and close — as validation occurs, update the log with results. Export assets/assumptions_log_template.md for peer review sign-off before delivery.

Inputs the skill needs

Analysis name and the decision it informs
Data sources, time period, and population being analysed
Key methodological choices made (and alternatives considered)
Stakeholder-provided business rule definitions
Any known data quality issues

Output

scripts/assumptions_tracker.py — CLI tool to log assumptions, flag critical ones, and export a summary
assets/assumptions_log_template.md — completed log for peer review and audit trail

이 저장소의 다른 Skills

같은 저장소

data-catalog-entry

ryudkiss-hue/nyc_data

Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.

2026-06-173

analysis-planning

ryudkiss-hue/nyc_data

Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.

2026-06-173

data-quality-audit

ryudkiss-hue/nyc_data

Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating production pipelines, onboarding new data sources, or when stakeholders report data quality concerns.

2026-06-143

segmentation-analysis

ryudkiss-hue/nyc_data

Identify distinct customer or user segments based on behavior, attributes, or value. Activate when you need to answer "who are our best customers?" or "what distinct groups exist in our user base?" and need data-driven profiles to inform strategy.

2026-06-143

visualization-builder

ryudkiss-hue/nyc_data

Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data output.

2026-06-143

methodology-explainer

ryudkiss-hue/nyc_data

Create a clear, transparent explanation of analytical methodology for any audience level. Activate when you deliver findings that require the audience to trust the method — A/B tests, attribution models, forecasts, statistical analyses, or anything where "how did you get that?" is a likely question.

2026-06-143

name	analysis-assumptions-log
description	Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.

Analysis Assumptions Log

When to use

Starting an analysis with significant scope, method, or data quality choices
Preparing work for peer review or stakeholder sign-off
Returning to an old analysis and needing to understand prior decisions
Working in a regulated environment where auditability is required
Handing off an analysis to another analyst

Process

Initialize the log — create a log entry for the analysis with its name, date, analyst, and the decision it informs. Use scripts/assumptions_tracker.py to initialise a structured JSON log.
Enumerate data assumptions — document representativeness, completeness, how missing values are handled, and any known quality issues. For each assumption, record the rationale and confidence level (high/medium/low). See references/assumption_categories.md for the full taxonomy.
Enumerate business logic assumptions — record metric definitions, time windows, inclusion/exclusion rules, and any definitions provided by stakeholders. Note alternatives considered.
Enumerate statistical assumptions — record distribution assumptions, independence claims, stationarity, or model assumptions relevant to the methods used.
Assess impact and flag critical assumptions — for each low-confidence assumption with high impact if wrong, create a validation plan. Run scripts/assumptions_tracker.py --report to surface the critical list.
Validate and close — as validation occurs, update the log with results. Export assets/assumptions_log_template.md for peer review sign-off before delivery.

Inputs the skill needs

Analysis name and the decision it informs
Data sources, time period, and population being analysed
Key methodological choices made (and alternatives considered)
Stakeholder-provided business rule definitions
Any known data quality issues

Output

scripts/assumptions_tracker.py — CLI tool to log assumptions, flag critical ones, and export a summary
assets/assumptions_log_template.md — completed log for peer review and audit trail