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analysis-assumptions-log

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UpdatedMay 31, 2026 at 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.

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ryudkiss-hue

ryudkiss-hue/nyc_data

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Related occupationsSOC

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Data ScientistsComputer and Mathematical Occupations·SOC 15-2051

SKILL.md

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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

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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