Individual skill
analysis-assumptions-log
Track and document analytical assumptions and decisions. Use when making analytical choices, documenting trade-offs, ensuring transparency, or creating audit trails for analytical work.
02-documentation-knowledge/analysis-assumptions-log/SKILL.md
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- claude-code Repository topic
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SKILL.mdcore · 2,327 bytesassets/assumptions_log_template.mddoc · 1,884 bytesreferences/assumption_categories.mddoc · 3,305 bytesscripts/assumptions_tracker.pyruns · 6,610 bytes
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# 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
1. **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.
2. **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.
3. **Enumerate business logic assumptions** — record metric definitions, time windows, inclusion/exclusion rules, and any definitions provided by stakeholders. Note alternatives considered.
4. **Enumerate statistical assumptions** — record distribution assumptions, independence claims, stationarity, or model assumptions relevant to the methods used.