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impact-quantification

impact-quantification

github.com/nimrodfisherData & databases504221 installsUpdated 5w ago
Repository

About impact-quantification

A data-analysis skill for estimating the numeric impact of proposed actions and opportunities. It produces low, base, and high estimates with documented assumptions to support decisions and initiative prioritization.

Key features

  1. 01Classify revenue, cost, risk, or efficiency impact
  2. 02Gather baseline and change assumptions
  3. 03Calculate revenue or cost scenarios
  4. 04Estimate low, base, and high ranges
  5. 05Document assumptions and sensitivity

Use cases

  1. 01Quantify an opportunity after an analytical finding
  2. 02Rank initiatives by estimated impact
  3. 03Prepare a business case for a decision
  4. 04Show uncertainty around projected benefits

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/nimrodfisher/data-analytics-skills --skill impact-quantification

After installing, type/impact-quantificationin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

assets/
business_case_template.md
impact_estimate_template.md
references/
assumption_documentation.md
impact_quantification_framework.md
scripts/
confidence_interval.py
cost_savings.py
revenue_impact.py

Details

From the project README

When to use

After an analytical finding surfaces a potential action, change, or opportunity. Use to produce a defensible numeric estimate that stakeholders can act on. Also use when prioritizing a backlog of initiatives — quantified impact is the primary ranking signal.

Process

  1. Classify the impact type — revenue growth, cost reduction, risk reduction, or efficiency gain. Each type has a different formula family (see references/impact_quantification_framework.md).
  2. Gather inputs — collect baseline metrics, affected population size, expected lift/reduction, time horizon, and confidence level.
  3. Build the point estimate — use scripts/revenue_impact.py for revenue/growth scenarios or scripts/cost_savings.py for cost/efficiency scenarios.
  4. Add uncertainty bounds — use scripts/confidence_interval.py to produce low/base/high estimates. Never deliver a single number without a range.
  5. Document assumptions — fill in references/assumption_documentation.md for every input that is estimated rather than directly measured; note the sensitivity of the output to each.
  6. Package the estimate — complete assets/impact_estimate_template.md with the range, assumptions, confidence, and recommended action; optionally build the full assets/business_case_template.md for larger decisions.

Inputs the skill needs

  • Baseline metric value (current state)
  • Affected population or volume
  • Expected change (lift %, absolute, or rate change)
  • Time horizon (monthly / annual)
  • Confidence level in inputs (high / medium / low)

Output

  • Impact estimate with low/base/high range
  • Assumption log (source and sensitivity for each input)
  • Completed impact_estimate_template.md or business_case_template.md

Comments

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