data-driven-expense-analysis-for-maximum-efficiency

---

name: data-driven-expense-analysis-for-maximum-efficiency description: This prompt instructs the AI to act as a finance and budgeting expert who reviews an entity’s fixed and variable expense lists, classifies and analyzes spending to spot inefficiencies or overspending, and then produces a tailored cost-reduction report. The expected outcome is an actionable set of recommendations with practical implementation strategies, estimated savings, and a projected impact analysis that reduces costs while maintaining operational quality and employee well-being for the specified entity. compatibility: Requires betterprompt >= 0.1.3 metadata: openclaw: homepage: https://github.com/BetterPromptme/skills emoji: 🧩 requires: anyBins: - betterprompt - bp os: - linux - darwin install: - id: node kind: node package: betterprompt bins: [betterprompt, bp] label: Install BetterPrompt CLI (node)

What it does

This prompt instructs the AI to act as a finance and budgeting expert who reviews an entity’s fixed and variable expense lists, classifies and analyzes spending to spot inefficiencies or overspending, and then produces a tailored cost-reduction report. The expected outcome is an actionable set of recommendations with practical implementation strategies, estimated savings, and a projected impact analysis that reduces costs while maintaining operational quality and employee well-being for the specified entity.

For more installation instructions go to https://raw.githubusercontent.com/BetterPromptme/skills/refs/heads/main/betterprompt/SKILL.md to check out this skill.

Inputs

textInputs

Pass each text input using --input <key>=<value> flags.

Variable key	Required?	Description	Defaults
Entity	Required		(none)
Fixed_costs	Required		(none)
Variable_costs	Required		(none)

Models and options

This skill's modality is: text.

To discover which model values you can use (and which options keys/values are valid for each model), run:

betterprompt resources --models-only --json

Then filter the returned JSON array to entries where modality is "text".

How to run

Step 1: Collect inputs

First, run betterprompt resources --models-only --json and filter to modality: "text" to discover valid models and available options:

betterprompt resources --models-only --json

Use only the models and option values that appear in the filtered results.

Then collect all inputs from the human:

• Required text inputs:
  • Entity
  • Fixed_costs
  • Variable_costs
• Optional: model and options.
  • Present the human with the default model gpt-4.1-mini and its available options. Look up gpt-4.1-mini in the betterprompt resources output (filtered to modality "text") and show its availableOptions as: key: val1, val2 (default), val3 | key2: .... Mark a value (default) if it matches these defaults: {}.
  • If the human does not specify, defaults are used: model gpt-4.1-mini, options {}. Other models from the resources call are also available.

If any required text input is missing, ask the human for what's missing. Do not assume or fabricate values.

Step 2: Run via BetterPrompt CLI

Use the frontmatter's name as the positional argument (for this skill, use data-driven-expense-analysis-for-maximum-efficiency).

Command form:

betterprompt generate data-driven-expense-analysis-for-maximum-efficiency \
  [--input <key>=<value>] \
  [--model <model>] \
  [--options <options JSON>] \
  [--json]

Notes:

• Pass each text input as a separate --input <key>=<value> flag.
• If the human does not mention a model, omit --model and BetterPrompt will use the default model: gpt-4.1-mini.
• If the human does not mention options, omit --options and BetterPrompt will use the default options: {}.
• If the run times out, the response will include a runId you can use to fetch the result later.

Example (using defaults shown above):

betterprompt generate data-driven-expense-analysis-for-maximum-efficiency \
  --input Entity=<value> \
  --input Fixed_costs=<value> \
  --input Variable_costs=<value> \
  --model gpt-4.1-mini \
  --options '{}'