Generate a Project+ style budget workbook with line items and monthly burn.
npx skills add https://github.com/kenmulford/PMSkills --skill budget-plan
Please help me install this Agent Skill. Skill: budget-plan Source: https://github.com/kenmulford/PMSkills/tree/main/skills/budget-plan Command: npx skills add https://github.com/kenmulford/PMSkills --skill budget-plan
Creating and validating project budgets is error-prone and time-consuming when stakeholders rely on qualitative estimates. This skill enforces hard guardrails and produces a reproducible .xlsx budget with explicit TBD placeholders where data is missing.
Describe the project situation in plain language to generate a baseline budget workbook using the budget-plan skill.
High-intent search queries and answers about installing and using this skill.
You generate a project budget xlsx by providing plain language inputs that the skill parses into a structured JSON schema, applying strict guardrails like no invented costs and explicit TBD placeholders for missing data. It renders a formatted workbook with line items, monthly burn, and variance.
The best way to handle TBD fields and fuzzy amounts in cost estimation is to preserve them explicitly without guessing. The skill flags integrity issues and inserts TBD placeholders, ensuring no reconciling plug lines or invented costs compromise the budget fidelity.
Yes, rendering the formatted budget workbook requires the openpyxl dependency. The skill uses openpyxl to apply fidelity checks to parsed JSON data and output the final xlsx file containing line items, categories, owner status, and variance.
Yes, you can use plain language to create a baseline project budget for a migration. The skill parses your initial descriptions, applies strict fidelity checks against unknown costs, and renders a sponsor-ready xlsx document with known line items and approved totals.
Your project budget workbook includes explicit TBD placeholders because the tool enforces hard guardrails against unknown costs. It never invents costs or uses plug lines, ensuring data integrity by flagging fuzzy amounts and missing fields rather than reconciling them with guesses.
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