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Generate Excel .xlsx workbooks — sheets, styled tables, live formulas, and charts — with openpyxl (formulas/editing), XlsxWriter (fast/large/streamed), or pandas.to_excel (quick DataFrame dumps). Use when the user wants a spreadsheet report, financial model, or data export as a real .xlsx file.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | xlsx-generation |
| description | Generate Excel .xlsx workbooks — sheets, styled tables, live formulas, and charts — with openpyxl (formulas/editing), XlsxWriter (fast/large/streamed), or pandas.to_excel (quick DataFrame dumps). Use when the user wants a spreadsheet report, financial model, or data export as a real .xlsx file. |
| origin | harness |
| workloads | ["writing","python-data"] |
Produce spreadsheets that stay dynamic — real formulas, correct values, zero errors.
| Tool | Use when | Cannot |
|---|---|---|
| openpyxl | Need formulas, formatting, or to edit an existing workbook | Slower/more memory; writes formula strings without their computed values |
| XlsxWriter | Brand-new large report; richest charts; stream huge data with constant_memory=True | Write-only — cannot read/modify existing files |
| pandas.to_excel | Quick tabular dump; multi-sheet via ExcelWriter | Thin styling; delegates to openpyxl/xlsxwriter |
Choose: edit existing / formulas back → openpyxl; large/streamed/charts → XlsxWriter; DataFrame → pandas.
from openpyxl import Workbook
from openpyxl.styles import Font
wb = Workbook()
ws = wb.active
ws["A1"] = "Report"; ws["A1"].font = Font(bold=True, size=14)
ws.append(["Name", "Score"])
ws.append(["Alice", 90])
ws.append(["Bob", 85])
ws["B5"] = "=SUM(B3:B4)" # a real formula, NOT a Python-computed total
wb.save("report.xlsx")
ws["B5"] = "=SUM(B3:B4)", not ws["B5"] = 175. This keeps the sheet dynamic when the user edits inputs.soffice --headless --convert-to xlsx --calc or a recalc script) and scan for #REF!, #DIV/0!, #VALUE!, #NAME?.data_only=True + save destroys formulas — that mode reads cached values only; never save a workbook loaded that way.constant_memory=True streams row-by-row; openpyxl write_only=True mode for big writes.from openpyxl.chart import BarChart, Reference → build Reference ranges → ws.add_chart(chart, "E2").wb.create_sheet("Summary"); with pandas use one ExcelWriter and multiple df.to_excel(writer, sheet_name=...).Create professional architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as standalone HTML files with SVG graphics, a built-in dark/light theme toggle, and one-click export to PNG / JPEG / WebP / SVG. Accepts plain-language descriptions or pasted Mermaid code (flowchart, sequenceDiagram, stateDiagram) and lays the diagram out from scratch in archify style. Use when the user asks for system architecture diagrams, infrastructure diagrams, cloud architecture visualizations, security diagrams, network topology, technical workflows, approval flows, runbooks, CI/CD flows, process diagrams, API call sequences, request lifecycles, data pipelines, ETL/ELT maps, PII boundaries, data lineage, state machines, lifecycle diagrams, status transitions, or asks to convert/beautify a Mermaid diagram.
Data analysis methodology — how to frame a question, pick the right technique, avoid statistical traps, and connect results to decisions. NOT a tool tutorial (pandas/polars/duckdb live in python-data-analysis) — this is the judgment layer: problem framing, analysis-type decision tree, experiment/causal design, and domain playbooks (churn, cohort, funnel, anomaly). Trigger: "왜 늘었/줄었지", 이탈 분석, 코호트, 퍼널, A/B 테스트, 인과추론, 상관 vs 인과, 유의성, 세그먼트, 이상 탐지, "이 데이터로 뭘 봐야", 지표 설계, exploratory analysis.
AWS cost management and FinOps practice — Cost Explorer, Budgets, Cost Anomaly Detection, CUR/data exports, cost allocation tags, Cost Categories, Savings Plans vs Reserved Instances, Compute Optimizer / Cost Optimization Hub, rightsizing, unit economics, showback/chargeback. Grounded in the FinOps Foundation Framework (Inform / Optimize / Operate). Trigger: cost anomaly, Savings Plan, Reserved Instance, rightsizing, cost allocation tag, chargeback, showback, unit cost, budget alert, CUR, Cost Explorer, unblended/amortized cost, RI coverage, commitment, "왜 청구서가 늘었지".
Track and report AI model token usage, spending, and budgets from a local cost-tracking database.
Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.
Conduct market research, competitive analysis, investor due diligence, and industry intelligence with source attribution and decision-oriented summaries. Use when the user wants market sizing, competitor comparisons, fund research, technology scans, or research that informs business decisions.