This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Categorize invoices and receipts by vendor, expense, and tax bucket, detect duplicates, and produce a tax-ready monthly summary. Use during bookkeeping, tax prep, or expense reimbursement.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Bulk-categorize a CSV of invoices or receipts, detect duplicates, and produce a tax-ready monthly summary.
invoice, invoices, receipt, receipts, expense, expenses, bookkeeping, accounting, tax, tax prep, categorization, vendor, reimbursement, monthly summary
Before categorizing, confirm these inputs. If any is unknown or vague, ASK — do not assume:
category_rules.json and cut the uncategorized bucketStop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
date,vendor,description,amount,currencypython scripts/invoice_categorizer.py receipts.csv
Goal: Convert a month of unstructured receipts into a categorized, tax-ready summary in under 10 minutes.
Steps:
python scripts/invoice_categorizer.py receipts.csvassets/category_rules.json for any recurring vendorsassets/monthly_summary_template.mdExpected Output: Categorized expense list + monthly totals by category + duplicate-suspect list.
Time Estimate: 10 minutes/month after initial rules are seeded.
Goal: Catch double-entered receipts before they reach the books.
Steps:
python scripts/invoice_categorizer.py receipts.csv --jsonduplicates_suspected listExpected Output: Cleaned CSV with no duplicate rows.
Time Estimate: 2-3 minutes per month.
Goal: Find spend creep — vendors whose monthly total grew significantly without you noticing.
Steps:
Expected Output: Vendor-spend trend list with flagged growth.
Time Estimate: 15 minutes per quarter.
Reads a CSV of receipts/invoices and:
assets/category_rules.json (extensible)# Human-readable summary
python scripts/invoice_categorizer.py receipts.csv
# JSON for programmatic use
python scripts/invoice_categorizer.py receipts.csv --json
# Use a custom rules file
python scripts/invoice_categorizer.py receipts.csv --rules my-rules.json
Expected CSV columns: date, vendor, description, amount (currency optional)
Date formats accepted: YYYY-MM-DD, MM/DD/YYYY, DD/MM/YYYY
references/expense_categorization_guide.md — Standard expense categories, common tax buckets (US Schedule C, UK self-employment, generic), how to map vendors to categoriesassets/category_rules.json — Default rules; extend with your recurring vendorsassets/monthly_summary_template.md — Format for handing the monthly summary to an accountantfinance/ skills for budgeting and forecastingc-level-advisor/cs-cfo-advisor cash-flow workflowsまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
日本語の概要は準備中です。原文の説明を表示しています。
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
日本語の概要は準備中です。原文の説明を表示しています。
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
日本語の概要は準備中です。原文の説明を表示しています。