# Spend Awareness > Summarize pasted transaction data by category in plain language to surface spending patterns — descriptive only, does not provide investment advice, budgeting recommendations, or savings plans - **Type:** Skill - **Install:** `agentstack add skill-jmlozano1990-cowork-starter-kit-spend-awareness` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [jmlozano1990](https://agentstack.voostack.com/s/jmlozano1990) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [jmlozano1990](https://github.com/jmlozano1990) - **Source:** https://github.com/jmlozano1990/Cowork-Starter-Kit/tree/main/skills/spend-awareness - **Website:** https://github.com/jmlozano1990/Cowork-Starter-Kit/releases/latest ## Install ```sh agentstack add skill-jmlozano1990-cowork-starter-kit-spend-awareness ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About ## When to use Use spend-awareness when the user pastes transaction data, bank exports, credit card statements, or a list of recent purchases and wants to understand where their money went. This skill produces a plain-language category summary — descriptive only. It surfaces patterns in what the user has already spent; it does not evaluate those patterns, recommend changes, or provide financial guidance of any kind. Use it when the user wants a clear picture of their spending, not advice about it. ## Triggers - User says "categorize my spending", "where did my money go", or "summarize transactions" — direct invocation. - User pastes a list of transactions, a bank export, or a statement and asks for a breakdown. - User asks which categories they spent the most on with transaction data attached. - User wants a plain-language summary of their purchases without financial advice. ## Instructions 1. **Read the full transaction data before categorizing.** Do NOT categorize on first pass — read all entries, then group. This prevents misclassification at the boundaries (e.g., a grocery order that includes household items). 2. **Assign each transaction to a category.** Use common-sense categories: Food & Dining, Transport, Subscriptions, Shopping, Health, Entertainment, Utilities, Services, and Other (for anything that doesn't fit). Apply categories consistently — do NOT invent new categories for one-off items; use Other instead. 3. **For each category, produce: category name, total amount, and transaction count.** Present as a plain-language bullet list. State totals in the same currency as the input. If multiple currencies are present, note it and do NOT convert. 4. **Surface patterns with neutral descriptive language only.** It is appropriate to note "Food & Dining is your largest category this period" or "Subscriptions accounts for 22% of total spend." It is NOT appropriate to evaluate whether this is good or bad, to compare to benchmarks, or to suggest changes. 5. **If the user asks for advice, redirect neutrally.** Use this exact phrase: "for planning, consider a financial advisor." Do NOT rephrase this as a suggestion to cut spending, to reconsider categories, or to set goals. 6. **Do not infer financial goals.** The user's spending pattern is data, not evidence of their priorities. Do NOT write sentences like "it looks like travel is a priority for you" or "you seem to be investing in your health." Describe the data; do not interpret it. ## Output format Plain language bullet list in the chat. Format per category: `- [Category]: $[total] ([N] transactions)`. After the category list, add one summary line: total spend across all categories and the date range if determinable from the data. No JSON, no YAML, no Obsidian wikilinks. Output is portable — usable in a notes app, spreadsheet, or plain text. ## Quality criteria 1. All transactions are categorized — nothing is silently dropped. 2. Category totals and counts are accurate — verifiable against the source data. 3. Language is descriptive only — no recommendations, no benchmarks, no evaluations. 4. If the user requests financial guidance, the skill redirects with the exact phrase: "for planning, consider a financial advisor." 5. No financial goals are inferred from the spending pattern. ## Anti-patterns - **Recommending investments** — any suggestion about where to put money, including low-risk or saving-focused suggestions, is investment advice and is outside this skill's scope. Do not provide investment advice. - **Suggesting budgeting changes** — "you might consider reducing your dining spend" or "this category seems high" are budgeting recommendations. This skill describes; it does not recommend. Do not make budgeting recommendations. - **Proposing savings plans** — "setting aside 10% from this category" or "if you cut subscriptions, you could save $X" are savings plans. This skill does not propose savings plans. - **Inferring financial goals** — writing sentences that imply the user has a goal (saving, investing, cutting costs) based on their spending data. The data shows what happened; it does not reveal intent. - **Moralizing spending categories** — labeling any category as excessive, unnecessary, indulgent, or problematic. Spending data is neutral. For any questions about planning, the appropriate redirect is: "for planning, consider a financial advisor." ## Example **Input (pasted transactions — partial):** ``` 2026-04-01 Whole Foods $87.43 2026-04-02 Uber $12.50 2026-04-03 Netflix $15.99 2026-04-04 Whole Foods $54.10 2026-04-05 Spotify $9.99 2026-04-06 Pharmacy $23.40 2026-04-07 Amazon $67.00 2026-04-08 Gas Station $48.00 ``` **Output:** - Food & Dining: $141.53 (2 transactions) - Transport: $60.50 (2 transactions) - Subscriptions: $25.98 (2 transactions) - Health: $23.40 (1 transaction) - Shopping: $67.00 (1 transaction) Total: $318.41 across 8 transactions (April 1–8, 2026). Food & Dining is the largest category this period, accounting for 44% of total spend. ## Writing-profile integration Spend-awareness is a structured data-summary skill — category labels, totals, and counts are data fields and profile-neutral. The summary observation line ("Food & Dining is the largest category") is the only prose element where register applies. Consult `context/writing-profile.md` for tone on this line only: a formal profile produces "Food & Dining represents the largest expenditure category"; a conversational profile produces "Food & Dining is where most of the spending went." The redirect phrase "for planning, consider a financial advisor" is always verbatim, never adapted. ## Example prompts - "Categorize my spending from these transactions: [paste]." - "Where did my money go last month? [paste statement]." - "Summarize this spending data by category: [paste]." - "What categories make up my recent purchases? [paste]." ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [jmlozano1990](https://github.com/jmlozano1990) - **Source:** [jmlozano1990/Cowork-Starter-Kit](https://github.com/jmlozano1990/Cowork-Starter-Kit) - **License:** MIT - **Homepage:** https://github.com/jmlozano1990/Cowork-Starter-Kit/releases/latest Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-jmlozano1990-cowork-starter-kit-spend-awareness - Seller: https://agentstack.voostack.com/s/jmlozano1990 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.