Personal budgeting and financial planning skill. Use when: (1) Analyzing spending patterns by category or time period, (2) Comparing budget vs actual spending, (3) Calculating savings rates, (4) Forecasting cash flow, (5) Planning tax-aware financial decisions. Tools: actual-mcp for budget/transaction data, ghostfolio-mcp for investment portfolio context. --- # Personal Budgeting ## Tool Mapping | Task | MCP Server | Key Tools | |------|-----------|-----------| | Transaction history, balances, budgets | actual-budget | `transaction(operation="list")`, `account(operation="list")`, `budget(operation="months"|"month")` | | Category breakdowns | actual-budget | `analytics(operation="spending_by_category")`, `category(operation="groups_list")` | | Investment balances and allocation | ghostfolio-mcp | `get_portfolio_summary`, `get_portfolio_positions` | | Net worth calculation | Both | Actual (cash/debt) + Ghostfolio (investments) | ## Spending Analysis ### Category Breakdown 1. Pull transactions for the target period using `transaction(operation="list")` with date range filters 2. Group by category — report both absolute amounts and percentage of total spend 3. Flag categories that exceed their budget allocation 4. Present results as a ranked table: Category | Budgeted | Actual | Variance | % of Total ### Month-over-Month Trends 1. Pull 3-6 months of transaction data 2. Compute per-category monthly totals 3. Calculate month-over-month change (absolute and percentage) 4. Flag categories with sustained increases (3+ consecutive months of growth) 5. Distinguish between recurring/fixed expenses (rent, insurance, subscriptions) and variable expenses (groceries, dining, entertainment) ### Anomaly Detection - Flag individual transactions > 2x the category's average transaction size - Flag categories where current month spend exceeds the trailing 3-month average by > 25% - Flag new payees not seen in prior months (potential new subscriptions) ## Budget vs Actual Variance Analysi
Run a competitive analysis — deep dive, landscape, synthesis, or monitoring.
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Discover, query, and analyze Israeli government open data from data.gov.il (CKAN API). Use when user asks about Israeli government data, "data.gov.il", government datasets, CBS statistics, or needs data about Israeli transportation, education, health, geography, economy, or environment. Supports dataset search, tabular data queries, and analysis guidance. Enhances existing datagov-mcp and data-gov-il-mcp servers with workflow best practices. Do NOT use for classified government data or data requiring security clearance.
Professional data analysis — read CSV/JSON/Excel, summary statistics, outliers, ASCII charts, insights. Use when the user needs to analyze data files or validate pipeline data.
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Alarm operations skill. List alerts, analyze alert rule context with datasource-enriched resource facts, and perform alert status operations with remediation guidance.
Fetch, scrape, or download football data from any source. Also handles API key setup and credential management. Use when the user wants to get data from StatsBomb, Opta, FBref, Understat, SportMonks, Wyscout, Kaggle, or any football data source. Also use when they ask about API keys, authentication, setting up access to a provider, or what data is available free vs paid.
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Complete toolkit for web crawling and data extraction using Crawl4AI. This skill should be used when users need to scrape websites, extract structured data, handle JavaScript-heavy pages, crawl multiple URLs, or build automated web data pipelines. Includes optimized extraction patterns with schema generation for efficient, LLM-free extraction.
PRP (Product Requirement Prompt) methodology for writing PRDs. Reference for best practices in structuring requirements documents for coding agents. --- # PRP Methodology — Quick Reference The PRP (Product Requirement Prompt) framework is a structured process for creating PRDs that coding agents can execute in a single pass. ## Core Principle A PRD must contain ALL context needed for implementation. If a fresh Claude session with only the PRD can't build the feature correctly, the PRD is incomplete. ## The 3-Step Process 1. **Write initial description** — Brain dump what you want: feature, tech stack, constraints, integrations, examples, documentation references 2. **Generate the PRD** — Research the codebase + web, interview the user, produce a structured document following the base template 3. **Execute the PRD** — Clear context, start fresh, implement from the PRD alone ## What Makes a Good PRD **DO:** - Reference specific files and code patterns from the codebase - Write testable validation criteria ("returns 401 on invalid token") - Include explicit non-goals to prevent scope creep - List anti-patterns specific to the project - Order implementation steps by dependency (what must exist before what) - Include migration strategy for existing data/behavior **DON'T:** - Use vague validation criteria ("works well", "is performant") - Leave technical design abstract ("use appropriate data structures") - Assume the implementing agent knows project conventions — spell them out - Skip the non-goals section — agents will over-build without boundaries - Write steps that can't be verified independently ## Interview Technique The most valuable part of PRD generation is the interview. Goal: reduce assumptions to near zero. - Ask at least 8-10 questions before writing - Batch questions in groups of 3-4 - Provide recommended answers based on codebase research - Cover: scope, users, technical constraints, data model, compatibility, edge cases, testing, anti-patterns - Final ques
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Build ServiceNow applications using the ServiceNow SDK and Fluent API (TypeScript). Use this skill whenever the user asks to create, scaffold, or generate ServiceNow apps, tables, flows, business rules, script includes, ACLs, or any Fluent-based metadata.
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学术论文搜索与分析服务 (Academic paper search & analysis)。当用户涉及以下学术场景时，必须使用本 skill 而非 web-search：搜索论文、查找 ArXiv/PubMed/PapersWithCode 论文、查询 SOTA 榜单与 benchmark 结果、引用分析、生成论文解读博客、查找论文相关 GitHub 仓库、获取热门论文推荐。Keywords: arxiv, paper, papers, academic, scholar, research, 论文, 学术, 搜索论文, 找论文, SOTA, benchmark, MMLU, citation, 引用, 博客, blog, PapersWithCode, HuggingFace.
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IDA Pro reverse-engineering skill for Codex, Claude Code, and OpenCode. Use when a user needs live IDA or Hex-Rays analysis through the local ida-pro-skill CLI and installed IDA bridge, especially for instance discovery, metadata, cursor or selection context, entrypoints, functions, callers, imports, strings, xrefs, pseudocode, globals, structs, renames, comments, byte patches, function creation, or explicit IDAPython, including WSL-to-Windows IDA setups.
Assesses whether a business question can be answered with data available in a Bauplan lakehouse. Maps business concepts to tables and columns, checks data quality on the relevant subset, validates semantic fit, and renders a verdict: answerable, partially answerable, or not answerable. Produces a structured feasibility report. Use when a user brings a business question, asks 'can we answer this', wants to know if the data supports an analysis, or before building a one-off analysis or pipeline.
