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Found 307 Skills
Extract edge hints from daily market observations and news reactions, with optional LLM ideation, and output canonical hints.yaml for downstream concept synthesis and auto detection.
Is wallet B copying wallet A? Are these two wallets from the same entity? Uses profiler compare for shared counterparties and tokens, plus labels and PnL for identity.
Decompose Return on Equity into component ratios to identify performance drivers. Use for financial analysis, performance benchmarking, and identifying improvement opportunities.
Financial time series analysis method toolkit. Covers stocks / commodity futures / cryptocurrencies / ETFs / foreign exchange / indices, full process from data acquisition to high-level analysis. Built-in 70+ analysis methods, covering 8 major method domains: time series testing, predictive modeling, cross-asset relationships, volatility risk, portfolio optimization, state recognition, commodity-specific analysis and network analysis. Tushare MCP tool (A shares/Hong Kong stocks/US stocks/futures/funds/macro) is preferred for data acquisition, and yfinance scripts are used to supplement assets not covered by tushare such as commodity futures (CL=F) and crypto (BTC-USD).
Generate clear, accurate performance reports for investment portfolios with benchmarks, attribution, and risk dashboards. Use when the user asks about portfolio performance reports, return summaries, benchmark comparison, risk dashboards, goal progress tracking, or GIPS-compliant reporting. Also trigger when users mention 'quarterly report', 'how did my portfolio do', 'time-weighted vs money-weighted return', 'annualized returns', 'net-of-fee performance', 'rolling Sharpe', or ask how to present investment results to clients.
Guides the user through spatial enrichment workflows — triggered by requests to enrich, add demographics, estimate population around locations, compute spatial features, sociodemographic analysis, "what's around" queries, buffer/isochrone + join patterns, or trade area enrichment.
Pull Bigdata.com (RavenPack) financial and news data through the official `bigdata-client` SDK and its public `/v1/*` REST endpoints when the Bigdata MCP server returns only pre-synthesized tearsheets but you need the machine-readable substrate underneath. MCP search returns prose chunks (text + relevance only — no per-chunk sentiment, no entity spans); its tearsheets give only aggregate values, not computable time series or per-field JSON. This skill bundles a verified, cost-guarded toolkit over the official REST API: annotated chunk search, entity/ISIN resolution, analyst estimates, calendar/surprise/ ratings/targets, financial statements, TTM metrics & ratios, prices, dividends, revenue segments, a daily entity-sentiment series, co-mention graph, screener, and batch search. Use it whenever the user mentions Bigdata.com, RavenPack, a `bd_v2_` key, the bigdata MCP, rp_entity_id, chunk/query_unit cost, or wants structured financials, fundamentals, prices, sentiment, or annotated news.
Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.
Comprehensive skill for Microsoft GraphRAG - modular graph-based RAG system for reasoning over private datasets
Patterns for building robust, reproducible genomics analysis pipelines. Covers workflow managers, NGS data processing, variant calling, RNA-seq, and common bioinformatics pitfalls. Use when ", " mentioned.
Parse raw text from an Instagram or TikTok Story insights screenshot and format it into a clean, spreadsheet-ready row with labeled fields. This skill should be used when parsing Story metrics from a screenshot, formatting Story insights for a spreadsheet, extracting metrics from a pasted Story screenshot, cleaning up Story analytics data, converting Story insights text into structured data, turning a Story performance screenshot into a row for the tracker, logging Story metrics into a spreadsheet, normalizing Story screenshot data, pulling numbers from a Story insights paste, organizing Story metrics from creator screenshots, processing a batch of Story screenshots into rows, building a Story metrics tracker from screenshots, or entering Story data from a screenshot into a sheet. For normalizing metrics from multiple sources into a unified table, see metrics-normalization-formatter. For calculating engagement rates and comparing to benchmarks, see engagement-rate-calculator-benchmarker.
Best practices for developing advanced, interactive, and publication-quality data visualizations using HoloViz HoloViews