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Found 149 Skills
Parse, analyze, and process SARIF (Static Analysis Results Interchange Format) files. Use when reading security scan results, aggregating findings from multiple tools, deduplicating alerts, extracting specific vulnerabilities, or integrating SARIF data into CI/CD pipelines.
Use when building Apache Spark applications, distributed data processing pipelines, or optimizing big data workloads. Invoke for DataFrame API, Spark SQL, RDD operations, performance tuning, streaming analytics.
This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.
Run regression analyses in Stata with publication-ready output tables.
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
Retrieves chemical compound information from PubChem and ChEMBL with disambiguation, cross-referencing, and quality assessment. Creates comprehensive compound profiles with identifiers, properties, bioactivity, and drug information. Use when users need chemical data, drug information, or mention PubChem CID, ChEMBL ID, SMILES, InChI, or compound names.
Perform technical analysis on stock K-line data, calculate indicators such as MA/MACD/RSI, and judge trends and trading signals. Trigger scenarios: (1) "Analyze the technical aspects of Moutai" (2) "Check if this stock is buyable" (3) "Technical analysis 600519" (4) Used when needing to judge stock trends and trading points. Need to use data-collect to obtain data first
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "replication lag", "partitioning strategy", "consistency vs availability", or "stream processing". Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.
Create a custom technical indicator using Numba JIT + NumPy. Generates production-grade, O(n) optimized indicator functions with charting and benchmarking.
Set up real-time indicator computation on live WebSocket market data. Streams LTP/Quote/Depth and computes indicators in real-time with optional Plotly live charting.
Generate comprehensive stock analysis report (PDF or markdown) with trend, PMCC, and fundamental analysis
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.