Results for “class-analysis”

Scans US equities for daily +20%/-20% movers, classifies catalysts and chart context, updates forward outcomes, and summarizes cohort patterns for research.

Analyzes .NET project code coverage and CRAP (Change Risk Anti-Patterns) scores to identify risk hotspots, methods blocking coverage gains, and prioritize where to add tests.

Executes malware samples in Cuckoo Sandbox to observe runtime behavior including process creation, file system modifications, registry changes, network communications, and API calls. Generates comprehensive behavioral reports for malware classification and IOC extraction.

Inspect, profile, clean, reconcile, analyze, visualize, and verify spreadsheet data while preserving formulas, formatting, types, and source files. Use when working with .xlsx, .xlsm, .xls, .ods, .csv, or .tsv files; answering questions from a workbook; auditing formulas or data quality; comparing sheets or versions; producing pivots, charts, forecasts, or summary workbooks; repairing malformed tables; or validating that spreadsheet edits and calculations are accurate.

Discovers competitors via search API, deeply researches each with a multi-lane pattern, and compiles an HTML report with overview, per-competitor deep dives, feature/pricing matrix, and mentions feed.

Guides data analysis, EDA, and ML tasks by teaching, diagnosing MCPs, and deciding with the user, offering multiple options and documenting decisions.

Guide through a structured data analysis workflow: define the question, validate data quality, select the appropriate analytical method, and produce decision-ready findings with caveats.

Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.

Analyze datasets to answer defined questions through statistical methods, trend identification, hypothesis testing, and correlation analysis. Use when the user needs evidence-backed findings or decisions from data; use exploratory-data-analysis instead for open-ended first-pass profiling before questions are defined.

Grades and classifies evaluation batch results, applying exclusions, diagnosing failure modes, computing pass rates, and generating summary tables for papers.

Rapidly classify malware samples against known family signatures using YARA rules, covering rule writing, scanning, and integration with analysis pipelines.

Provides a structured framework for analyzing cyber intrusions by examining four core features: Adversary, Capability, Infrastructure, and Victim. Covers implementing the Diamond Model programmatically to classify and correlate intrusion events, build activity threads, and generate pivot-ready intelligence.

Analyzes the volatility smile and skew for a single options expiration, providing implied volatility curves, skew metrics, and shape classification to reveal market pricing of tail risk and directional fear.

Detect IBD-style Distribution Days for QQQ/SPY, track 25-session expiration and 5% invalidation, count d5/d15/d25 clusters, classify market risk, and emit TQQQ/QQQ exposure recommendations.

Ranks weekly token unlocks by Absorption Ratio (unlock value / 7d avg volume) instead of supply percentage, with per-event cliff vs linear classification, recipient category, and a one-line market read.