Results for “class-analysis”

Reviews code through the lens of 12 classic software engineering books, catching design smells, coupling issues, and architectural risks that linters miss.

Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features and classifying samples with machine learning models.

Analyzes why a piece of content resonates with audiences using five classic communication theories, identifies the emotional core and effective stance, and outputs discussion directions for a chatroom.

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.

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.

Grades individual test methods and produces a compact markdown table with a letter grade, score band, and one-line note for each test.

Provides file paths to language-specific reference files for polyglot test analysis skills, enabling framework-aware detection of test markers, assertions, and patterns across .NET, Python, TypeScript, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, and C++.

Analyze closed deals and lost opportunities for patterns

Classify reply sentiment and determine next action

Perform a detailed SWOT analysis — strengths, weaknesses, opportunities, and threats with actionable recommendations. Use when doing strategic assessment, competitive analysis, or evaluating a product or business position.

Refactoring Analyst

Calculus Based Analysis Skill

Big Data Based Analysis Skill

Turn churned and downgraded accounts into an early-warning system. Reads the customers who left or shrank, extracts the themes behind why, builds a warning checklist from the real causes, then reads your active book and flags which accounts look like the ones that just churned. Built for B2B retention teams, customizable to your CRM and your customer data. Trigger on "why do customers churn", "what do churned accounts have in common", "build me a churn early-warning list", "which accounts are at risk of churning", "who looks like a recent churner", or any retention post-mortem.

Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md. Identifying inconsistencies, duplications, ambiguities, and underspecified items.

Post-execution skill crystallization and learning engine updates for WinDAGs. Runs after successful execution to update Thompson sampling parameters, track method quality, detect monster-barring, log near-miss events, and signal Kuhnian crises. Activate on "curator", "learning update", "skill crystallization", "Thompson sampling", "monster-barring", "near-miss", "Kuhnian crisis", "post-execution learning". NOT for pre-execution risk scanning (use windags-premortem), retrospective analysis (use windags-looking-back), or DAG construction (use windags-architect).

Diagnoses structural problems in an existing test suite—brittleness, mock overuse, and other decay risks—drawing on twelve classic engineering books including xUnit Test Patterns and Working Effectively with Legacy Code.

Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"

Multi-perspective dialectical reasoning with cross-evaluative synthesis. Spawns parallel evaluative lenses (STRUCTURAL, EVIDENTIAL, SCOPE, ADVERSARIAL, PRAGMATIC) that critique thesis AND critique each other's critiques, producing N-squared evaluation matrix before recursive aggregation. Triggers on /critique, /dialectic, /crosseval, requests for thorough analysis, stress-testing arguments, or finding weaknesses. Implements Hegelian refinement enhanced with interleaved multi-domain evaluation and convergent synthesis.

分析故事人物关系，识别类型、特点及发展变化。适用于深度理解人物关系网络、分析关系对情节推动作用、为情节设计提供关系支撑

Refactors a class toward the Single Responsibility Principle by identifying responsibilities, grouping cohesive behavior, and extracting focused classes.

Conduct gap analyses between current and desired states. TRIGGERS - Use when user needs help with gap-analysis related tasks.

Expected error decomposes into bias, variance, and irreducible noise.

Conduct gap analyses between current and desired states. TRIGGERS - Use when user needs help with gap-analysis related tasks.

Query JASPAR for transcription factor binding site (TFBS) profiles (PWMs/PFMs), searching by TF name, species, or class, scanning DNA sequences for binding sites, and comparing matrices. Use when doing motif analysis, regulatory genomics, transcription factor binding prediction, or interpreting regulatory/non-coding GWAS variants. Part of the AlterLab Academic Skills suite.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.