Results for “sui”

Identify and test open redirect vulnerabilities in web applications by analyzing URL redirection parameters, bypass techniques, and exploitation chains for phishing and token theft.

Provides exact payloads and bypass techniques for every PortSwigger DOM-based vulnerability lab variant, including zero-day extensions and blue team detection strategies.

Spark by Lightspark: statechain-derived L2 with FROST threshold signatures, leaf architecture, Lightning compatibility. Beta launched April 2025; managed by Spark Operators (SOs). USE WHEN: integrating with Spark, evaluating Spark vs Ark vs Lightning, designing leaf-based UTXO trees.

Liquid Network: federated sidechain by Blockstream. Confidential Transactions, asset issuance (LBTC, USDt-Liquid, others), 2-min blocks, n-of-m federation peg-out. Elements codebase. USE WHEN: building on Liquid, peg-in/peg-out integrations, designing CT-based privacy.

Test web applications for HTTP Host header injection vulnerabilities to identify password reset poisoning, web cache poisoning, SSRF, and virtual host routing manipulation risks.

Parse and analyze Windows forensic artifacts including MFT, registry hives, prefetch files, event logs, LNK files, and jump lists using Eric Zimmerman's EZ Tools suite and KAPE.

Enforces a mandatory pre-commit/pre-PR testing gate: detects the test stack, runs the full suite, fixes failures, creates minimal tests when none exist, and verifies coverage before allowing commits.

Drafts professional legal letters for personal injury litigation including settlement demands, cease-and-desist, notice letters, and response correspondence. Tailors tone by recipient type. Use when drafting demand letters, pre-suit correspondence, insurance claim letters, cease-and-desist notices, or litigation response letters.

Generates a complete mobile test suite for Flutter, React Native, iOS, or Android apps, covering unit, widget, integration, snapshot, accessibility, and platform-specific tests, then runs them with a self-healing loop.

Provides a structured deep-dive into CORS misconfiguration vulnerabilities with exact payloads for every PortSwigger lab variant, including zero-day escalation techniques and blue-team detection guidance.

Drafts U.S. oil and gas royalty deeds conveying NPRI or participating royalty interests with fractional interest, land description, lease burden, cost allocation, and warranty language suitable for recording. Trigger when drafting royalty deeds, NPRI conveyances, mineral royalty transfers, or oil and gas royalty assignments.

ESLint - pluggable JavaScript/TypeScript linter USE WHEN: user mentions "ESLint", "JavaScript linting", ".eslintrc", asks about "eslint config", "linting rules", "eslint plugins", "flat config" DO NOT USE FOR: Biome - use `eslint-biome` skill, TypeScript-specific - use `typescript-eslint` skill, code quality principles - use `quality-common`

Provides a formal evaluation framework for Claude Code sessions, implementing eval-driven development (EDD) principles to define pass/fail criteria, measure reliability with pass@k metrics, and create regression test suites.

Quantifies whether a stock is suitable for long-term holding or requires tiered profit-taking using a novel 'rollercoaster rate' metric, running 15 exit strategies over ~10 years of daily history per ticker.

Process-based discrete-event simulation in Python with SimPy — processes, queues, shared resources, and time-based events. Use when simulating systems where entities contend for shared resources over time, such as manufacturing systems, service operations, network traffic, or logistics. Part of the AlterLab Academic Skills suite.

Ark protocol: VTXOs (Virtual Transaction Outputs) batched off-chain with periodic on-chain settlement. ARKADE (Ark Labs) is the first mainnet implementation. ASP (Ark Service Provider) coordinates rounds; users keep unilateral exit. USE WHEN: building Ark integrations, evaluating Ark vs Lightning, designing batched payment flows.

Access USPTO APIs for patent and trademark searches, examination history (PEDS), assignments, citations, office actions, and trademark status (TSDR). Use when searching patents or trademarks, conducting prior art searches, retrieving patent examination or assignment records, or doing intellectual property (IP) analysis. Part of the AlterLab Academic Skills suite.

Comprehensive QA and testing skill for quality assurance, test automation, and testing strategies for ReactJS, NextJS, NodeJS applications. Includes test suite generation, coverage analysis, E2E testing setup, and quality metrics. Use when designing test strategies, writing test cases, implementing test automation, performing manual testing, or analyzing test coverage.

Multi-objective optimization with pymoo — NSGA-II, NSGA-III, MOEA/D, Pareto-front computation, constraint handling, and standard benchmarks (ZDT, DTLZ). Use when solving multi-objective or constrained optimization problems, computing Pareto-optimal trade-offs, or tackling engineering design problems with competing objectives. Part of the AlterLab Academic Skills suite.

Queries the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biomedical relationships across genes, drugs, diseases, phenotypes, pathways, and biological processes. Use when exploring drug-disease or gene-disease links, building disease-centric knowledge subgraphs, or sourcing relations for drug repurposing and precision-medicine analyses. Part of the AlterLab Academic Skills suite.

xUnit.net testing framework with Fact, Theory, fixtures, DI, and mocking with Moq and NSubstitute. Covers .NET testing best practices. USE WHEN: user mentions "xUnit", ".NET testing", "Fact", "Theory", "InlineData", "Moq", "NSubstitute", "C# unit test" DO NOT USE FOR: NUnit - use `nunit`, Vitest - use `vitest`, Jest - use `jest`, Playwright - use `playwright`

Query gnomAD (Genome Aggregation Database) for population allele frequencies and gene constraint scores (pLI, LOEUF) reflecting loss-of-function intolerance. Use when checking how common a variant is across populations, filtering rare-disease candidate variants, assessing variant pathogenicity, or identifying loss-of-function intolerant genes. Part of the AlterLab Academic Skills suite.

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.

Build complete mass-spectrometry workflows with pyOpenMS — feature detection, peptide identification, protein quantification, and full LC-MS/MS pipelines across many MS file formats (mzML, mzXML) and algorithms. Use for comprehensive proteomics and MS data processing — for simple spectral comparison and metabolite identification use matchms. Part of the AlterLab Academic Skills suite.

pnpm package manager. Fast, disk-efficient with excellent monorepo support. Use when managing dependencies or setting up monorepos. USE WHEN: user mentions "pnpm", "pnpm workspace", "pnpm-workspace.yaml", asks about "pnpm commands", "pnpm install", "workspace protocol" DO NOT USE FOR: npm (use standard npm commands), yarn (use yarn commands), bun package manager

Python performance profiling with cProfile, tracemalloc, and line_profiler. Use for identifying bottlenecks and memory issues. USE WHEN: user mentions "Python profiling", "cProfile", "memory profiling", asks about "Python performance", "tracemalloc", "line_profiler", "py-spy", "Python optimization", "Python memory leak" DO NOT USE FOR: Java/Node.js profiling - use respective skills instead

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.

Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra, identifying metabolites, or library searching; for full LC-MS/MS proteomics pipelines use pyopenms. 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.

Runs time series machine learning with the aeon library — classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search via scikit-learn compatible APIs. Use when working with temporal data, sequential patterns, or time-indexed observations (univariate or multivariate) that need specialized algorithms beyond standard ML approaches. Part of the AlterLab Academic Skills suite.

Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially expressed genes between conditions from raw bulk RNA-seq counts. Part of the AlterLab Academic Skills suite.

Applies medicinal-chemistry filters with the medchem library — drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, and molecular complexity metrics for compound prioritization and library cleanup. Use when filtering or triaging a compound library, flagging PAINS or reactive groups, or assessing drug-likeness of candidate molecules. 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.

Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use when identifying cancer-specific genetic vulnerabilities, finding synthetic lethal interactions, checking whether a gene is essential in given cell lines, or validating oncology drug targets. Part of the AlterLab Academic Skills suite.