Results for “data-analysis”

Perform runtime iOS app security assessments using Objection and Frida to inspect keychain, filesystem, and memory, bypass client-side protections, and evaluate data storage, network, and authentication controls during authorized penetration tests.

This skill provides guidance for video analysis and processing tasks using computer vision techniques. It should be used when analyzing video frames, detecting motion or events, tracking objects, extracting temporal data (e.g., identifying specific frames like takeoff/landing moments), or performing frame-by-frame processing with OpenCV or similar libraries.

You are an expert in AG2 (formerly AutoGen), the open-source multi-agent conversation framework. You help developers build systems where multiple AI agents collaborate through structured conversations — with tool use, human-in-the-loop, code execution, group chat orchestration, and nested conversations — for complex tasks like software development, research, and data analysis.

Structured threat modeling skill using the PASTA framework (Process for Attack Simulation and Threat Analysis) combined with Adam Shostack's 4-question framework. Use this skill whenever the user asks to do threat modeling, security analysis, map the attack surface, identify threats, or review an application for security risks — even if they don't mention PASTA or a specific framework by name. Core activities: Component Mapping (architecture + data flows), Critical Assessment (business impact prioritization), and Logic Flaw Identification (attacker mindset on business logic). Produces: component map diagram (Mermaid), data flow diagram (Mermaid), attack tree (Mermaid), STRIDE threat table, prioritized risk register, and an actionable mitigation plan. Invoke proactively for any security review, architecture review, or "what could go wrong?" session.

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.

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 Ensembl genome database REST API across 250+ species for gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, and Variant Effect Predictor (VEP) annotations. Use when mapping gene IDs or coordinates, fetching genomic sequence, finding orthologs across species, or predicting variant consequences for genomic research. 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.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `marketpulse`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Query real-time and historical equity market data—prices, news, financial statements, metrics, analyst estimates, insider and institutional activity, SEC filings, earnings press releases, segmented revenues, stock screening, and macro interest rates. Use when you need broad public-market research from a single AIsa-backed skill. Use when: the user needs market data, stock analysis, dividend research, or read-only financial data workflows.

Structured methodology for solving, writing, and presenting hiring case studies for Senior Product Manager and Technical PM roles. Use when resolving a case study, reviewing a case study response, or building a presentation prototype from a completed case study. Covers the full process from problem understanding to final delivery, including data analysis, written response, and interactive presentation.

Generate a MoonSpec implementation plan and design artifacts from a single-story spec. Use when the user asks to run or reproduce `/moonspec.plan`, create or update `plan.md`, produce `research.md`, `data-model.md`, `contracts/`, or `quickstart.md`, evaluate repo principles, define separate unit and integration test strategies, and perform repo-aware gap analysis before `/moonspec.tasks`.

Run the standard single-cell RNA-seq analysis pipeline with Scanpy on AnnData — QC filtering, normalization, dimensionality reduction (PCA, UMAP, t-SNE), Leiden/Louvain clustering, marker/differential expression, PAGA trajectories, and plotting. Use when analyzing scRNA-seq data through clustering, cell-type annotation, DE, or pseudotime workflows; for building or reading the .h5ad data structure itself (layers, obs/var, concatenation, backed mode) prefer alterlab-anndata instead, and for RNA velocity from spliced/unspliced counts prefer alterlab-scvelo instead. Part of the AlterLab Academic Skills suite.

Create and edit landscape terrain — heightmaps, sculpting, paint layers, terrain analysis, mesh projection, and procedural features like mountains/valleys/craters (LandscapeService). Use when the user asks to create a landscape, sculpt/raise/lower terrain, import or export a heightmap, paint terrain layers, or add procedural terrain features. For terrain materials load landscape-materials; for real-world heightmaps load terrain-data.

Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.

Long-running design anthropologist that builds comprehensive visual databases from 500-1000 real-world examples, extracting color palettes, typography patterns, layout systems, and interaction design across any domain (portfolios, e-commerce, SaaS, adult content, technical showcases). This skill should be used when users need exhaustive design research, pattern recognition across large example sets, or systematic visual analysis for competitive positioning.

Query NCBI Gene via the E-utilities and Datasets APIs, searching by gene symbol or Gene ID and retrieving gene information (RefSeqs, GO terms, genomic locations, associated phenotypes) including batch lookups. Use when resolving gene symbols to IDs, annotating gene lists, or pulling functional and positional gene metadata for downstream analysis. Part of the AlterLab Academic Skills suite.

Model a Phase-Locked Loop (PLL) IC from its datasheet or system specs using Mixed-Signal Blockset. Without this skill, agents universally select the wrong solver and produce non-functional PLL models — 100% of unguided attempts fail. Covers Integer-N, Fractional-N, Dual Modulus architectures, loop filter design, lock time optimization, VCO phase noise configuration, and msbPllArchitectures/msbPllFoundation block assembly. Use when: PLL modeling, frequency synthesizer design, phase noise simulation, lock time analysis, charge pump design, loop filter tuning, datasheet-to-model, Mixed-Signal Blockset PLL, msbPllArchitectures.

Run full computational-pathology workflows with PathML — whole-slide-image (WSI) analysis across 160+ slide formats, multiplexed immunofluorescence (CODEX, Vectra, MERFISH), nucleus segmentation/classification (HoVer-Net, HACTNet), tissue- and cell-graph construction, HDF5 dataset management, and deep-learning model training on pathology data. Use when the user builds end-to-end deep-learning pathology pipelines, analyzes multiplexed or spatial-proteomics slides, or segments nuclei. For lightweight H&E slide preprocessing, tissue masking, or plain Random/Grid/Score tile extraction prefer alterlab-histolab instead. Part of the AlterLab Academic Skills suite.

Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.

Production pipeline for interactive and generative visual art using p5.js. Creates browser-based sketches, generative art, data visualizations, interactive experiences, 3D scenes, audio-reactive visuals, and motion graphics — exported as HTML, PNG, GIF, MP4, or SVG. Covers: 2D/3D rendering, noise and particle systems, flow fields, shaders (GLSL), pixel manipulation, kinetic typography, WebGL scenes, audio analysis, mouse/keyboard interaction, and headless high-res export. Use when users request: p5.js sketches, creative coding, generative art, interactive visualizations, canvas animations, browser-based visual art, data viz, shader effects, or any p5.js project.

Production pipeline for interactive and generative visual art using p5.js. Creates browser-based sketches, generative art, data visualizations, interactive experiences, 3D scenes, audio-reactive visuals, and motion graphics — exported as HTML, PNG, GIF, MP4, or SVG. Covers: 2D/3D rendering, noise and particle systems, flow fields, shaders (GLSL), pixel manipulation, kinetic typography, WebGL scenes, audio analysis, mouse/keyboard interaction, and headless high-res export. Use when users request: p5.js sketches, creative coding, generative art, interactive visualizations, canvas animations, browser-based visual art, data viz, shader effects, or any p5.js project.

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaf

Audit and attack session cookies via missing Secure/HttpOnly/SameSite attributes, overly broad Domain/Path scope, non-expiring persistent cookies, absent __Host- and __Secure- prefixes, browser cache leakage (Cache-Control: no-store missing), session token predictability via Burp Sequencer analysis, server-side session not invalidated on logout, and SSO single-logout bypass. Tools: Burp Suite Repeater/Sequencer, OWASP ZAP, EditThisCookie, Tamper Data, Cookiebro.

Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.

YouTube Search API via AIsa unified endpoint. Search YouTube videos, channels, and playlists with a single AIsa API key — no Google API key or OAuth required. Use this skill when users want to search YouTube content. For other AIsa capabilities (LLM, financial data, Twitter, web search), see the aisa-core skill. Use when: the user needs YouTube search, trend discovery, channel research, or SERP analysis.

California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA) compliance advisor — business threshold analysis, consumer rights fulfillment (access, delete, correct, opt-out of sale/sharing, limit SPI), privacy notice drafting, service provider vs. contractor vs. third-party classification, sensitive personal information (SPI) handling, data minimization, opt-out mechanisms, CPPA enforcement, penalty exposure, GDPR comparison, and gap assessments for businesses operating in or targeting California residents.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `trend-forecast`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Multi-signal trend forecasting for autonomous agents. Combines prediction market odds, Twitter/X social sentiment, news velocity, and stock market data into a unified trend analysis with confidence scoring. Powered by AIsa — one API key, five data streams. Use when: the user needs X/Twitter research, monitoring, posting, or engagement workflows.

>- DMAIC (Define-Measure-Analyze-Improve-Control) — Six Sigma structured problem-solving for chronic, data-driven quality improvement projects. Use when a problem recurs despite corrective actions, when a process needs systematic capability improvement, or when a customer requests a Six Sigma approach. Use 8D for reactive single-incident problems; use DMAIC for recurring systemic issues requiring statistical analysis. Covers IATF 16949 §10.1 and ISO 9001 §10.3.

This skill should be used when the user asks about crypto prices, market data, K-line charts, funding rates, open interest, long/short ratios, whale orders, liquidation data, crypto news, newsflash, Twitter crypto tweets, trending coins, airdrops, drop radar, airdrop research, project analysis, exchange listings, stock quotes, treasury holdings, or any crypto market query. Also use when user asks about configuring or checking AiCoin API key. Use when user says: 'BTC price', 'check price', 'show K-line', 'funding rate', 'open interest', 'whale orders', 'long/short ratio', 'crypto news', 'newsflash', 'ETF', '监管', '政策', 'trending coins', 'airdrop', 'drop radar', '查行情', '看价格', '大饼多少钱', 'K线', '资金费率', '多空比', '鲸鱼单', '新闻', '快讯', '新闻快讯', '热门币', '空投', '空投项目', '空投机会', '空投研报', '项目分析', '项目详情', '上了哪些交易所', '推特', 'Twitter', 'liquidation map', '配置AiCoin key', 'AiCoin API key', 'AiCoin key安全吗'. Covers 200+ exchanges with real-time data. MUST run node scripts to fetch real data. NEVER generate fake prices or hallucinate market

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.

Performs deep architectural analysis of a specified module, directory, or feature area by examining structural coupling, data flow, concurrency patterns, risk, and SOLID alignment. Use when the user wants to assess, evaluate, or review the architecture, design quality, dependency structure, coupling, cohesion, or technical debt of an existing part of the codebase. Not for investigating specific bugs, runtime errors, or failures — use investigate. Not for test planning — use automated-test-planning. Not for file-level code review — use code-review. Not for researching open-ended options, prior art, or how something works — use research. Not for designing a new interface or contract — use design-an-api. Not for writing documentation or architectural decision records.

Comprehensive post-build verification of Shannon Framework's application outputs using three-layer methodology: Flow Verification (execution trace analysis), Artifact Verification (physical output inspection), and Functional Verification (runtime testing). Verifies Shannon built production-ready applications across all domains (Frontend, Backend, Database, Mobile, DevOps). Ensures NO MOCKS compliance, cross-platform functionality, and complete integration. Use after: Shannon builds any application via /shannon:wave, need to verify build quality, production readiness assessment.

Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B tests with confounders, using instrumental variables, performing counterfactual reasoning ("what would have happened if..."), validating causal claims with sensitivity analysis, working with observational data where randomization is impossible, or any analysis where the question is "what is the CAUSAL effect of X on Y" rather than just "how do X and Y relate?"