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Organize competitive analysis across six areas, from market structure to competitors’ future plans. Choose the steps relevant to a business decision and turn findings into comparison matrices, sales reference sheets, positioning responses, and threat assessments.

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• Planning competitive research for an annual strategy review
• Combining scattered competitor research into usable materials
• Preparing sales responses against a specific rival

Routes listed-company investment research to specialist skills for earnings analysis, valuation, financial model updates, and portfolio risk reviews. Helps organize evidence, assumptions, and investment theses into reports, models, memos, and monitoring trackers.

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• Prepare earnings previews and post-results analysis
• Assess company valuation and update financial models
• Track investment theses and upcoming catalysts

Prepare single-cell or single-nucleus RNA-seq analysis by checking inputs and selecting workflows that turn FASTQ sequencing files into count matrices. Route quality checks, cell annotation, clustering, and UMAP visualization to the embedded downstream skill.

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• Start single-cell RNA-seq analysis from FASTQ files
• Choose a workflow for generating count matrices
• Route existing matrices to quality checks and cell annotation

Prepare a Python environment for Mixpanel analysis by installing required libraries and checking credentials and connectivity. Use it for first-time setup, missing dependencies, or guidance on service account and OAuth sign-in options.

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• Set up a new environment for Mixpanel analysis
• Install missing Python analysis libraries
• Configure service account or OAuth credentials

Analyze company financial statements and ratios, then estimate value using projected cash flows and comparisons with similar businesses. Produce a valuation range, documented assumptions, scenario analysis, and risk assessment to support investment decisions.

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• Valuing a public company using projected cash flows
• Assessing startup unit economics and cash runway
• Evaluating the financial implications of an acquisition

Analyze business concepts in a codebase to map business areas and suggest service boundaries. Use Domain-Driven Design, an approach organized around business meaning, to classify responsibilities, assess how closely they belong together, and recommend clearer integration points.

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• Mapping business capabilities in an unfamiliar codebase
• Planning service boundaries around business terminology
• Reviewing mixed responsibilities before refactoring

Organize existing code components into business areas to prepare for service separation. Produce domain maps, component assignment tables, and refactoring plans that align namespaces, the naming structure used to organize code, with business responsibilities.

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• Grouping existing components by business responsibility
• Planning service extraction from an existing application
• Aligning code namespaces with business areas

Extract YouTube transcripts and analyze messaging, structure, intended viewers, emotional appeal, credibility, and calls to action—requests for viewers to act. Analyze one video or compare several keyword-selected videos to identify recurring strategies and content gaps.

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• Compare competitors' video content strategies
• Research intended viewers and their problems
• Identify topics competitors have not covered

Collect customer reviews from e-commerce product and review pages, including review text, ratings, dates, verified purchase status, and helpful votes. Gather multiple pages of feedback to prepare data for customer opinion research and sentiment analysis.

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• Gathering customer feedback for product research
• Preparing review text for sentiment analysis
• Collecting recent or helpful Amazon reviews

Calculate the count, sum, minimum, maximum, and average of supplied numbers or datasets, then present the results in a clear, structured format. Analysis results can also be saved to a file when requested.

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• Check the count and total of supplied numbers
• Summarize minimum, maximum, and average values
• Get a basic numerical summary of a dataset

Organize Excel analysis from reading multiple sheets through cleaning, filtering, aggregation, and export. Choose processing methods based on row counts, and use supporting skills to produce tables, charts, and Excel or CSV reports.

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• Clean missing values and inconsistent text in Excel
• Filter and aggregate data across multiple sheets
• Analyze large spreadsheets using row-based processing strategies

Capture MSBuild execution records as binary logs during .NET builds, tests, packaging, publishing, or restores. It explains unique filenames, PowerShell quoting, and file checks so logs remain available for later failure or performance analysis.

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• Saving logs before investigating a build failure
• Keeping separate logs for Debug and Release builds
• Choosing log filenames for CI artifact uploads

Choose statistical tests for research data, check their assumptions, and interpret effect sizes, which describe the magnitude of differences or relationships. This skill also supports sample size planning and produces results in APA reporting style, with figures and tables.

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• Choosing a test for group comparisons or relationships
• Checking normality, variance, and outliers before analysis
• Estimating the sample size needed for a study

Extract statistical values from research PDFs for meta-analysis, which combines results across studies. Track each value’s source and confidence, prioritize review, and manage human corrections and final approval in a shared data structure.

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• Extracting statistics from research PDFs
• Prioritizing uncertain or conflicting values for review
• Tracking human corrections and final approval

Review existing R scripts for code quality, reproducibility, domain correctness, and coding standards. Produce a separate report for each script, plus a severity-based summary of findings and the three most critical issues, without changing source files.

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• Checking whether an analysis script is reproducible
• Reviewing R code against coding conventions
• Auditing several related R scripts

Review a pre-analysis plan before registration or submission using six specialized agents. Combine checks of clarity, consistency, causal design, statistics, and feasibility into a prioritized report with revision recommendations tailored to the selected registry or journal.

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• Reviewing a pre-analysis plan before registration
• Checking alignment between hypotheses, outcomes, and analysis
• Assessing statistical power and multiple testing plans

Develop a research question, search and verify literature, combine findings, and produce a structured report. Seven modes cover guided research discussions, briefs, literature reviews, fact-checking, and systematic reviews, with statistical pooling of study results when appropriate.

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• Clarify a research topic through guided questions
• Build an annotated bibliography and synthesize findings
• Check claims against verified sources

Estimate how an intervention affects outcomes using Bayesian causal analysis. The workflow confirms causal assumptions with the user, selects a suitable method, checks whether findings withstand challenges, and reports effects with uncertainty and limitations.

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• Estimating the effect of a policy or intervention
• Mapping causal relationships before analysis
• Selecting a method for observational data

Analyze research data with guidance on choosing statistical tests, checking their assumptions, and measuring effect sizes, meaning the magnitude of differences or relationships. It also supports sample size planning, Bayesian analysis, and APA-style reports.

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• Comparing groups in experimental or survey data
• Choosing tests and checking their assumptions
• Planning the required study sample size

Estimate study sample sizes and statistical power, the chance of detecting a specified effect. Calculate detectable effects for a fixed sample, compare assumptions with power curves, and document adjustments for dropout, multiple comparisons, or clustered observations.

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• Estimate participant or sample counts before data collection
• Find the smallest effect detectable with a fixed sample
• Compare sample requirements across plausible effect sizes

Competitor page gap analysis — take a query you want to win and the pages currently outranking you, and produce a concrete brief on what to add or change to compete. Compares content depth and coverage (subtopics they cover that you don't), structure (headings, format, tables, media), search-intent match, E-E-A-T signals, schema/rich-result eligibility, internal linking, and on-page optimization — across your page vs. the top-ranking competitors. Use this skill whenever the user wants to know why a competitor outranks them on a specific query, what a top-ranking page does better, a content-gap analysis against specific competitor URLs, or a brief to beat a specific SERP. Trigger on: "why does competitor X outrank me", "content gap analysis", "competitor page analysis", "what does the top result have that I don't", "how do I beat this page", "analyze competitor pages", "SERP gap", "compete for [keyword]". For seed keyword discovery use /keyword-research; for a content calendar use /content-planner.

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Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.

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Activate this agent for any future-oriented question that requires deep quantitative analysis, historical precedents, and structured scenario planning. Triggers include: "Will [X]?", "Who will win [X]?", "What happens to [X]?", prediction requests with high stakes, foresight analysis, STEEEP scenario planning, futures cone, competitive race analysis, technology adoption curves, geopolitical shifts, or any question about a future outcome that deserves rigorous multi-step analysis. This agent runs a 12-step deterministic pipeline: Claude handles intelligence, Python handles all arithmetic. Year is NOT required — the engine infers the horizon. REQUIRES: Bash tool + Python 3.x. Not compatible with claude.ai web (use Soft Predict Future instead).

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Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".

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