1K skills ・ Works with: Excel · sorted by popularity
Inspect scientific data files to understand their format, structure, and quality. Covering over 200 formats, this skill produces Markdown reports with statistical summaries, potential issues, and recommendations for preprocessing, visualization, and further analysis.
Create and edit spreadsheet files, clean messy tables, and convert tabular formats. Add formulas, formatting, and charts while following existing template styles, then recalculate formulas and check for errors before delivering the spreadsheet.
Import blockchain addresses, names, and tags from CSV or XLSX into the TRES Finance address book. Review a preview after blank entries and duplicate addresses are handled, then receive a summary of successful imports and failures.
Analyze TRES Finance XLSX reports and summarize the findings. Identify report types from sheets and columns, check balances, transactions, cost basis, or ERP synchronization, and highlight discrepancies, key metrics, and items requiring follow-up.
Extract unidentified external addresses from TRES Finance and export a deduplicated Excel workbook. An import-ready contacts sheet lets users add names and tags, while a reference sheet shows inferred networks, transaction counts, and USD volumes to help prioritize labeling.
Add multiple blockchain wallets or exchange accounts to TRES Finance. Import wallet details from CSV, Excel, or plain text, check addresses and duplicates, review the proposed entries, and confirm before submitting them together.
Retrieve economic data from official statistics and established research sources after confirming price basis, seasonal adjustment, frequency, and currency. Return structured tables with source details and access dates, plus loading guidance for the user’s statistical software.
Organize interview transcripts and other qualitative material into codes, or meaning labels, and broader themes. Develop coding guidelines, compare researchers’ coding decisions, and document whether additional data still introduces new findings.
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.
Run a five-stage finishing workflow for a drafted LaTeX paper: polish, revise, adapt its structure to a target journal, polish again, and verify references. Receive updated files, stage backups, a reference audit spreadsheet, and a final report.
Turn a recurring workflow into an agent skill, or improve an existing one. Draft instructions, compare test outputs against a baseline, review quality and resource usage, and refine the description that determines when the skill is invoked.
Audit references in a LaTeX manuscript by checking whether papers exist, bibliographic details are accurate, and cited findings support each claim. Produce a structured report with suggested corrections, then review and apply edits with a change log.
Create summary tables, treatment and control comparisons, and correlation matrices using Python, R, or Stata. Summarize averages, variation, sample sizes, and missing data to understand a dataset before regression or prepare tables for a research paper.
Review economic reports for methodological gaps, unsupported figures, and errors such as double counting. Produce a red, amber, and green scorecard with issues ranked by severity, using the relevant appraisal framework and proposing corrections when requested.
Prepare benefits and costs tables for a project before a cost-benefit analysis. Each item includes importance, sponsor cash flow, a measurement approach, and a monetary valuation method, using one of five supported appraisal frameworks.
Research an industry or product and produce a report with cited sources covering market size, competitors, regulation, supply chains, and pricing. Compare supported regions side by side and assess how concentrated competition is.
Organize Python and Stata research code using shared conventions for folders, filenames, data checks, logs, and outputs. Templates guide data cleaning, merging, regression analysis, and figure or table creation, with documentation for reproducing results.
Compare project options by valuing costs and benefits over time. Report net present value, the value remaining after discounted costs, separately for society and the sponsor, alongside funding needs and tests of how assumptions affect results.
Create a compact public finances briefing for the UK, US, or Australia using official data. Cover deficits, debt, taxes, spending, and fiscal rules, with dated sources, selected sections, multiple export formats, and optional debt sustainability analysis.
Convert documents, images, audio, web pages, and structured data into Markdown for AI analysis. Preserve headings, lists, tables, and links, extract text through OCR or transcription, and process multiple files or ZIP archives together.
Compile a macroeconomic briefing for the UK, US, euro area, or Australia using official data. Summarize key indicators with green, amber, or red assessments, source citations, and publication dates, with options for selected topics and output formats.
Build a reproducible Stata analysis from data cleaning through modeling, diagnostic checks, and robustness tests. Generate runnable .do files, regression tables, and figures for empirical papers, with additional workflows for epidemiology and machine learning approaches to causal inference.
Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.
Build reproducible empirical analyses in R, from data cleaning to statistical modeling and publication tables and figures. Covers economics, epidemiology, and machine learning approaches to causal inference, which estimates effects of interventions.