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Create consistent records for evaluating AI agents, including task success, invalid tool calls, token use, and actual costs. Produce CSV tables, database definitions, and JSON validation schemas to support reproducible comparisons and checks for performance declines.

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• Recording agent evaluation results in a shared format
• Comparing a candidate agent with a baseline
• Tracking actual cost per solved task

Turn broker trade exports into a report on holding periods, trading frequency, win rate, and realized profit or loss. Diagnose patterns such as holding losers longer, overtrading, chasing rising prices, and fixation on particular price levels, with numerical evidence.

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• Reviewing win rate, profit or loss, and holding periods
• Checking trading habits with numerical evidence
• Comparing results by month, symbol, or market

Diagnose failed or abnormal backtests, which test trading strategies on historical data. Inspect result files, strategy code, and configuration to identify bugs, make targeted repairs, rerun tests, and verify the updated metrics.

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• Investigating backtest execution errors
• Finding why a strategy produces no trades
• Checking delayed trades or low capital usage

Evaluate investment factors across multiple securities using statistical measures and grouped backtests. Compare how well each factor predicts future returns, examine its consistency over time, and combine useful factors with equal or performance-based weights.

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• Testing whether an investment factor predicts returns
• Comparing factor strength across industries and markets
• Checking how predictive power changes with holding periods

Describe your ideal customer in plain language to find matching companies and decision makers. The skill adds company details and contact information, then presents a ranked lead table showing how closely each prospect matches your criteria.

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• Find sales prospects by industry and company size
• Identify decision makers with specific job titles
• Prioritize leads by fit with your ideal customer

Use sales history and current stock to estimate stockout dates, reorder timing, quantities, and costs. Identify cash tied up in slow-moving inventory, and prepare purchase order and supplier email drafts for approval.

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• Deciding what to reorder this month
• Checking whether stock will last until delivery
• Finding cash tied up in slow-moving stock

Coordinate month-end reconciliation, owner sign-off, and a 30/60/90-day cash forecast based on closed books. Combine the results into a reporting packet with financial explanations and open items, with approval required before external delivery.

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• Reconcile monthly books against payment data
• Explain changes in revenue and margin
• Refresh cash forecasts using closed-month figures

Explore a table or data file before starting analysis. Summarize its structure, missing values, duplicates, and distributions, flag potential quality issues, and suggest useful measures, grouping columns, and follow-up analyses.

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• Understanding an unfamiliar table or data file
• Checking missing values, duplicates, and suspicious entries
• Choosing measures and grouping columns for analysis

Turn spreadsheets, query results, or pasted data into clear Python charts. Get help choosing chart types, formatting labels and colors, and producing static images or interactive charts with hover details and zoom.

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• Chart monthly revenue trends for a report
• Compare product scores in a ranked bar chart
• Explore data with hover details and zoom

Design agents that read, update, summarize, or sync spreadsheet data. Define how they select the right table, check existing values, verify writes, request confirmation for destructive changes, and report the exact rows and ranges processed.

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• Design an agent that refreshes a leads sheet each morning
• Build weekly sales reports from spreadsheet data
• Define an agent that syncs data between two systems

Define product KPIs, design dashboards, and analyze retention, feature adoption, and conversion. Choose metrics suited to each product stage, compare groups of users over time, and turn meaningful changes into concrete product actions.

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• Choosing KPIs for each product stage
• Designing dashboards for different audiences
• Comparing retention across signup groups

Convert office documents, spreadsheets, presentations, ebooks, and PDFs into Markdown text that an agent can read. Save large conversions to a file and use hosted text recognition for scanned PDF pages when needed.

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• Prepare Word documents for an agent to read
• Convert spreadsheets and slides into Markdown
• Extract content from PDFs and ebooks

Compare machine learning experiment results from JSON or CSV files. Calculate improvements over a baseline, summarize variation across repeated runs, identify trends and unusual results, and suggest experiments to test possible explanations.

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• Compare model performance across experimental conditions
• Check variation across runs with different random seeds
• Identify trends as a parameter changes

Turn an arXiv paper URL or PDF into extracted text, word frequency tables, and reference links. Create additional materials such as a two-person audio discussion, an interactive webpage, presentation slides, and a visual summary.

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• Analyze frequent terms and reference links in a paper
• Turn a paper into a two-person audio discussion
• Prepare presentation slides and a visual summary

Read, write, and transform workspace files by running Python through run_shell when execute_code_sandbox cannot access them. The workflow covers explicit file paths, batch processing, error handling, and checking generated outputs.

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• Reading configuration files in the workspace
• Transforming CSV or JSON data files
• Processing multiple text files in a batch

Analyze numerical data in Excel with box plots and an outlier summary. Extract error values from text containing the ± symbol, chart their distribution, and export statistical results as a CSV report.

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• Compare numerical distributions across spreadsheet columns
• Count outliers using the interquartile range
• Extract error values from text and chart their distribution

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.

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• Converting PDF and Office documents to Markdown
• Extracting text from images and audio
• Extracting web content and YouTube transcripts

Generate Python code to retrieve economic indicators and financial prices from sources including FRED, World Bank, BLS, OECD, IMF, and Yahoo Finance. Scripts include data cleaning, indicator documentation, and error handling for reusable datasets and updates.

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• Downloading GDP, unemployment, and inflation data
• Building datasets across countries and sources
• Automating updates for ongoing research

Use this skill when reading sensor data from CSV files, writing simulation results to CSV, processing time-series data with pandas, or handling missing values in datasets.

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Use this skill to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to dashboard', 'data visualization'. Output is a single .xlsx. Scene-layer on officecli-xlsx: inherits every xlsx hard rule. DO NOT invoke for: a single budget tracker / one-sheet CSV-with-formatting (use xlsx), a 3-statement / DCF / LBO financial model (use financial-model), a weekly report with ≤ 1 chart and < 10 rows (use xlsx).

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Use this skill to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to dashboard', 'data visualization'. Output is a single .xlsx. Scene-layer on officecli-xlsx: inherits every xlsx hard rule. DO NOT invoke for: a single budget tracker / one-sheet CSV-with-formatting (use xlsx), a 3-statement / DCF / LBO financial model (use financial-model), a weekly report with ≤ 1 chart and < 10 rows (use xlsx).

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Reads existing brand DNA, keywords.csv, and prompts.csv, then produces a plan.csv — a strictly-schema'd content architecture telling the next pipeline step which pages to create, for which keyword/prompt clusters, and in what build order.

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omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existin

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Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).

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