productivity▌

Production-ready skill combining Python computation (pandas, scipy, numpy, pysam, statsmodels) with ToolUniverse annotation tools for epigenomics analysis.

Session-aware REPL for NanoClaw v2 with persistent markdown storage and dynamic skill loading. \n \n Eight core commands: model switching, skill loading, session branching, cross-session search, history compaction, and export to multiple formats (md/json/txt) \n Markdown-backed sessions enable persistent state, version control compatibility, and human-readable session history \n Designed for zero external runtime dependencies and deterministic, local command handling to maintain simplicity and p

Distributed tracing, service maps, and performance analysis.

Research competitors and generate an interactive HTML battlecard for sales. \n \n Analyzes 1–5 competitors via web search, pulling product features, pricing, recent releases (90 days), positioning, and customer sentiment \n Outputs a self-contained HTML artifact with a comparison matrix overview and clickable tabs for each competitor, including talk tracks, objection handling, and \"landmine questions\" to expose weaknesses \n Optional connectors to CRM, docs, chat, and transcripts enrich battle

Automated analysis of 200+ scientific data formats with format-specific insights and quality assessment. \n \n Supports six major scientific domains: chemistry, bioinformatics, microscopy, spectroscopy, proteomics, and metabolomics, with 60+ formats per category \n Auto-detects file type and generates detailed markdown reports including metadata extraction, statistical summaries, data quality metrics, and visualization recommendations \n Includes reference documentation for each format covering

Skill by ara.so — Daily 2026 Skills collection.

Expert guidance for fine-tuning LLMs with parameter-efficient methods and production optimization. \n \n Covers LoRA, QLoRA, and full fine-tuning workflows with Hugging Face PEFT, including dataset validation, hyperparameter configuration, and adapter merging for deployment \n Provides a complete minimal working example with LoRA setup, training loop, and quantization variants for memory-constrained environments \n Includes five-stage workflow: dataset preparation, method selection, training wit

Read, analyze, and transform Excel spreadsheets with pandas, pivot tables, charts, and data cleaning. \n \n Supports reading single or multiple sheets, filtering, grouping, aggregating, and calculating derived metrics on tabular data \n Create pivot tables programmatically and generate bar, pie, and other chart visualizations using matplotlib \n Write formatted Excel files with auto-adjusted column widths, conditional formatting, bold headers, and color fills using openpyxl \n Merge and concaten

Startup Metrics Framework \n Comprehensive guide to tracking, calculating, and optimizing key performance metrics for different startup business models from seed through Series A. \n Use this skill when \n \n Working on startup metrics framework tasks or workflows \n Needing guidance, best practices, or checklists for startup metrics framework \n \n Do not use this skill when \n \n The task is unrelated to startup metrics framework \n You need a different domain or tool outside this scope \n \n

$23