Desktop Guardian
An MCP server that provides on-demand skill loading for AI coding assistants.
4,796,759 AI resources indexedWorking toward the world’s largest AI resource index.
Reusable instructions for compatible AI agents.
Field counts come from the latest full-catalog snapshot. A resource can belong to several fields and platforms.
An MCP server that provides on-demand skill loading for AI coding assistants.
Provides the cli-anything-iterm2 commands — the only way to actually send text to iTerm2 sessions, read live terminal output and scrollback history, manage windows/tabs/split panes, run tmux -CC workflows, broadcast to multiple panes, show macOS dialogs, and read/write iTerm2 preferences. Include...
Generate R/Python code for volcano plots from DEG (Differentially Expressed Genes) analysis results. Triggered when user needs visualization of gene expression data, p-value vs fold-change scatter plots, publication-ready figures for bioinformatics analysis.
10 econometrics skills. Trigger: causal analysis, regression models, treatment effects, panel data. Design: method-centric guides with R/Python code and diagnostic tests.
Design and execute statistical analyses including regression modeling, hypothesis testing, power analysis, and robustness checks using R, Stata, SPSS, or Python
Comprehensive fuzzing operations with AFL++, libFuzzer, and OSS-Fuzz integration
Deep integration with Ghidra and IDA Pro for binary analysis and reverse engineering
Exploit development automation using pwntools framework
Analyze test coverage and identify gaps before migration to ensure adequate safety nets
Validates gate criteria before PRD lifecycle advancement by delegating to the readiness scoring pipeline (scripts/readiness.py). Returns a graduated PASS / WARN / BLOCK verdict with top blockers and their causal chain. Triggers before advancing from v0.X to v0.Y or explicit `/ghm-gate-check`.
Motion capture data processing and analysis skill for gait analysis and biomechanical studies
Implement or correct SVM cross-validation code in R or Python to accurately calculate AUC by computing the metric per iteration using decision values or probabilities, avoiding methodological errors like label averaging.
Try a broader search or clear some filters.
Platform labels describe intended use. Read each resource's author credits, requirements and available access methods.