screenpipe-cli
Manage screenpipe pipes (AI-driven automations) and integrations via CLI. Create, run, schedule, and debug local agents to automate tasks based on your computer activity.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
497 skills found
Manage screenpipe pipes (AI-driven automations) and integrations via CLI. Create, run, schedule, and debug local agents to automate tasks based on your computer activity.
Delegates coding tasks to the OpenAI Codex CLI for features, refactoring, PR reviews, and automated issue fixing within Git repositories.
Implement Google Gemini API audio capabilities: process, transcribe, and summarize audio files, analyze environmental sounds, and generate natural speech with controllable TTS.
Standardized debugging and diagnostic guidelines for AI coding agents.
Generate professional pull request descriptions using Grey Haven Studio standards, ensuring clear summaries, motivation, technical implementation details, and testing strategies.
Orchestrate multi-agent AI swarms using the ClawTeam CLI to automate parallel task execution, dependency management, and team collaboration with git worktree isolation and tmux support.
Security-first auditing framework for AI-generated code. Provides multi-level protection including hardcoded secret detection, dangerous pattern identification, and comprehensive vulnerability audits for modern web applications.
Standardized structure and templates for project documentation, including READMEs, API references, CLI guides, and directory layouts.
Resets workflow artifacts in the .otto/ directory. Safely removes tasks, specs, and browser sessions for a clean start.
A suite of .NET engineering skills for Domain-Driven Design (DDD), EF Core persistence, BDD-style unit testing, and IDE-like semantic code understanding with Serena MCP.
A design-focused coding agent that brings world-class interface craft, motion, and systematic front-end engineering to your development workflow.
Aggressively prune grammatical scaffolding and filler text from inputs to optimize LLM token usage while retaining core semantic content.