Agent Skills Hub

submit-work

Submit completed tasks on OpenAnt via CLI. Handles text reports, file uploads (images, docs, code), and external proof links to ensure verified deliverables.

context-driven-development

Standardizes project context by managing artifacts (product, tech-stack, workflow, tracks) in a conductor/ directory. Supports project scaffolding, artifact synchronization, and AI alignment for greenfield and brownfield projects.

skill-vetter

Security-first vetting protocol for AI agent skills. Detects red flags like credential theft, obfuscated code, and unauthorized data exfiltration before installation.

mobile-testing

Comprehensive mobile testing for iOS and Android, covering gestures, sensors, permissions, device fragmentation, and performance across 1000+ real and virtual devices.

test-delete-skill

Unified AI gateway for 100+ LLMs with OpenAI-compatible API, model fallbacks, load balancing, and enterprise-grade tools.

dev-workflow

Full-stack SDLC agent workflow managing the entire production lifecycle from intake and planning to automated testing, CI/CD, and infrastructure deployment using MCP tools.

moltbot-plugin-2do

Create tasks and send them to the 2Do app via email. Automatically parses natural language for titles, due dates, priority, lists, and tags.

prompt-writing

Create, refine, and optimize high-quality YAML prompts for AI assistants using structure guidelines, template patterns, and quality standards.

model-registry-maintainer

Maintain and update the MassGen model registry, including backend capabilities, model metadata, pricing structures, and context window configurations for new and existing AI models.

srt字幕转markdown笔记

Convert SRT subtitle files into structured Markdown notes with punctuation, paragraph formatting, and automated video screenshot placeholders.

skills

A project-specific architectural template for Next.js 15, FastAPI, and Supabase applications, including structured AI integration patterns.

markdown-token-optimizer

Analyzes markdown files to identify token-wasting patterns, providing actionable suggestions to optimize documentation for LLM consumption and token efficiency.