Canonical source and governance toolkit for Zhijian AI public Agent Skills

Bootstraps a fresh Ubuntu VPS into a complete multi-agent AI development environment in 30 minutes: coding agents, session management, safety tools, and coordination infrastructure

Effortlessly request recommended movies, TV shows and anime to Jellyseer/Overseer based on your recently watched content on Jellyfin, Plex or Emby—let SuggestArr handle it all automatically, keeping your library fresh with new and exciting content!

Go HTTP client with browser-identical TLS/HTTP2 fingerprinting. Bypass bot detection by perfectly mimicking Chrome, Firefox, and Safari at the cryptographic level (JA3/JA4, Akamai fingerprint, header order). Supports HTTP/1.1, HTTP/2, HTTP/3, sessions, cookies, and proxies.

A principles-first workflow for AI coding agents providing reusable phases and end-to-end workflows for software development tasks.

HTTP(S)/SOCKS5 rotating residential proxies - code examples & general information.

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

Create original editorial technology covers with a controlled layout, custom diagram generation, and editable SVG or PNG output.

Structured data gathering from any website using AI-powered scraper, crawler, and browser automation. Scraping and crawling with natural language prompts. Equip your LLM agents with fresh data. AI Studio python SDK for intelligent web data gathering.

Use when the user wants to install cognee and run their first remember → recall flow with the Python SDK — fresh setup, virtual env, extras selection, or a minimal working example.

Use when the user wants to drive cognee from the terminal with cognee-cli — remember/recall/forget/improve memory commands, managing datasets and config, or database migrations.

When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.