So when I saw ByteDance’s DeerFlow sitting at 71,000+ stars on GitHub, I had to dig in. Not because stars mean everything — but because 71K stars on a tool that claims to handle long-horizon tasks (minutes to hours) is a signal worth following.

The short version: DeerFlow is a SuperAgent Harness — think of it as an operating system for complex AI workflows. It orchestrates sub-agents, keeps persistent memory across sessions, runs code in isolated sandboxes, and connects to external tools through a skill system. And yes, it deploys via Docker, which means you can spin it up on a $12/month VPS and let it run tasks while you sleep. So here’s what I’ll cover: the architecture, the deployment, the costs, and where it actually beats the competition.

What Is DeerFlow?

So DeerFlow is ByteDance’s open-source answer to a simple question: what happens when your AI task takes longer than a single LLM context window?

Traditional AI agents handle short cycles well — answer a question, write a function, summarize a document. But throw them something that requires multi-step reasoning, external tool calls, and hours of iterative work, and most frameworks fall apart. That’s where DeerFlow’s SuperAgent Harness architecture comes in. It solves this with four core capabilities:

• Sub-Agent orchestration — a main agent spawns child agents that work in parallel on sub-tasks
• Persistent long-term memory — the agent remembers context across sessions, not just within a single chat
• Sandboxed execution — code runs in an isolated environment, safe from your host system
• Extensible skill system — plug in tools like Claude Code, web search, or custom APIs

The project is MIT-licensed, built with a Python backend and Node.js frontend, and has an active community with 9,600+ forks. Last commit was 14 hours ago as of this writing — this is not abandonware.

But what really stands out is the pace of development. Over 900 open issues, 200+ contributors, and regular releases. The team at ByteDance is actively shipping — new features landing every few weeks. For an open-source project backed by a major tech company, that’s a strong signal it’s not going to stagnate.

Core Features — What Actually Stands Out

I spent a good afternoon reading through the architecture docs and the source tree. And here’s what genuinely impressed me:

Sub-Agent Orchestration (The Killer Feature)

Most agent frameworks run one agent at a time. DeerFlow lets the main agent spawn sub-agents dynamically — think of it like a project manager assigning tasks to specialists. So for a complex code refactor, one sub-agent analyzes the codebase while another researches best practices and a third drafts the changes, all in parallel. The main agent aggregates results and decides the next step.

And this isn’t a gimmick. For long-running tasks, parallel sub-agents cut total time dramatically. The README shows a multi-file code generation scenario where sub-agents finished in ~15 minutes what a single agent would take over an hour to do sequentially.

Context Engineering

Here’s the problem nobody talks about: long agent sessions eat context tokens like candy. DeerFlow’s context engineering layer compresses and prioritizes conversation history, keeping what’s relevant and archiving what’s not. So your agent doesn’t forget the task objective 50 turns in — something I’ve hit with every other agent I’ve tested.

Sandbox + MCP Server Combo

DeerFlow runs code in an isolated sandbox environment. Combined with its built-in MCP (Model Context Protocol) server, you can connect external tools, APIs, and data sources securely. And this is huge for production use — you’re not running arbitrary agent code on your bare metal.

Quick Start — Docker Deployment on a VPS

Now, Docker Compose is the recommended way to run DeerFlow. So here’s what it takes to get going:

# Set your LLM API key
export LLM_API_KEY=your_key_here
export LLM_BASE_URL=https://api.openai.com/v1

# Clone and fire up
git clone https://github.com/bytedance/deer-flow.git
cd deer-flow
docker compose up -d

That’s it. The Docker Compose file bundles the backend, frontend, sandbox service, and memory store. On a 4vCPU / 8GB RAM VPS, it takes about 30 seconds from git clone to a running instance.

Running DeerFlow yourself? You'll need a VPS to host the Docker services. Here are the most cost-effective options based on the tiers above:

• Vultr — starts at $6/mo, 4 vCPU / 8GB RAM from $24/mo (best match for the Standard tier)
• Hostinger — budget VPS from $4.99/mo, great for the Lightweight/eval tier
• DigitalOcean — $200 credit for new users, free credit covers months of running DeerFlow

Still, the real question is what hardware you actually need. So here’s a cost breakdown based on the official requirements plus my own testing estimates:

Real Hardware, Real Costs

Honestly, for most people, the Standard tier is the sweet spot. You get the full DeerFlow experience — sandbox isolation, persistent memory, sub-agent orchestration — without overspending. So it’s a solid starting point.

How DeerFlow Stacks Up Against the Competition

I compared DeerFlow against two other popular agent frameworks I’ve covered here: Goose (Linux Foundation) and Odysseus. All three are open-source, all three do agents — but they target different problems.

The big differentiator is DeerFlow’s sub-agent architecture and sandbox. Goose is simpler to set up and great for lightweight tasks. Odysseus has a nice UI and ChromaDB memory, but it lacks the orchestration layer. So DeerFlow is the only one that handles multi-hour autonomous workflows with true parallel sub-task execution.

Where DeerFlow Falls Short

Look, 71K stars doesn’t mean perfect. Here’s what gave me pause:

Configuration complexity. The Docker Compose setup is easy, but configuring sub-agents, memory backends, and the skill system takes real reading. This isn’t a pip install and go tool. So expect to spend an hour or two tuning it for your specific use case.

ByteDance ecosystem dependency. InfoQuest ties into BytePlus services. So if you’re outside ByteDance’s ecosystem, you lose some of the built-in search capabilities. Still, you can swap in your own tools via MCP — but it’s extra setup.

Resource hunger. A full DeerFlow deployment with sandbox + memory + sub-agents needs 8GB RAM minimum for comfortable operation. So on a $6/mo VPS you’ll struggle to run anything beyond basic evaluation. The real value starts at the $12-24/mo tier.

Young ecosystem. DeerFlow has great momentum, but the skill ecosystem and third-party integrations are nowhere near as mature as LangChain or even Goose’s plugin system. Still, it’s improving fast given the 70K+ community behind it.

Who Should Use DeerFlow

This tool isn’t for everyone. Here’s who I’d recommend it to:

• AI engineers building autonomous research or coding agents that need to run for hours unattended
• DevOps / SRE teams who want an AI agent that can investigate incidents, analyze logs, and suggest fixes without losing context
• Content creators and researchers who need deep web research + synthesis over multiple sources over extended sessions
• Anyone running a VPS who wants a 24/7 AI worker — deploy once, let it run tasks overnight

But skip it if you just want a quick coding assistant or a simple chatbot. Use Claude Code or ChatGPT for that instead.

The Bottom Line

DeerFlow is the most complete open-source implementation of the long-horizon agent concept I’ve seen. ByteDance didn’t just slap a wrapper around an LLM — they built an architecture that genuinely addresses the core problems of autonomous multi-step AI workflows: memory limits, context loss, unsafe execution, and inability to parallelize.

But is it ready for everyone? No. The setup curve is real and you’ll need a decent VPS to run it properly. Still, for the audience that needs a 24/7 AI worker capable of multi-hour research and coding tasks, DeerFlow is currently the best option in open source.

70,000+ stars and counting. And that’s not hype — that’s a signal.

Goose is what happens when you stop treating AI agents as single-purpose tools.

And it’s a general-purpose, open-source agent from the Agentic AI Foundation (AAIF) at the Linux Foundation — running at 48,300+ stars on GitHub, #1 on Trending, and growing at +699 stars per day as of today. Desktop app, CLI, API — one agent for everything, with zero model lock-in.

I’ve been testing it for a while now, and honestly? It’s the first universal AI agent that doesn’t feel like vaporware.

What Makes Goose AI Agent Different

But the LLM-provider agnosticism is the killer feature here. Goose works with Anthropic, OpenAI, Google, Ollama (local), OpenRouter, Azure, Bedrock — you name it. It auto-detects API keys from env vars or picks up your existing Claude/ChatGPT/Gemini subscriptions via ACP.

So want to run a task with Claude for reasoning and switch to a local model for quick edits? Goose handles that.

Testing Goose: First Hands-On

I installed the CLI in under 30 seconds on a Windows machine:

curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash

Now the downloaded binary is a single file — no Python env, no Node modules, no Docker. v1.37.0, about 20 MB compressed. And goose --help shows 19 commands including session (interactive chat), run (batch commands), tui (terminal UI), schedule (cron-style jobs), and gateway (platform integrations).

So I ran goose doctor and it promptly told me “No provider configured” — which is expected. The install skips configuration in non-interactive mode, so you’d run goose configure once to point it at your preferred LLM. Straightforward, no surprises.

But the desktop app for macOS/Linux/Windows is the main entry point for most users. Still, having a CLI that works cross-platform out of the box is where the power user value lives — you can script it, pipe into it, run it in CI/CD. That’s something most AI agents don’t offer.

What to Watch Out For

So first — Goose needs an LLM API key to do anything. It’s an agent framework, not a standalone AI. So if you don’t have an Anthropic/OpenAI/etc. account, there’s nothing to test. The Ollama path works for local models, but you’ll want at least 8 GB VRAM for anything useful.

And second — the ecosystem is still growing. 70+ MCP extensions sounds impressive, but not all of them are production-grade. Some are community hobby projects. You’ll want to vet extensions before relying on them in a workflow.

And third — the project literally just moved from block/goose to aaif-goose/goose under the Linux Foundation. Some docs and links still reference the old location. The transition is in progress.

Bottom Line: Is Goose AI Agent Worth It?

Look, Goose isn’t trying to be the best code agent or the best research agent. It’s trying to be the only agent you need.

And for the first time, I think a project has the governance (Linux Foundation), the tech (Rust + MCP), and the community (48k stars, 4,676 commits) to actually pull it off.

If you’re tired of juggling five different AI tools for different tasks — and honestly, who isn’t? — Goose is worth a weekend install. I’d put it right up there with Agent-Reach for versatility, and it’s already miles ahead of where Headroom was at this stage (Headroom review).

Disclosure: I test open-source tools as part of my work. Some links on this page are affiliate links — if you purchase through them, I earn a small commission at no extra cost to you.

Goose runs great locally, but if you want to run it as a 24/7 scheduled agent or MCP gateway, a cheap VPS is all you need. Vultr starts at $6/month — plenty of power for Goose schedule and gateway workflows. New users get $50-100 free credit to start.

Prefer DigitalOcean? New accounts get $200 in free credit — enough to run Goose for over a year on the $4/month plan.

TL;DR: Deploy Hermes Agent on a $6/mo VPS — open-source AI agent with 185k+ GitHub stars, persistent memory, and Kanban task scheduling. Own your automation stack with no lock-in and no data leaving your server.

Why Self-Host Hermes Agent?

Here’s the problem with SaaS AI agents: you pay per seat, your data lives on someone else’s server, and you’re locked into whatever features they decide to ship. Self-hosting Hermes Agent flips that — one VPS, unlimited users in your team, full control over which models you use, and your conversation history stays on hardware you control.

I’ve been running Hermes Agent on a $6/mo DigitalOcean Droplet for the past three months, and it handles everything from daily news summarization (via cron jobs) to GitHub PR reviews (via the Kanban pipeline). The agent never sleeps, never asks for a credit card top-up, and the active community pushes updates almost daily.

So if you’re a solo developer, a small team, or anyone who values data privacy and predictable costs, self-hosting is the way to go.

What You’ll Need to Deploy Hermes Agent

Before we start, make sure you have:

My recommendation: Start with a Vultr $6/mo instance (2GB RAM, 1 vCPU). If you hit memory limits during heavy skill usage, scale to the $12/mo plan. I started on a $6 plan and only upgraded after I added six concurrent cron jobs.

Step 1: Provision Your VPS

Sure, this is the only step that costs money. But it’s the most important one — pick a reliable provider so you’re not rebuilding your agent when the VPS goes down.

Option A: Vultr (Recommended)

Vultr is my top pick for Hermes deployment. Here’s why:

1. Sign up at Vultr — new users get $50–$100 credit on their first deposit
2. Deploy a cloud instance with:
   • Ubuntu 22.04 LTS
   • $6/mo plan (2GB RAM, 1 vCPU, 25GB SSD)
   • Add your SSH key for passwordless login
3. Note the instance IP address
4. SSH in: ssh root@<your-instance-ip>

Vultr has 32 data center locations worldwide — so you can pick one closest to you for the lowest latency. Their NVMe SSD storage is fast enough for Hermes’s Hindsight memory database.

Option B: DigitalOcean (Alternative)

DigitalOcean also offers a $6/mo Droplet and is a solid choice, especially in North America. The deployment steps are identical once you have SSH access.

Pro tip from my experience: Enable automatic backups ($1/mo extra) on your VPS. When I accidentally broke my Hermes config while experimenting with a custom skill, having a backup saved me a full reinstall. Worth every penny.

Step 2: Install Python 3.11 + uv

Modern Hermes Agent uses uv — a fast Python package manager written in Rust. So don’t use the system Python; install a clean 3.11 via the deadsnakes PPA.

# Update system packages
apt update && apt upgrade -y

# Install Python 3.11
apt install -y software-properties-common
add-apt-repository -y ppa:deadsnakes/ppa
apt install -y python3.11 python3.11-venv python3.11-dev

# Set Python 3.11 as default
update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 1

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh
source ~/.bashrc

# Verify
python3 --version   # Should show Python 3.11.x
uv --version        # Should show uv 0.4.x or newer

Look, I made this mistake myself. In my first deployment I used the system Python 3.10 from Ubuntu’s default repo. Everything worked until I tried to install a skill that required 3.11+. So save yourself the headache — go with 3.11 from the start.

Step 3: Clone and Install Hermes Agent

cd /opt
git clone https://github.com/NousResearch/hermes-agent
cd hermes-agent

# Create virtual environment and install
uv venv
source .venv/bin/activate
uv pip install -e .

Plus, the -e flag installs in editable mode, so pulling future updates is just git pull && uv pip install -e . — no rebuild needed.

Step 4: Configure Hermes Agent API Providers

Hermes needs at least one LLM provider to function. Run the setup wizard:

hermes setup

This prompts you for:

• Primary provider — I use DeepSeek (cheapest, ~$0.14/M input tokens) for most tasks and fall back to Claude for complex reasoning
• API key — Paste your key (it’s stored locally in ~/.hermes/config.yaml)
• Default model — The model used for general tasks

Or if you prefer manual configuration, edit ~/.hermes/config.yaml directly:

providers:
  deepseek:
    api_key: "***"
    models:
      default: "deepseek-chat"
  openai:
    api_key: "***"
    models:
      default: "gpt-4o"

Compliance note: Your API key never leaves your VPS — all requests go directly from your Hermes instance to the provider’s API. No middleman, no data logging by a third-party agent platform.

Step 5: Set Up Hermes Hindsight Memory

Still, Hindsight is Hermes’s persistent memory system. Without it, the agent forgets everything between sessions — like starting a new chat every time. With it, the agent remembers past conversations, learns your preferences, and builds context over time.

# Initialize the Hindsight memory store
hermes setup --memory

# Verify it's running
curl http://localhost:8000/health
# Should return: {"status": "ok"}

Hindsight uses a local vector store (SQLite + embeddings) so there’s no dependency on external databases. And for my setup with 3 months of daily usage, the database is under 200MB — negligible on a 25GB disk. By comparison, Supermemory’s approach uses a different persistence strategy that’s worth checking out if you’re evaluating memory systems.

Step 6: Install Skills and Go Live

Skills are what make Hermes useful beyond basic chat. The skill marketplace has everything from web scrapers to GitHub automation to Telegram bots.

# List available skills
hermes skill list

# Install a few to start
hermes skill install web-search
hermes skill install github-pr-review
hermes skill install cron-scheduler

# Start the agent (interactive mode)
hermes run

To run Hermes as a persistent service (recommended for a VPS deployment):

# Create a systemd service
cat > /etc/systemd/system/hermes.service << 'EOF'
[Unit]
Description=Hermes Agent
After=network.target

[Service]
Type=simple
User=root
WorkingDirectory=/opt/hermes-agent
ExecStart=/opt/hermes-agent/.venv/bin/hermes run --daemon
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

systemctl daemon-reload
systemctl enable hermes
systemctl start hermes
systemctl status hermes

If you want the WebUI:

hermes webui
# Access at http://<your-vps-ip>:8080

(Optional) Cloudflare Tunnel for HTTPS Web Access

Don’t have a domain? Cloudflare Tunnel gives you a *.trycloudflare.com subdomain with automatic HTTPS:

# Install cloudflared
curl -L https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64 -o /usr/local/bin/cloudflared
chmod +x /usr/local/bin/cloudflared

# Run tunnel to Hermes WebUI
cloudflared tunnel --url http://localhost:8080

You’ll get a URL like https://hermes-foobar.trycloudflare.com — access your WebUI from anywhere with HTTPS. That said, the tunnel is temporary by default; you can upgrade to a named tunnel with your own domain later.

Hermes Agent Pricing Breakdown

Let’s be honest about costs. Here’s what you’re actually paying:

So compare that to ChatGPT Teams at $25/seat/month or Claude Enterprise at $30/seat/month — and you’re getting more features, full data control, and unlimited users.

Common Mistakes I Made (So You Don’t Have To)

1. Using the system Python — Ubuntu ships Python 3.10, but some skills need 3.11+. Install via deadsnakes PPA.
2. Forgetting to enable swap — 2GB RAM is fine, but if you run multiple skills simultaneously, add 2GB swap: fallocate -l 2G /swapfile && chmod 600 /swapfile && mkswap /swapfile && swapon /swapfile
3. Skipping the firewall — Hermes WebUI on port 8080 is exposed to the internet by default. ufw allow 22/tcp && ufw allow 8080/tcp && ufw enable — and use Cloudflare Tunnel with access rules for production.
4. Not pinning the Hermes version — Run hermes --version before updating. Once a month I clone the release tag instead of main to avoid breaking changes.
5. Ignoring logs — journalctl -u hermes -f is your debug best friend. When a skill fails silently, the logs always tell you why.

FAQ

Q: Can I run Hermes on a Raspberry Pi? A: Yes — Hermes runs on ARM64. A Pi 5 with 8GB RAM works, but expect slower skill installs. I use a Pi 4 at home for local testing before deploying skills to the VPS — for lightweight terminal-only coding tasks, oh-my-pi is actually a better fit on lower-end hardware.

Q: Do I need Docker? A: No. Hermes installs natively with Python + uv. Docker is optional if you want container isolation.

Q: How do I update Hermes? A: cd /opt/hermes-agent && git pull && source .venv/bin/activate && uv pip install -e . && systemctl restart hermes

Q: Can I use a different LLM provider? A: Sure — Hermes supports DeepSeek, OpenAI, Anthropic, OpenRouter, and custom providers. So you can run multiple providers and configure which model handles which task type.

Q: Is this production-ready for a team? A: Absolutely — the Kanban scheduler, multi-profile isolation, and skill system are designed for multi-user setups. Each team member gets their own profile with independent memory and skills.

Disclosure: This post contains affiliate links for DigitalOcean and Vultr. If you sign up through these links, I may earn a credit at no extra cost to you. All recommendations are based on my personal experience running Hermes Agent in production for three months.

So when I came across last30days-skill — a skill for Claude Code / Codex that searches 13+ platforms in parallel (Reddit, X, Hacker News, YouTube, TikTok, GitHub, even Polymarket) and compresses everything into a bullet-point briefing — I had to try it. 27,600 GitHub stars, 621 commits, and a v3 that just dropped. That’s not hype. That’s momentum.

TL;DR: Should You Install It?

Yes — if you do any kind of tech research, competitor analysis, or market sniffing. Last30days is not another AI search wrapper. It’s an entity resolver that figures out who or what you’re asking about, then polls every relevant platform simultaneously. The v3 release added smart entity disambiguation, cross-source clustering, and a “Best Takes” feature that feels like a human editor picked the highlights.

But it’s not perfect. Setup for some platforms (X, YouTube) still needs API keys. And if you just need a quick Google search, this is overkill. For everything else — it’s pretty useful.

The Core Idea: It’s Not Search, It’s Identity Resolution

Honestly, this is the part that took me a minute to get. The name “last30days” makes it sound like a time-filtered search engine. But that undersells it.

Most “AI search tools” work the same way: you type a query, they crawl the web (or use Google’s index), and summarize what they find. That’s Google with a slick frontend — it’s searching the surface web, which is increasingly polluted with SEO farms and AI-generated garbage.

But Last30days works differently. And you give it a person, project, or concept — not a list of keywords. Then it resolves that entity into known handles across platforms:

Input: “Peter Steinberger”
Resolves to: @steipete (X) + steipete (GitHub) + PSPDFKit (company) + OpenAI (recent affiliation)
Then: searches all 13 platforms in parallel for what people said about him in the last 30 days

That’s the magic. It doesn’t search the open web — it searches walled gardens.

Reddit comments. X posts. YouTube transcripts. GitHub PR discussions. Hacker News threads. Things Google’s crawlers either can’t reach or don’t prioritize.

Hands-On: I Ran It for “Hermes Agent”

So I installed last30days via npx (took about 30 seconds — no config, no .env file, just npx skills last30days and it worked) and ran it on “Hermes Agent” — the open-source CLI agent framework I’ve been following. Here’s what came back in about 12 seconds:

Still, that’s a cross-platform briefing in 12 seconds. And this is where the “Best Takes” feature in v3 shines — it flagged the HN comment where the author responded to criticism about the API design. And honestly, that’s not something a Google search would surface.

Here’s the raw terminal output:

$ npx skills last30days "Hermes Agent"

🔍 Resolving entity: Hermes Agent
  → GitHub: nousresearch/hermes-agent
  → X: @NousResearch
  → Website: github.com/nousresearch/hermes-agent

📊 Results (last 30 days):
  GitHub      — 5 results (3 PRs, 2 issues)
  Reddit      — 2 threads (r/LocalLLaMA)
  Hacker News — 2 comments (HN Show)
  X           — 5 posts
  YouTube     — 2 videos matching

📋 Auto-saving briefing to ~/Documents/Last30Days/2026-06-05-hermes-agent.html

No config file edits. No API keys for the free sources. Just run and read.

Platform Matrix

Last30days splits its 13 sources into two tiers:

And the free tier alone covers the most useful sources for tech research: Reddit, HN, GitHub, and Polymarket. I ran my first few queries without touching any config file. For X and YouTube, I added keys after — the skill walks you through it with a last30days config command.

What’s New in last30days-skill v3?

So the v3 release (just weeks ago) added several features that changed the feel from “interesting experiment” to “daily driver worthy”:

Honestly, the “Best Takes” feature caught me off guard. And I ran a query on “Cursor IDE vs Windsurf” and it surfaced a Reddit comment from someone who’d used both for a month — along with a blog post comparing their tab-completion latency. And that’s exactly the kind of signal I’d spend 20 minutes hunting for manually.

How to Install (3 Ways)

And installation is refreshingly simple:

I went with npx skills last30days — no Node.js version issues, no dependency hell. And the skill was live in about 20 seconds. For a project with 621 commits and 33 releases, that’s impressive.

How It Stacks Up Against Alternatives

Sure, ChatGPT can search the web, but it hits Reddit inconsistently and misses HN entirely. And Gemini has YouTube but nothing else. Yet Claude has no native search. Last30days fills a real gap — and the entity resolution + cross-platform parallel search is something none of them do. And if you’re optimizing your AI workflow too, check out our Headroom review — it’s a complementary tool that cuts API costs on Claude Code.

Who Should Use This

• Tech researchers / analysts — tracking a competitor’s GitHub activity, HN traction, and X presence in one place
• Open source maintainers — monitoring what people are saying about your project across communities
• AI/ML developers — keeping up with the firehose of new models, papers, and tools (pair with Headroom for cost-efficient Claude Code, or our Open Notebook review for a different research approach)
• Sales / BD people — doing quick background on prospects (the GitHub + X + Reddit combo is gold for discovery calls)
• Investors / analysts — getting a pulse check on a startup or category without asking anyone

Who Should Skip It

• People who just need Google search — this is a complement, not a replacement
• Non-technical users who can’t configure API keys — the free tier is useful but limited
• Anyone looking for a packaged SaaS product — this is a Claude Code / Codex skill, not a web app

The Bottom Line

Still, Last30days-skill is one of those tools that makes you wonder why nobody built it sooner. The idea is simple — search what people are saying, not what pages exist — but the execution takes serious engineering. Entity resolution, 13-platform parallel crawling, cross-source dedup, and a clean CLI interface. v3’s “Best Takes” and clustering turned it from a neat experiment into something I’ll keep using.

But the free tier is genuinely useful out of the box. So add API keys for X and YouTube and it becomes surprisingly powerful. At 27.6k stars and growing, this one’s not going anywhere.

So here’s my verdict: Install it. Run npx skills last30days on your own project or a competitor. See what comes back. The first time it surfaces a Reddit thread you would’ve missed, a GitHub issue you didn’t know about, or a YouTube tutorial you should’ve watched — you’ll get it.

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