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This is pure gold for anyone using Hermes Agent. 10 GitHub skills + tools that make Hermes instantly more useful. Pick the tools for each bottleneck: research, writing, web reading, PDFs, coding, security, memory, and design. 01 Agent Reach → research YouTube, X, Reddit, GitHub, and the web 02... Humanizer → remove robotic AI writing patterns 03 Defuddle → turn cluttered web pages into clean readable content 04 Caveman → make agent responses shorter and more direct 05 PDF Creator → turn Markdown into styled PDFs with visual checks 06 Oh My Hermes → split large projects across specialized agents 07 Agent Skills → give coding agents a plan, build, test, and review process 08 Community Cybersecurity Skills → choose individual defensive playbooks for authorized reviews 09 Codebase Memory MCP → build a persistent map of a large codebase 10 Make Interfaces Feel Better → improve typography, spacing, interactions, and UI polish Save this + read the article below for a more in depth guid and to copy my exact setup! ↓show more

Brandon Chin

218,994 subscribers

67,178 views • 28 days ago •via X (Twitter)

Science & Technology Education

Anya Rossi• Live Now

Private livecam show

21 Comments

Milian27 days ago

Check out Umanitek's security plugin, was just added to Hermes.

ʟɪsᴀ28 days ago

Me adding more skills to Hermes

BentoBoi28 days ago

Beefing up Hermes into a beast!

ʟɪsᴀ28 days ago

It’s fun

Harley Lewis Foote27 days ago

Most people only ever need two of those ten, the rest is config you'll never touch.

BentoBoi27 days ago

What 2

swapcherry27 days ago

caveman one is real

BentoBoi27 days ago

I like caveman

Hermes Agent Super-Intel28 days ago

Great stack for Hermes bottlenecks. Defuddle plus a codebase-memory MCP looks like the combo that stops agents re-reading the same pages on long runs. Which of the 10 do you leave on by default?

BentoBoi28 days ago

Like all of them esp the coding ones and agent reach

Haley28 days ago

Hundreds of hours testing down to these 10 is careful work

BentoBoi28 days ago

I like these 10

ben27 days ago

W graphic

BentoBoi27 days ago

W Ben

Ted | unfair.so27 days ago

great thing you can do that all in one place called tiny fish

逸风·666 | 🎒27 days ago

Humanizer and Defuddle alone save so much friction

BentoBoi27 days ago

Yea I use humaizer a good amt

Âxel ⟢27 days ago

Pure gold

BentoBoi27 days ago

My g

Bren Luengo ♥ Cool Guides27 days ago

Caveman and Defuddle are the two I'd install first, everything else is situational.

lost_user27 days ago

do i need A $5000 pc for this to run

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Sloemo

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rari🈀

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Hermes Agent Super-Intel

46,378 views • 2 months ago

this is where GPT-6 Astra is getting f...cking illegal 10 open-source GitHub repos I'd use to build the system around the model 01 PydanticAI ▸ → typed agents + structured outputs 02 LangGraph ▸ → state + long-running agent workflows BUILD THE AGENTS 03 Agno ▸ → agents + teams + workflows 04 smolagents ▸ → agents that execute through code GIVE THEM MEMORY 05 Mem0 ▸ → persistent agent memory 06 Graphiti ▸ → temporal knowledge graphs GIVE THEM A RUNTIME 07 E2B ▸ → isolated sandboxes for AI-generated code 08 Daytona ▸ → infrastructure for running agent workloads TEST EVERYTHING 09 DeepEval ▸ → evals for LLM systems 10 Langfuse ▸ → traces + evals + production monitoring the flow: request → agent → memory → tools → runtime → eval → retry → deploy that's the part people underestimate the model is one folder the system around it is the product ⭣

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Lummox

43,873 views • 1 month ago

WTF Broooooooooooooooooo... this is HEAVEN for anyone using AI to build UIs. 🤯 It's a massive collection of agentic UI skills that help AI coding agents build better-looking, more polished interfaces. You can explore skills across different categories like colors, architecture, design systems, frontend development, and much more. Let's be honest... AI can generate some serious UI slop by default. But with the right skills and a little design knowledge, you can turn that slop into an absolute MASTERPIECE. 🔥 If you're a developer, designer, or vibe-coder, you NEED to bookmark this :) Follow me for more amazing AI, Coding & Web Dev insights 💎

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26,089 views • 19 days ago

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Ridark

15,483 views • 15 days ago

this GPT-6 Astra setup is getting f...cking dangerous 8 GitHub repos for turning one coding agent into an entire engineering team 01 OpenHands ▸ → autonomous software development 02 SWE-agent ▸ → issue → inspect repo → patch → test BUILD THE WORKERS 03 Aider ▸ → pair programming directly inside your repo 04 Cline ▸ → plan → edit → terminal → browser 05 Roo Code ▸ → specialized coding agent modes GIVE THEM A WORKSPACE 06 E2B ▸ → isolated sandboxes for parallel execution 07 Daytona ▸ → reproducible environments for agent workloads CHECK THE WORK 08 SWE-bench ▸ → test agents against real GitHub issues the swarm: issue → planner → split tasks → parallel agents → patches → tests → critic → merge one agent fixes the backend another handles the frontend another runs tests while a critic checks the diff that's where GPT-6 Astra gets interesting not one AI writing more code an entire software team running inside the repo ⭣

this GPT-6 Astra setup is getting f...cking dangerous 8 GitHub repos for turning one coding agent into an entire engineering team 01 OpenHands ▸ → autonomous software development 02 SWE-agent ▸ → issue → inspect repo → patch → test BUILD THE WORKERS 03 Aider ▸ → pair programming directly inside your repo 04 Cline ▸ → plan → edit → terminal → browser 05 Roo Code ▸ → specialized coding agent modes GIVE THEM A WORKSPACE 06 E2B ▸ → isolated sandboxes for parallel execution 07 Daytona ▸ → reproducible environments for agent workloads CHECK THE WORK 08 SWE-bench ▸ → test agents against real GitHub issues the swarm: issue → planner → split tasks → parallel agents → patches → tests → critic → merge one agent fixes the backend another handles the frontend another runs tests while a critic checks the diff that's where GPT-6 Astra gets interesting not one AI writing more code an entire software team running inside the repo ⭣

Lummox

37,036 views • 1 month ago

Moving your Coding Agents to the Cloud (ft. Codex Desktop & GPT-6 Astra) 00:00 The limitations of running agents locally 01:13 Parallel development and Git worktrees 02:03 Moving your coding agent to a remote machine 02:25 Introducing Railway Cloud Agents 02:52 Installing the CLI and signing in 03:03 Configuring your project and default agent 03:42 Bringing your agent skills 04:24 Connecting the Codex desktop app 04:48 How SSH configuration works 05:10 Adding the SSH connection in Codex 05:39 Creating a remote project and starting a chat 06:09 Long-running tasks and remote execution 06:26 ChatGPT mobile access 07:03 Using Railway from your coding agent 07:15 Orchestrating multiple cloud agents

Moving your Coding Agents to the Cloud (ft. Codex Desktop & GPT-6 Astra) 00:00 The limitations of running agents locally 01:13 Parallel development and Git worktrees 02:03 Moving your coding agent to a remote machine 02:25 Introducing Railway Cloud Agents 02:52 Installing the CLI and signing in 03:03 Configuring your project and default agent 03:42 Bringing your agent skills 04:24 Connecting the Codex desktop app 04:48 How SSH configuration works 05:10 Adding the SSH connection in Codex 05:39 Creating a remote project and starting a chat 06:09 Long-running tasks and remote execution 06:26 ChatGPT mobile access 07:03 Using Railway from your coding agent 07:15 Orchestrating multiple cloud agents

Railway

82,044 views • 2 months ago

this is pure f*cking treasure 15 GitHub projects with 1.21M combined stars that can form a real agent stack specs. memory. web data. documents. context. sandboxes. monitoring. video 01 hermes-agent ▸ 02 OpenSpec ▸ 03 caveman ▸ 04 Scrapling ▸ 05 Docling ▸ 06 PageIndex ▸ 07 mem0 ▸ 08 headroom ▸ 09 Daytona ▸ 10 TrendRadar ▸ 11 Fabric ▸ 12 spec-kit ▸ 13 hyperframes ▸ 14 OpenMontage ▸ 15 AI Engineering Hub ▸ the loop: define the job → collect the evidence → parse the docs → save the memory → compress the context → run the code safely → watch what changes → ship the output the entire stack is open source save this to build your own business with the help of an AI employee ⭣

this is pure f*cking treasure 15 GitHub projects with 1.21M combined stars that can form a real agent stack specs. memory. web data. documents. context. sandboxes. monitoring. video 01 hermes-agent ▸ 02 OpenSpec ▸ 03 caveman ▸ 04 Scrapling ▸ 05 Docling ▸ 06 PageIndex ▸ 07 mem0 ▸ 08 headroom ▸ 09 Daytona ▸ 10 TrendRadar ▸ 11 Fabric ▸ 12 spec-kit ▸ 13 hyperframes ▸ 14 OpenMontage ▸ 15 AI Engineering Hub ▸ the loop: define the job → collect the evidence → parse the docs → save the memory → compress the context → run the code safely → watch what changes → ship the output the entire stack is open source save this to build your own business with the help of an AI employee ⭣

beamnxw ./

159,360 views • 1 month ago

HERMES JUST FIXED THE BIGGEST PROBLEM WITH BROWSER AGENTS Most AI agents still click around websites like confused interns. This new setup gives them the map. Hermes + → Hermes now connects to Browserbase’s new Browse hub → Browse launched with 100+ browser skills → Each skill is a plain-text playbook for a specific website or task → Your agent can search, preview, and install skills inside Hermes Why This Matters: ✓ Less random clicking ✓ Fewer timeouts ✓ Better form filling ✓ More reliable website navigation ✓ Skills can be edited, reused, bundled, and shared The Real Stack: → Hermes runs the agent → Browserbase handles cloud browser infrastructure → gives the agent site-specific skills → Vision helps when pages get weird → Bundles let you load repeat workflows faster The killer detail: If a website breaks, you don’t wait for the model to magically improve. You update the skill. Now your agent gets better forever. That’s the difference between an AI tool and an actual agent system.

HERMES JUST FIXED THE BIGGEST PROBLEM WITH BROWSER AGENTS Most AI agents still click around websites like confused interns. This new setup gives them the map. Hermes + → Hermes now connects to Browserbase’s new Browse hub → Browse launched with 100+ browser skills → Each skill is a plain-text playbook for a specific website or task → Your agent can search, preview, and install skills inside Hermes Why This Matters: ✓ Less random clicking ✓ Fewer timeouts ✓ Better form filling ✓ More reliable website navigation ✓ Skills can be edited, reused, bundled, and shared The Real Stack: → Hermes runs the agent → Browserbase handles cloud browser infrastructure → gives the agent site-specific skills → Vision helps when pages get weird → Bundles let you load repeat workflows faster The killer detail: If a website breaks, you don’t wait for the model to magically improve. You update the skill. Now your agent gets better forever. That’s the difference between an AI tool and an actual agent system.

Julian Goldie SEO

43,472 views • 5 months ago

GitHub is becoming f...cking dangerous for AI agents 10 open-source repos that turn one powerful model into an entire autonomous system 01 LangGraph ▸ → stateful agent orchestration 02 PydanticAI ▸ → typed agents + structured outputs BUILD THE CONTROL LAYER 03 Mastra ▸ → agents + workflows + memory 04 Agno ▸ → multi-agent systems GIVE IT CONTEXT 05 Cognee ▸ → data → knowledge → agent memory 06 Graphiti ▸ → knowledge that changes over time GIVE IT A COMPUTER 07 Browser Use ▸ → AI agents that operate websites 08 E2B ▸ → isolated environments for execution KEEP IT ALIVE 09 Langfuse ▸ → traces + evals + observability 10 DeepEval ▸ → test agent behavior before production the architecture: model → context → planner → memory → tools → execution → eval → retry that's the shift happening on GitHub the best repos aren't making another chatbot they're building everything the model needs around it ⭣

GitHub is becoming f...cking dangerous for AI agents 10 open-source repos that turn one powerful model into an entire autonomous system 01 LangGraph ▸ → stateful agent orchestration 02 PydanticAI ▸ → typed agents + structured outputs BUILD THE CONTROL LAYER 03 Mastra ▸ → agents + workflows + memory 04 Agno ▸ → multi-agent systems GIVE IT CONTEXT 05 Cognee ▸ → data → knowledge → agent memory 06 Graphiti ▸ → knowledge that changes over time GIVE IT A COMPUTER 07 Browser Use ▸ → AI agents that operate websites 08 E2B ▸ → isolated environments for execution KEEP IT ALIVE 09 Langfuse ▸ → traces + evals + observability 10 DeepEval ▸ → test agent behavior before production the architecture: model → context → planner → memory → tools → execution → eval → retry that's the shift happening on GitHub the best repos aren't making another chatbot they're building everything the model needs around it ⭣

Lummox

168,963 views • 1 month ago

Hermes keeps finding ways to make agent work cost fewer tokens. And the latest cuts are automatic. Recent changes reduced recurring context overhead across several parts of Hermes: • Desktop’s model-facing UI tool schema dropped 41% • Memory and Skills guidance went from 537 to 255 tokens • Desktop’s always-served guidance was trimmed again • roughly another 727 tokens were removed from recurring platform and steering instructions That matters whether you use Hermes for coding, research, content, automations, business workflows, or long-running agent tasks. Every token spent explaining tools, Skills, memory, or platform behavior is context that isn’t being spent on your actual work. Hermes was already notably lean here. Composio recently put that to the test using the same K3 model across competing agent harnesses. Hermes came out among the strongest on completion while also posting the lowest average cost per task in their comparison. And the Hermes team clearly isn’t treating that as good enough. They continue cutting token waste across tools, Skills, memory, platform instructions, compression, and other parts of the agentic process. The goal is simple: more of your context goes toward the work you actually asked Hermes to do. Clip via Tom

Hermes keeps finding ways to make agent work cost fewer tokens. And the latest cuts are automatic. Recent changes reduced recurring context overhead across several parts of Hermes: • Desktop’s model-facing UI tool schema dropped 41% • Memory and Skills guidance went from 537 to 255 tokens • Desktop’s always-served guidance was trimmed again • roughly another 727 tokens were removed from recurring platform and steering instructions That matters whether you use Hermes for coding, research, content, automations, business workflows, or long-running agent tasks. Every token spent explaining tools, Skills, memory, or platform behavior is context that isn’t being spent on your actual work. Hermes was already notably lean here. Composio recently put that to the test using the same K3 model across competing agent harnesses. Hermes came out among the strongest on completion while also posting the lowest average cost per task in their comparison. And the Hermes team clearly isn’t treating that as good enough. They continue cutting token waste across tools, Skills, memory, platform instructions, compression, and other parts of the agentic process. The goal is simple: more of your context goes toward the work you actually asked Hermes to do. Clip via Tom

Hermes Release Watch

20,037 views • 2 months ago

HERMES AGENT IS STARTING TO FEEL LIKE A DIFFERENT CATEGORY OF AI TOOL. Not just a chatbot or a coding assistant, but a server side agent that remembers workflows, builds skills, and gets better the more you use it.

HERMES AGENT IS STARTING TO FEEL LIKE A DIFFERENT CATEGORY OF AI TOOL. Not just a chatbot or a coding assistant, but a server side agent that remembers workflows, builds skills, and gets better the more you use it.

0xMarioNawfal

49,497 views • 6 months ago