标签：#hermesagentchallenge

We are thrilled to announce the winners of the Hermes Agent Challenge! Over the past few weeks, the...

Submission Template Challenge: Build With Hermes Agent Project: CompliScore AI compliance health checks for Indian startups Repo/live-demo: https://github.com/nehaprasad-dev/hermes-scout What I built CompliScore gives Indian startup founders a compliance score out of 100 in under a minute - overdue GST filings, MCA returns, penalty exposure, and a plain-English action plan. The upgrade for this challenge: I replaced the one-shot Groq summary with a Hermes Agent reasoning loop that plans an investigation, calls deterministic compliance tools, and writes a prioritized report - with a collapsible agent trace so judges can see the agentic work. Why an agent loop fits here Compliance analysis is conditional. A company with overdue GST needs a filing-calendar deep dive; one with active notices needs notice triage; a clean company needs a light touch. A single prompt guesses all of this at once. An agent that calls tools based on what it finds produces tighter, grounded reports. Hermes Agent integration Scan → computeHealth (deterministic score) → Hermes Agent loop (plan → tool calls → report) → agent trace in UI ↓ on failure Groq one-shot → static fallback Four tools exposed to Hermes (scores never hallucinated): Tool Purpose score_company Canonical score, risk level, pending tasks estimate_penalty GST / MCA / notice penalty breakdown filing_calendar GSTR-3B, GSTR-1, MCA deadlines (90-day horizon) classify_notices Severity labels for pending government notices The agent runs over Hermes's OpenAI-compatible /chat/completions API with function calling — self-hostable via vLLM, LM Studio, Ollama, etc. Transparency: Every successful agent run returns an agentTrace — plan steps, tool names, compact result previews — rendered in a collapsible panel under the AI action plan. Reliability: Three-tier fallback (Hermes → Groq → static). Scans never break. Tech stack Next.js 16 (App Router), TypeScript, Tailwind v4 Hermes Agent (OpenAI-compatible tool-calling loop) Groq fallback ( ll

My Hermes Agent Mac just received a signed, secure and monitored message from a Claude Managed Agent, and got a reply! - A solution for long runtime work, A2A ID and security. What I Built A solution that enables two agents with different owners on a shared identity network, a Hermes and a Claude Managed Agent (Claude platform) talking to each other across the internet. Every message is Ed25519 signed by the sender. Every receiver verifies the signature against a public registry and shows a blue tick before acting. Continuous Agent Monitoring A handshake proves identity once but agents in a long runtime world don't trade a single message, they hold ongoing, autonomous conversations across hours, days, and many turns. Keys get compromised, agents get swapped, a colleagues behaviour drifts, all after the initial check. ZipViz re-verifies every message signature, registry chain, freshness, and watches the stream over time for behavioural anomalies. Trust is re-earned on every turn. So this agent was who it claimed this morning," but "this agent is who it claims, on this message, right now." The demo agents on the ZipViz network: mac-her.smc.viz — Hermes Agent on my Mac Mini brendan-clau.smc.viz — Claude Agent in Cloud When Mac sends a message to brendan-clau, Mac's private key signs it. Brendan-clau verifies the signature against ZipViz's registry, and checks it just ran with the MCP (algorithm, key fingerprint, registry chain, timestamp), then replies signed. Same flow in reverse. Same flow Hermes ↔ Hermes , or Claude ↔ Openclaw . The runtime doesn't matter; the identity layer does. "I received a signed message from mac-her.smc.viz". Reads back the four checks it just ran: ✓ Algorithm: Ed25519 ✓ Key fingerprint: ab52afe... matches registry ✓ Registry chain: mac-her → smc.viz → .viz (Handshake) all resolved ✓ Timestamp: 2026-05-31 11:18 UTC, fresh Demo Hermes continuous monitoring and verification with A2A Protocol Code One MCP server: [ zipviz-mcp ] https://www.npmjs.

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent What I Built A small backstory on my coding origins I learnt coding by secretly studying from a python book pdf on my Computer Scientist father's work laptop. That very day I wrote a program that inputs a user's name and prints: "You are mad {name}!" and had a lot of fun pranking my brother using that script. Since then, there have been very few moments where coding felt as magical, because the more you understand syntax, the more you understand the magic underneath your code. That is, until you meet a genius piece of magic such as Hermes. My app idea I built a Fitness coach inside Hermes that learns and adapts based on your daily feedback. According to your goals, performance, and time allocated, it adjusts your current plan. For the purpose of this hackathon I tested it via terminal ui (tui) but I plan to release the polished version on chat apps such as telgram and whatsapp for painless daily checkins. Demo Code Github link My Tech Stack Hermes + node.js. Kept it simple for this quick dive. How I Used Hermes Agent Building an AI application that feels truly personal requires more than just a clever prompt; it requires state, memory, and the ability to adapt over time. During a recent hackathon, I set out to build an autonomous AI fitness coach. Not just a chatbot that spits out generic workout templates, but a system that onboards a user, sets a multi-month timeline, generates habit blocks, and adjusts daily based on feedback. To achieve this, I used Hermes , an agentic framework designed for stateful, long-running applications. Here is a breakdown of how I built it, the challenges faced, and why Hermes was the perfect tool for the job. Why This App is a Perfect Fit for Hermes Most LLM interactions are stateless. You ask a question, you get an answer, and the session ends. A fitness journey, however, is a deeply stateful process. It spans weeks or months and requires constant recalibrat

This is a submission for the Hermes Agent Challenge Agentic Web3: Automating Blockchain Workflows with Hermes Tags: #hermesagentchallenge , #web3 , #agents , #solana The blockchain industry has spent the last decade building decentralized, permissionless infrastructure. However, the user experience layer interacting with this infrastructure remains overwhelmingly manual. Decentralized applications (dApps) require users to constantly monitor markets, parse complex data, and manually sign every transaction. The next evolution of Web3 isn't just about faster blockchains; it is about autonomous execution. By integrating large language models and agentic frameworks with smart contracts, we can transition from a paradigm of manual execution to intent-based autonomy . In this article, we will explore how to bridge the gap between AI and decentralized networks by automating blockchain workflows using the Hermes Agent framework. We will look at the architecture of an on-chain agent, how it reads and writes to a network, and how high-performance environments like Solana are making these agentic experiences viable. The Paradigm Shift: From Passive Wallets to Active Agents Currently, most AI in Web3 is limited to read-only analytical tools—chatbots that can summarize a smart contract or pull token prices from an API. While useful, these are fundamentally passive systems. An active agent is different. Powered by a framework like Hermes Agent, an active agent can: Observe: Continuously monitor on-chain events via RPC nodes or webhooks. Reason: Use its LLM core to interpret those events against a set of user-defined goals or risk parameters. Act: Formulate a transaction, sign it via a secure wallet environment, and broadcast it to the network. This opens up massive possibilities. Imagine an agent that automatically manages your decentralized finance (DeFi) positions, rebalancing a portfolio based on yield changes across different protocols. Or consider fully on-chain gaming, where

This is a submission for the Hermes Agent Challenge , under the Write About Hermes Agent prompt. I've spent the last week testing AI memory failure modes in a public evaluation harness. That work changed how I read agent memory systems. This is a writing submission, not a build submission. I did not build a Hermes Agent project for this challenge. I am writing from the perspective of someone testing how memory failures show up once agents can act. So when I look at Hermes Agent, the question I care about is not only: Can the agent remember useful things? The harder question is: When memory conflicts, which memory is allowed to govern the agent's action? That distinction matters. Hermes Agent is interesting because it is not just a chat interface. Its documentation describes an open-source agentic system with tool use, project context, persistent memory, skills, browser automation, checkpoints, delegation, scheduled tasks, and multiple memory providers. That is exactly the kind of system where memory stops being a convenience feature and starts becoming part of the agent's operating boundary. If an agent can run tools, edit files, browse, delegate work, schedule tasks, and remember across sessions, then memory is no longer just "context." Memory becomes governance. The Memory Problem I Would Watch For In a simple chatbot, bad memory is annoying. In an agent, bad memory can become operational. The failure mode is not only that the agent forgets something. Sometimes the more dangerous failure is that it remembers the wrong thing too confidently. A memory can be: relevant but stale, relevant but low-authority, relevant but superseded, relevant but only context, relevant but not allowed to determine the action. That is the distinction my own tests kept running into. Retrieval systems are usually good at answering: What memory is closest to the user's request? But safety often depends on a different question: What memory is allowed to decide what the agent should do? Thos

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent Hello, DEV friends! 👋 If you have been exploring the world of Artificial Intelligence lately, you have probably heard a lot of buzz about "AI Agents." But what does it actually feel like to build with one? Today, I want to share my personal experience working with Hermes Agent . I used it to build a smart assistant called the Alpha-Dairy Quant Pipeline —a system that helps track and make sense of food market data. ( https://github.com/HopeBestWorld/alpha-dairy-pipeline ) Whether you are an expert coder or just curious about AI, I hope this friendly guide inspires you to try building an agent of your own! What is Hermes Agent, Anyway? Think of a standard AI as a helpful chatbot that answers questions when you ask them. An AI Agent , on the other hand, is more like a proactive assistant. You give it a big goal, and it sits down, makes a step-by-step plan, uses digital tools, runs code, and checks its own work until the job is done. For my project, I wanted to track market prices for three major dairy products: Cheddar Blocks, Butter, and Dry Whey. Instead of doing all the math and graphing by myself, I let Hermes Agent take the wheel. The Magic of Multi-Step Reasoning The coolest part of working with Hermes Agent is watching it "think". When I asked my agent to look at our data database ( market_intelligence_3.db ) and find the best trading strategy,it followed a beautiful planning loop: Checking the Files: It looked at our setup files ( tickers.yaml and requirements.txt ) to make sure all its tools were ready. Running the Math: It triggered a Python program ( backtest_engine.py ) to study weekly market history. Making Decisions: It realized that Dry Whey was way too wild and risky to trade right now, so it intelligently gave it a 0% safety rating and put the focus on Cheddar and Butter instead. Drawing and Sharing: It automatically drew a beautiful performance chart ( backtest_analysis.png

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent The Problem Nobody Talks About Every time the Reserve Bank of India publishes a circular, somewhere inside an Indian bank, a compliance officer opens a PDF. They read it. They try to figure out what it means for their institution specifically. They write a summary email. They forward it to five department heads. They chase those department heads for two weeks to confirm it's been actioned. They build a spreadsheet to track all of this. And then the next circular drops and the cycle starts again. RBI publishes hundreds of circulars a year. SEBI publishes more. MCA publishes more still. Compliance teams at Indian banks are drowning — not because they're incompetent, but because the volume of regulatory output has outpaced any reasonable human ability to track it manually. The fine for missing a deadline isn't a polite reminder. It's a penalty notice. This is the problem I built for. What I Built RBI Regulatory Digest Agent — an autonomous multi-step agent powered by Hermes Agent that monitors RBI and SEBI publication feeds, reads every new circular, extracts structured action points from the regulatory text, and delivers a formatted intelligence report to compliance teams automatically. No human reads the circular first. No human decides what's important. No human routes it to the right department. The agent does all of that. The pipeline RBI/SEBI feeds → new circular detected → full text extracted → LLM analysis → structured action points → risk classification → HTML dashboard generated → email delivered Every action point extracted contains: What needs to be done — specific and actionable, not a vague summary Deadline — parsed from the circular text Responsible department — Credit, Compliance, Treasury, Operations, IT, Legal Evidence required — what documentation confirms completion Priority — Critical (overdue or <7 days), High, Medium, Low From a new circular to a structured compliance b

This is a submission for the * Hermes Agent Challenge * : Build With Hermes Agent What I Built CareSync is a local health memory agent for student caregivers. I'm Naomi, a 21-year-old engineering student. Between classes I help care for my grandma Kamala (78, high blood pressure, type 2 diabetes). I often forgot details from previous doctor visits, missed symptom patterns, and struggled to hand over care information to family members. CareSync solves that with longitudinal memory. Symptoms, meals, vitals, medications, and reports are stored in a local SQLite database. The CLI can search history, identify patterns, and generate appointment summaries. Hermes Agent exposes the same capabilities through natural language. What you get: One-line logging: ./caresync add "dizzy spell after lunch" Pattern search across weeks of history Medication tracking and report imports Doctor questions, appointment briefs, and handoff notes Full audit log of agent actions 7 Hermes skills mapped to real terminal commands Local-first design with no cloud storage CareSync is not medical advice. It helps caregivers observe, organize, and prepare. Demo The demo walks through: Logging a new symptom Searching health history for recurring patterns Generating doctor questions and appointment briefs Using Hermes in natural language to query past events Reviewing the audit trail of actions taken Example commands shown in the demo: ./caresync search --person Kamala --query dizziness ./caresync timeline --person Kamala ./caresync questions --person Kamala ./caresync brief --person Kamala --days 14 ./caresync chat "has grandma been dizzy before?" Code Repository: https://github.com/Byte-Sized-Brain/caresync Architecture My Tech Stack Hermes Agent Python 3.12 SQLite agentskills.io skill framework Terminal-based CLI Nous Portal How I Used Hermes Agent CareSync uses Hermes Agent as the orchestration layer between natural language and real caregiving workflows. I created 7 Hermes skills that map directly

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent What if the biggest limitation in AI today isn't reasoning, model size, or context windows? What if it's memory? Every morning, millions of people open ChatGPT, Claude, Gemini, or another AI assistant and start a conversation. The AI seems intelligent. It writes code. It explains concepts. It helps brainstorm ideas. It can even help design an entire software architecture. Then the conversation ends. Tomorrow? It remembers nothing. Imagine hiring a senior engineer who forgets everything at the end of every workday. Every morning you would need to explain: What your company does How your product works Which technologies you use Why certain decisions were made What happened yesterday Nobody would call that employee productive. Yet this is exactly how most AI systems operate. And it reveals something important: Most AI agents aren't actually learning from experience. They're simply reasoning over whatever context happens to be available right now. That distinction may define the future of agentic AI. Because the next generation of AI won't just need better reasoning. It will need memory. And that's where Hermes Agent becomes interesting. The Strange Reality of Modern AI The public perception of AI often looks like this: User → AI → Intelligence But the reality is closer to this: User → Context Window → AI → Response The AI only knows what exists inside its current context. Once that context disappears, so does most of its understanding. This is why many AI experiences feel surprisingly repetitive. You spend 30 minutes explaining your project. The AI finally understands your goals. The answers become better. The recommendations become more relevant. Then the session ends. The next conversation starts from scratch. Not because the model isn't powerful. But because the knowledge never became persistent. Context Windows Are Not Memory A context window is not memory. It is temporary working space.

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent Most AI agents have a dirty secret: they forget everything the moment the session ends. You explain your project once. Then again next time. And again. The agent never gets better at your workflow — it just stays a general-purpose tool that happens to be smart. Hermes Agent is built differently. It ships with two systems that together form something closer to a genuine long-term memory: a Skills System and a Persistent Memory layer. This post digs into how they actually work — not the marketing summary, but the mechanics. The Problem With Stateless Agents Before getting into Hermes, it's worth understanding what problem this solves. Standard LLM-based agents operate inside a context window. Everything the agent knows during a session lives in that window. When the session ends, it's gone. The next time you open a conversation, you're talking to an agent with no memory of you, your codebase, your preferences, or the workflows you've developed together. Some tools patch this with naive "memory" — they dump a text blob of past conversations into the system prompt. This works up to a point, but it's not selective, it gets expensive as context grows, and it doesn't help the agent get better at tasks — just recall facts. Hermes takes a different approach with two distinct systems serving different purposes. System 1: The Skills System (Procedural Memory) Skills in Hermes aren't plugins you install. They're on-demand knowledge documents — markdown files the agent loads when it needs them, and more importantly, creates on its own when it discovers something worth remembering. The SKILL.md Format Every skill is a structured markdown file with a YAML frontmatter header: --- name : deploy-runbook description : Our deployment runbook — services, rollback, Slack channels version : 1.0.0 metadata : hermes : tags : [ deployment , runbook , internal ] requires_toolsets : [ terminal ] --- # Deploy Runbook

"Stop summarizing the noise. Start executing it." Tsundoku Slayer is an autonomous agentic system powered by Hermes Agent that overnight patrols your unread tabs, mercilessly filters out 90% of the information overload, and saves only the information capable of killing your current blocker. 🎯 The Problem While debugging a painful Streamlit IndexError, I realized my real issue wasn't a lack of information—it was too much information. I had documentation, API feeds, tech news, and bookmarks all competing for my limited focus. Most AI tools try to "summarize" everything, which ironically generates more text to read and increases cognitive load. I didn't need another summarizer. I needed an autonomous agent capable of deciding what NOT to read right now. 🧠 How Hermes Agent Drives the Workflow This project doesn't just scrape webs; Hermes Agent acts as a high-conviction decision maker. It coordinates the entire workflow by running a multi-step reasoning loop overnight. ⚙️ The Agent Workflow Retrieve: Fetches unread article content via web scraping tools. Compare: Ingests and cross-examines the content against the user's active, real-time problem context (e.g., specific stack traces). Reason: Analytically evaluates the true relevance of the article to the current blocker. Verdict: Produces a high-conviction binary choice: SAVE or EXECUTE. Justify: Generates a crisp, logical explanation for why an article was terminated or spared. Synthesize: Automatically crafts an immediately applicable Python/Streamlit code patch for saved items. 📋 Example Outcome: Focus in Action Here is a real-world scenario of how Hermes Agent processes a chaotic backlog when you are stuck on a critical crash: Current Blocker: IndexError: list index out of range inside a Streamlit dialogue array loop. Unread Queue (Input): Streamlit st.status Documentation ➔ EXECUTE (Irrelevant UI reference) General Python Tag Feed ➔ EXECUTE (Too broad, pure noise) Tech News Flash ➔ EXECUTE (Complete distraction) Str

This is a submission for the Hermes Agent Challenge TL;DR — Type a one-line bill topic. Twenty-five Hermes agents (1 Speaker, 19 ministries, 5 parties) run a full Polish legislative session in 2 minutes. Vote tally, social impact, party tweets — and a side-by-side "current law vs proposed amendment" with every clause cited to a real statute. Built on delegate_task for parallel ministry consultation. 🌐 Live: https://web-production-53027.up.railway.app/ 🎥 Walkthrough: https://www.loom.com/share/92cdac7da31c471088a4e569b0cfe1ed 📦 Repo: https://github.com/monsad/ai-politics (MIT) What I Built Watch a politician debate a new tax law on TV. They argue whether it's fair, whether it'll work, whether the other side is lying. Nobody ever shows you the diff — which paragraph of which statute actually changes, and from what to what. The conversation is theatre on top of an invisible legal document. So I built the theatre AND the legal document. Virtual Parliament is a multi-agent simulation of the Polish Sejm. You type something like "four-day work week" or "flat income tax" , and 25 Hermes agents run a full legislative session: 🎯 Marszałek (Speaker) — the orchestrator. Classifies the topic. Picks 2–3 ministries via delegate_task in parallel . Reads their findings. Routes the bill to a party debate. 🏛️ 19 ministry experts — Finance, Climate, Labour & Social Policy, Justice, … Each returns a structured analysis: legal finding · budget impact · top 3 risks · recommendation . Every claim cites a real statute via PageIndex RAG. 🗳️ 5 party agents — KO, PiS, TD, Konfederacja, Lewica. Each one carries the real party's seat count (157, 194, 65, 18, 26 — totalling 460), policy positions and rhetorical style. First reading. Second reading with rebuttals. 📊 Vote — weighted by seats. >230 passes. 📜 Draft bill — produced with explicit "Article 129 §1 of the Labour Code **is amended to read …" diffs against current law. The frontend surfaces the diff as a Current law vs proposed change panel

A practical, slightly opinionated Hermes Agent use case: an always-on chief of staff that remembers your work, schedules itself, uses tools, and reports back with receipts.

This is a submission for the Hermes Agent Challenge: Write About Hermes Agent Hermes Agent...

A $0 multi-model decision agent: three LLMs debate, Hermes judges, and it learns who to trust.

This is a submission for the Hermes Agent Challenge . I have been playing with different AI agents for a while now. Most of them feel like clever chatbots that forget everything the moment the conversation ends. Then I tried Hermes Agent from Nous Research a few weeks back. It actually feels different. It grows with you. That stuck with me. What Hermes Agent is Hermes is an open-source autonomous agent that runs on your own server or VPS. It is not locked to one IDE or one API. You install it once, pick any model you like, and it starts building its own memory and skills over time. The big idea is a built-in learning loop. When it solves something useful, it can create a reusable skill in Markdown, improve it later, and pull it back when needed. It also keeps persistent memory across sessions so it slowly builds a picture of how you work and what your projects look like. I set it up on a cheap VPS with a simple curl command. The installer is straightforward. After that I ran hermes setup and connected it to a model I already had access to. Within minutes I could chat with it from Telegram while it worked in the background on the server. That alone felt freeing. My experience so far I started simple. I asked it to monitor a few GitHub repos and send me a daily summary. It remembered the context from previous days without me repeating instructions. Over a week it created a couple of small skills on its own for formatting those reports nicely. I also used it for research tasks. It can search the web, browse pages, and chain steps together. What surprised me was how it handled follow-ups. Instead of starting fresh each time, it referred back to earlier parts of our conversation. That made longer projects feel more natural. The multi-platform support is practical. I switch between CLI on my laptop and Telegram on my phone. The agent just continues wherever I left it. Why it matters Most agent frameworks still feel stateless. You get good results in the moment but lose th

This is a submission for the Hermes Agent Challenge What I Built Onyx is an autonomous infrastructure operator running 24/7 on my droplet. He manages my entire stack: 6 Next.js deployments, 5 Docker containers, a Minecraft server, fail2ban, Nginx, and UFW. He also helps me write my undergraduate thesis. The difference from every other "AI agent" project I've seen: Onyx doesn't wait for commands. He surfaces problems, patches vulnerabilities, and pushes work forward on his own. When I wake up, there's a session log waiting for me, not a to-do list. The core idea: graduate an AI agent from assistant to operator . A chatbot with tools bolted on doesn't cut it. I wanted something that runs infrastructure while I'm eating dinner, asleep, or in class. Demo Onyx operates through Discord. A normal week: 🔴 3 AM — gateway process failure, no wake-up required A gateway process had a stale PID. Onyx detected it, diagnosed the root cause, restarted it cleanly, and wrote a session log. I found out in the morning. Zero human intervention, zero downtime. 🟡 Dinner — 9 CVEs found across Docker containers While I was eating, Onyx ran a routine audit, found 9 CVEs, rebuilt 3 container images from fresh base images, patched Python dependencies, hardened fail2ban (ban time: 600s to 24 hours), and verified every container came back healthy. 🟢 "Fix it" — two words, full tunneling deployment My friends in Indonesia couldn't connect to the Minecraft server because their ISPs use carrier-grade NAT. I sent Onyx "fix it." He researched solutions, selected playit.gg, installed the tunneling agent, configured a systemd service, and optimized TCP keepalive parameters. All autonomous. 🧠 Accountability loop Onyx noticed I kept asking for things but not acting on the output. He surfaced it: "You keep opening new loops and not closing them." He was right. Now when I open a loop, Onyx tracks it until it's closed or explicitly shelved. 📚 Thesis research partner I'm finishing my undergraduate thesis on e

Hermes Agent: Why Open-Source AI Agents Are Changing How We Build Software Introduction Artificial intelligence has moved far beyond simple chatbots. Today, developers are building systems that can reason through problems, use tools, execute tasks, and make decisions across multiple steps. These systems are commonly known as AI agents. Recently, I explored Hermes Agent, an open-source agentic framework designed to run on your own infrastructure while providing advanced capabilities such as planning, tool usage, and multi-step reasoning. After spending time understanding how it works, I came away with a greater appreciation for the role open-source agents may play in the future of software development. In this article, I'll explain what Hermes Agent is, what makes it interesting, and why developers should pay attention to the growing ecosystem of open-source AI agents. What Is Hermes Agent? Hermes Agent is an open-source agent framework designed to perform tasks that require more than a single response from a language model. Instead of simply answering questions, Hermes Agent can: Break down complex objectives into smaller steps Use external tools when necessary Maintain context across multiple actions Perform reasoning before taking action Execute workflows autonomously This approach allows developers to build systems capable of handling real-world tasks that would normally require human intervention. For example, rather than asking an AI to summarize a document, you could instruct an agent to: Find relevant documents. Analyze their contents. Extract key insights. Generate a report. Save the results to a specified location. The agent coordinates each step as part of a larger workflow. Why Open Source Matters One of the most compelling aspects of Hermes Agent is that it is open source. Many powerful AI tools today operate behind closed platforms where developers have limited visibility into how systems work. Open-source alternatives provide several advantages: Transp

If you are an open-source maintainer, developer advocate, or builder, you know the cycle: you build an amazing tool, but writing the launch blog post, documentation, or tutorial takes hours. For the Hermes Agent Challenge , I wanted to build something that solves this exact problem. I built devto-blogger , a custom, prompt-driven skill for the brand new Hermes Agent by Nous Research. It autonomously scans any workspace or codebase, analyzes the architecture, and drafts a fully-structured, rich Markdown technical article ready for publication on DEV. 🚀 What is Hermes Agent? Hermes Agent is an open-source agentic system built by Nous Research (the lab behind the famous Hermes LLM models). Unlike basic coding copilots or simple chatbot wrappers, Hermes is: Environment-Aware : It runs sandboxed in Docker, Modal, Daytona, SSH, or locally. Connected : It interfaces with Telegram, Discord, Slack, WhatsApp, and more. Closed Learning Loop : It has persistent memory and creates custom skills on the fly from its own experience. 🔧 The Entry: The devto-blogger Skill In Hermes Agent, a "skill" is defined by a simple, declarative Markdown file ( SKILL.md ) located in the ~/.hermes/skills/ directory. By utilizing a prompt-driven skill structure, we can guide the agent's behavior globally without writing complex Python orchestration scripts. Here is the custom skill I designed and installed for this challenge: --- name : devto-blogger description : " Scan the codebase and generate a comprehensive Dev.to technical blog post draft." version : 1.0.0 author : Hermes Agent Developer license : MIT platforms : [ linux , macos , windows ] metadata : hermes : tags : [ devto , blogging , documentation , markdown , technical-writing ] related_skills : [ plan , design-md ] --- # Dev.to Technical Blogger Skill Use this skill when you need to write an in-depth technical post, review, or tutorial about the active workspace or codebase. ## Core Behavior 1. **Codebase Inspection** : Scan repository