{"slug": "hermes-repo-dojo-most-agents-answer-hermes-learns-then-it-safely-contributes", "title": "Hermes Repo Dojo: Most Agents Answer. Hermes Learns. Then It Safely Contributes.", "summary": "Hermes Repo Dojo, a new local AI developer tool, transforms any public GitHub repository into a guided onboarding academy by enabling the Hermes Agent to learn, create reusable skills, and safely contribute code. Unlike standard AI coding agents that only answer questions, the system analyzes a repository, extracts hidden onboarding knowledge, converts it into reusable workflows, and generates a first contribution inside a sandbox clone. The tool turns static code repositories into living, self-teaching systems that preserve the contributor journey and guide future interactions.", "body_md": "*This is a submission for the Hermes Agent Challenge: Build With Hermes Agent*\n\nTurn any GitHub repo into a living onboarding academy.\n\nMost AI coding agents answer questions about a repository.\n\n**Hermes Repo Dojo does something different.**\n\nIt lets Hermes Agent learn a GitHub repository, transform that understanding into reusable repo-specific skills, improve on a second pass, and safely create a first contribution inside a sandbox clone.\n\nThis is not a repo chatbot.\n\nThis is an agentic onboarding system.\n\n**Hermes Repo Dojo** is a local AI developer tool that turns any public GitHub repository into a guided onboarding academy for new contributors.\n\nA user pastes a GitHub repository URL, and Hermes Repo Dojo generates:\n\nThe core idea:\n\nA repository should not only store code. It should teach. It should remember. It should safely guide contribution.\n\nOpen-source onboarding is broken.\n\nEvery new contributor usually has to repeat the same painful discovery process:\n\nMaintainers and senior contributors carry hidden knowledge in their heads.\n\nNew contributors do not.\n\nAI agents can answer questions about a repo, but most of that knowledge disappears after the answer.\n\nHermes Repo Dojo turns that one-time exploration into reusable operational knowledge.\n\n🎥 Demo video:\n\nIn the demo, I use:\n\n```\nhttps://github.com/karpathy/micrograd\n```\n\nHermes Repo Dojo analyzes `micrograd`\n\n, a tiny educational autograd engine, and produces:\n\n`sigmoid`\n\nactivation to the `Value`\n\nclassThe final demo flow is:\n\n```\nGitHub URL\n   ↓\nHermes Repo Analysis\n   ↓\nSkill Forge\n   ↓\nSecond Pass Improvement\n   ↓\nPatch Arena\n   ↓\nHermes Brain Replay\n```\n\nGitHub repository:\n\n[https://github.com/jpablortiz96/hermes-repo-dojo](https://github.com/jpablortiz96/hermes-repo-dojo)\n\nThe app runs locally and invokes Hermes through the CLI from the backend.\n\nHermes Agent is not decorative in this project.\n\nIt is the operating intelligence behind the workflow.\n\nHermes Repo Dojo invokes Hermes through commands like:\n\n```\nhermes chat --toolsets \"terminal,skills\" -q \"...\"\n```\n\nHermes is used to:\n\nThe key Hermes capabilities I leaned on were:\n\n| Hermes Capability | How Repo Dojo Uses It |\n|---|---|\n| Terminal tool use | Inspecting local cloned repositories |\n| Skills | Creating reusable repo-specific workflows |\n| Multi-step reasoning | Analysis → skills → second pass → patch |\n| CLI-first architecture | Running Hermes locally from the backend |\n| Agentic workflow | Turning codebase exploration into a guided product experience |\n\n**What if every repository could teach itself?**\n\nA repository is usually static.\n\nIt stores code, but it does not preserve the contributor journey behind the code.\n\nHermes Repo Dojo makes the repository operational.\n\nIt lets Hermes Agent inspect a codebase, extract hidden onboarding knowledge, convert that knowledge into reusable skills, and use those skills to guide better future interactions.\n\nThe result is a repository that does not just contain code.\n\nIt becomes a living onboarding workflow.\n\nHermes extracts a repository profile:\n\nFor `micrograd`\n\n, Hermes identifies Python, Autograd, Neural Networks, Pytest, and the key files a contributor should inspect first.\n\nInstead of showing raw folders, Hermes transforms the structure into logical areas.\n\nFor `micrograd`\n\n, it maps:\n\n`micrograd/engine.py`\n\n`micrograd/nn.py`\n\n`demo.ipynb`\n\n, `trace_graph.ipynb`\n\n, `test/test_engine.py`\n\nThis is where the project becomes more than analysis.\n\nHermes generates reusable repo-specific skills.\n\nFor the demo, it creates:\n\n```\nMicrogradAutogradTrace\nMicrogradMLPTraining\nMicrogradGraphViz\n```\n\nThe point is simple:\n\nRepository understanding should not disappear after one answer. It should become reusable operational knowledge.\n\nAfter generating skills, Hermes Repo Dojo runs a second pass.\n\nBefore skills:\n\nAfter skills:\n\nMost agents answer once.\n\nHermes improves on the second pass.\n\nPatch Arena is where Hermes can safely contribute.\n\nThis is not reckless auto-coding.\n\nPatch Arena creates a sandbox clone:\n\n```\nworkspace/patch-arena/{repo}-{timestamp}\n```\n\nThen Hermes can create or assist with a contribution **inside that sandbox only**.\n\nSafety rules:\n\n`git push`\n\n`sudo`\n\n`apt-get`\n\nFor `micrograd`\n\n, Patch Arena creates a safe first contribution:\n\n``` python\n+ import math\n+\n  class Value:\n      \"\"\" stores a single scalar value and its gradient \"\"\"\n\n+     def sigmoid(self):\n+         s = 1 / (1 + math.exp(-self.data))\n+         out = Value(s, (self,), 'sigmoid')\n+\n+         def _backward():\n+             self.grad += (s * (1 - s)) * out.grad\n+         out._backward = _backward\n+         return out\n```\n\nIt also adds a smoke test and verifies the result.\n\nThis is the moment where the product becomes more than an onboarding dashboard.\n\nHermes does not just explain the repo.\n\nIt creates a safe first contribution.\n\nMost agent systems hide the process in logs.\n\nHermes Brain Replay turns the agent journey into a visible product experience.\n\nIt shows:\n\n```\nRepo Ingested\nDNA Extracted\nArchitecture Mapped\nSkills Forged\nSecond Pass Improved\nPatch Created\nVerification Passed\nLearning Trace Completed\n```\n\nThis is designed to make the agent’s learning process understandable, visual, and demo-friendly.\n\n``` php\nflowchart TD\n    A[GitHub Repo URL] --> B[Next.js UI]\n    B --> C[Analyze API]\n    C --> D[Clone Repository]\n    D --> E[Hermes Agent CLI]\n    E --> F[Repo DNA JSON]\n    F --> G[Dashboard]\n    F --> H[Skill Forge]\n    H --> I[Generated SKILL.md Files]\n    I --> J[Hermes Skills Directory]\n    F --> K[Second Pass]\n    K --> L[Before vs After]\n    F --> M[Patch Arena]\n    M --> N[Sandbox Clone]\n    N --> O[Safe Patch Generation]\n    O --> P[Diff Preview + Verification]\n    F --> Q[Hermes Brain Replay]\n    H --> Q\n    K --> Q\n    M --> Q\n```\n\nHermes Repo Dojo separates learning from contribution.\n\nScout Mode is read-oriented.\n\nIt inspects the repository, extracts facts, generates onboarding analysis, and forges skills.\n\nPatch Arena is write-enabled, but only inside a sandbox clone.\n\nThe original repository stays untouched.\n\nAgent providers can hit quota, timeout, or return unsafe output.\n\nTo keep the product reliable, Hermes Repo Dojo can fall back to locally extracted repository facts and sandbox-safe patch generation.\n\nThat fallback does not replace Hermes Agent. It protects the user experience and environment when provider limits interrupt the live flow.\n\n| Typical AI Repo Tool | Hermes Repo Dojo |\n|---|---|\n| Answers one-off questions | Creates reusable repo-specific skills |\n| Explains files | Builds a contributor onboarding path |\n| Suggests changes | Generates sandboxed patches with diff and verification |\n| Hides agent process in logs | Visualizes the full learning trace |\n| Static analysis | Skill generation and second-pass improvement |\n\nThis is not a chatbot for repositories.\n\nIt is an agentic onboarding system.\n\nBuilding this project taught me that agentic products need more than model output.\n\nThey need:\n\nHermes Agent was a strong fit because the project is not just about generating text.\n\nIt is about tool use, skill creation, repository understanding, and multi-step workflows.\n\nThe next versions could include:\n\nMost agents answer.\n\nHermes learns.\n\nThen it safely contributes.", "url": "https://wpnews.pro/news/hermes-repo-dojo-most-agents-answer-hermes-learns-then-it-safely-contributes", "canonical_source": "https://dev.to/jpablortiz96/hermes-repo-dojo-most-agents-answer-hermes-learns-then-it-safely-contributes-1kda", "published_at": "2026-05-31 02:16:17+00:00", "updated_at": "2026-05-31 02:41:59.140339+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "artificial-intelligence", "machine-learning"], "entities": ["Hermes Agent", "Hermes Repo Dojo", "GitHub", "micrograd", "karpathy"], "alternates": {"html": "https://wpnews.pro/news/hermes-repo-dojo-most-agents-answer-hermes-learns-then-it-safely-contributes", "markdown": "https://wpnews.pro/news/hermes-repo-dojo-most-agents-answer-hermes-learns-then-it-safely-contributes.md", "text": "https://wpnews.pro/news/hermes-repo-dojo-most-agents-answer-hermes-learns-then-it-safely-contributes.txt", "jsonld": "https://wpnews.pro/news/hermes-repo-dojo-most-agents-answer-hermes-learns-then-it-safely-contributes.jsonld"}}