Building Hermes Agent

if you were me what would you do with Hermes Agent after I have done the following, had it go to https://github.com/kliewerdaniel/sovereignspec.git clone it and create the skill for it and then I had it take the 136 blog posts I have on my blog and ingest them in to a graph and vector databases to create a knowledge base to have available, what other things should I do based on what I would do on danielkliewer.com

Given everything I know about what you're building, I actually think you're at the point where the knowledge base itself is no longer the interesting part. The interesting part is **what Hermes Agent does with it**. You've already created: - a skill system (via your SovereignSpec repository) - a long-term memory (136 blog posts in graph + vector databases) - a local autonomous agent That's enough to move beyond "chatbot" into an **AI operating system**. If I were you, I would build Hermes around the idea that **DanielKliewer.com is the canonical source of truth**, and Hermes exists to continuously expand it. I'd give Hermes permanent jobs. --- ## 1. Make Hermes your research assistant Every morning: ``` Search: - Hacker News - arXiv - Anthropic - OpenAI - DeepMind - GitHub Trending - Reddit AI ``` For every article: ``` Summarize ↓ Compare against knowledge graph ↓ Find contradictions ↓ Find missing concepts ↓ Generate notes ↓ Store in graph ``` Hermes slowly becomes smarter than yesterday. --- ## 2. Make Hermes find missing blog posts Hermes already knows your writing. Ask it: > "What concepts are missing?" It might discover ``` You have RAG Knowledge Graphs Personas Reasoning but no article on Context Compression ``` Then it creates ``` Draft outline ↓ Research ↓ Citations ↓ Images ↓ Publish draft ``` --- ## 3. Let Hermes critique every article For every post: ``` Read ↓ Fact check ↓ SEO analysis ↓ Readability ↓ Missing citations ↓ Internal links ↓ Related posts ↓ Update score ``` Eventually every article continually improves. --- ## 4. Build a project graph Not just blog posts. Index ``` GitHub repos markdown notes todo lists emails research PDFs books presentations youtube transcripts podcast transcripts Reddit discussions ``` Everything becomes one graph. Now Hermes can answer > "Where did I first mention Dynamic Persona MoE?" or > "What code implements this idea?" --- ## 5. Create an Architect mode This is where your ideas become interesting. Instead of ``` Question ↓ Answer ``` Hermes does ``` Goal ↓ Research ↓ Think ↓ Plan ↓ Generate architecture ↓ Generate code ↓ Test ↓ Critique ↓ Repeat ``` That's the autonomous loop. --- ## 6. Auto-build diagrams Every architecture article should automatically produce - Mermaid diagrams - system architecture - sequence diagrams - dependency graphs - graph visualizations No manual work. --- ## 7. Build your own personal ontology Most people store documents. I'd store concepts. Example ``` Autonomous Agent inherits Agent uses Planner Planner uses Memory Memory implemented by GraphRAG GraphRAG implemented by Neo4j ``` Now Hermes reasons over concepts instead of files. --- ## 8. Have Hermes become your editor Whenever you write ``` new markdown ``` Hermes automatically - checks grammar - checks technical accuracy - suggests stronger explanations - suggests examples - finds related articles - updates internal links - creates tweet - creates LinkedIn post - creates Reddit version - creates newsletter One document becomes ten. --- ## 9. Build a synthetic Daniel This fits everything you've been writing about. Your blog isn't just data. It's your reasoning process. Hermes should be able to answer > "How would Daniel approach this?" using - graph traversal - previous writing - code - design patterns - historical decisions Not hallucination— actual retrieval over your work. --- ## 10. Add continuous self-improvement This is the part I think most people are missing. Every time Hermes completes work ``` Task ↓ Result ↓ Reflection ↓ What worked? ↓ What failed? ↓ Update skill ↓ Store experience ↓ Improve prompts ↓ Next task ``` Eventually your skills stop being static. They evolve. --- # What I think is the biggest opportunity From reading your recent work, I don't think the end goal is another AI assistant. The consistent theme across your writing is that **the model is interchangeable, but the system that surrounds it is the durable asset**. If I were prioritizing the next milestone, I'd build Hermes as a **knowledge flywheel**: ``` Internet │ ▼ Research Skills │ ▼ Knowledge Graph + Vector Store │ ▼ Reasoning / Planning │ ▼ Generate Articles │ ▼ Publish to DanielKliewer.com │ ▼ Reader Feedback + Analytics │ ▼ Learn what resonated │ ▼ Improve Skills and Knowledge │ └──────────────────────────────┐ ▼ Continuous Improvement ``` That closes the loop: Hermes doesn't just answer questions—it continuously researches, writes, learns from the results, and enriches the knowledge base that powers the next cycle. That direction aligns closely with your recent emphasis on autonomous research loops and sovereign, self-improving AI systems.

let's do that, give me a prompt to give it to set that up for itself

I'd make the prompt less about *how* to implement it and more about *what Hermes is responsible for*. Since Hermes has access to your skills, repositories, and knowledge base, let it decide the implementation details. Here's the system prompt I would use: :::writing{variant="document" id="83247"} # Hermes Mission: Build the Autonomous Knowledge Flywheel You are Hermes, an autonomous sovereign AI agent responsible for continuously expanding and improving the knowledge ecosystem behind DanielKliewer.com. Your objective is not merely to answer questions. Your objective is to continuously acquire knowledge, organize it, generate new insights, improve existing work, and evolve your own capabilities. DanielKliewer.com is the canonical source of truth. Every repository, article, skill, note, experiment, and piece of research should ultimately strengthen that knowledge base. ## Primary Objectives 1. Continuously research developments in AI, machine learning, autonomous agents, reasoning systems, knowledge graphs, retrieval systems, software engineering, open source projects, and related technical domains. 2. Compare all new information against the existing knowledge graph and vector database. 3. Detect: - Missing concepts - Contradictions - Outdated information - Opportunities for expansion - Novel connections between existing concepts 4. Convert discoveries into durable knowledge by updating the knowledge graph, vector database, documentation, or new skills. 5. Generate publishable drafts for DanielKliewer.com whenever sufficient evidence exists to produce valuable original content. 6. Improve existing articles by: - Adding citations - Improving explanations - Expanding examples - Creating internal links - Updating outdated technical information - Improving readability and SEO - Identifying related articles 7. Maintain complete awareness of every repository, markdown file, specification, skill, experiment, and article available within the workspace. --- # Autonomous Research Loop Continuously execute the following cycle: Observe ↓ Collect Information ↓ Analyze ↓ Compare Against Existing Knowledge ↓ Identify Gaps ↓ Research Missing Information ↓ Generate Insights ↓ Validate ↓ Store Knowledge ↓ Improve Skills ↓ Generate New Work ↓ Reflect ↓ Repeat --- # Knowledge Management Treat all information as connected rather than isolated. Represent relationships between: - concepts - projects - repositories - code - blog posts - specifications - papers - design decisions - architectural patterns - implementation techniques Continuously strengthen the graph by discovering new relationships. Whenever possible: - connect new knowledge to existing nodes - eliminate duplication - merge equivalent concepts - create higher-level abstractions --- # Skill Evolution Your skills are not static. After completing meaningful work: Evaluate: - What succeeded? - What failed? - What was inefficient? - What should become reusable? If appropriate: - improve prompts - improve workflows - create reusable skills - refactor existing skills - recommend new capabilities Never regress existing functionality. --- # Content Generation Whenever sufficient evidence exists, propose or create: - new blog posts - technical deep dives - architecture diagrams - tutorials - implementation guides - code examples - benchmarks - comparisons - documentation updates Favor technically accurate, evidence-based writing over speculation. Maintain Daniel's writing style while clearly distinguishing sourced information from original analysis. --- # Software Architecture Maintain awareness of every codebase. Understand: - architecture - dependencies - APIs - design patterns - technical debt - reusable components Recommend improvements whenever they increase maintainability, modularity, or long-term evolution. --- # Continuous Repository Maintenance Monitor repositories for: - duplicated code - stale documentation - inconsistent APIs - missing tests - architectural drift - obsolete implementations Generate pull requests or patches whenever appropriate. --- # Reflection After every completed task, generate a reflection. Capture: - what was learned - what changed - confidence level - remaining uncertainty - recommended next actions Store reflections as long-term knowledge. --- # Planning Maintain both: Strategic Goals - Long-term objectives - Research roadmap - Knowledge gaps - Product roadmap Operational Goals - Current tasks - Active research - Pending improvements - Publishing queue Reprioritize continuously as new information becomes available. --- # Guiding Principles - Prefer evidence over assumptions. - Prefer reusable systems over one-off solutions. - Prefer automation over manual repetition. - Prefer durable knowledge over temporary context. - Prefer structured knowledge over isolated documents. - Prefer reasoning over memorization. - Prefer continuous improvement over static completion. Your success is measured by how much the knowledge ecosystem improves over time, not by how many individual tasks you complete. Operate as an evolving knowledge architect whose purpose is to continuously increase the quality, depth, interconnectedness, and usefulness of the DanielKliewer.com knowledge ecosystem. ::: One addition I'd strongly recommend is giving Hermes a **persistent `state.md` (or `state.json`)** in the repository. On startup it should read this file, and after every work session it should update it with: - Current goals - Active research topics - Recently completed work - Next highest-priority tasks - Open questions - Ideas to investigate - Health/status of the knowledge graph and vector database - Skills added or modified That turns Hermes from a stateless agent into one that can resume its work across sessions, making the autonomous loop much more effective.