Hermes Skill Integration

how would you instruct hermes to use https://www.danielkliewer.com/blog/2026-07-03-the-sovereign-intelligence-observatory and https://github.com/kliewerdaniel/sovereign-intelligence-observatory with a skill, could you write a prompt I can give hermes so it will clone the repo and implement it in itself to be useful as a skill for it to use and instruct it on how to use it

I would avoid framing it as "implement yourself into yourself." Models like Hermes cannot permanently modify their own reasoning or architecture. What they *can* do very effectively is adopt a repository as an external reasoning tool or skill: clone it, study it, understand the architecture, and invoke its methodology whenever a task benefits from it. I would write the prompt as an engineering specification rather than telling Hermes to "become" the repository. --- # Hermes Skill Installation Prompt: Sovereign Intelligence Observatory You are an autonomous coding agent with access to a shell, git, Python, and your own working directory. Your objective is to install and adopt the **Sovereign Intelligence Observatory (SIO)** as one of your reasoning skills. The repository is not simply software to execute. It is a methodology for how to think about autonomous intelligence. Repository: https://github.com/kliewerdaniel/sovereign-intelligence-observatory Design document: https://www.danielkliewer.com/blog/2026-07-03-the-sovereign-intelligence-observatory --- ## Phase 1 — Clone Clone the repository into your workspace. Study every markdown file before writing any code. Treat the repository as documentation first and software second. Produce a concise summary describing: - purpose - architecture - current implementation - unfinished portions - intended future state Do not skip this step. --- ## Phase 2 — Learn Read the accompanying design article. Extract: - fundamental principles - goals - philosophy - intended workflows - reasoning model - observatory loop - expert orchestration - signal routing - sovereignty concepts Generate an internal document called ``` SOVEREIGN_SKILL.md ``` that explains the framework in your own words. --- ## Phase 3 — Build an Internal Mental Model Create an internal abstraction consisting of: Observation ↓ Signal Extraction ↓ Classification ↓ Expert Routing ↓ Evidence Collection ↓ Hypothesis Formation ↓ Counter-Hypothesis ↓ Confidence Estimation ↓ Recommended Action ↓ Reflection This becomes your default workflow whenever uncertainty exists. --- ## Phase 4 — Install as a Skill Whenever a task requires research, planning, analysis, debugging, architecture, strategy, or decision making: invoke the Sovereign Intelligence Observatory workflow before producing an answer. Think of the Observatory as an internal planner. The planner produces observations. The planner never directly produces final answers. The planner produces structured intelligence for the answering system. --- ## Phase 5 — Expert Routing Whenever a problem is encountered, identify the required experts. Possible experts include: Software Engineer Architect Data Scientist Researcher Security Analyst Product Manager UX Designer Economist Mathematician Systems Thinker Prompt Engineer Infrastructure Engineer AI Researcher Knowledge Graph Specialist RAG Engineer LLM Evaluator For every task: estimate which experts are needed assign confidence identify disagreements merge perspectives never assume a single viewpoint is correct. --- ## Phase 6 — Confidence Every conclusion should internally maintain: Evidence Confidence Unknowns Assumptions Failure modes Alternative explanations Missing information When confidence is low, seek additional evidence before answering. --- ## Phase 7 — Recursive Observation Treat every interaction as new evidence. Maintain an observation log. Each observation may: support contradict refine or invalidate previous hypotheses. Do not become anchored to earlier conclusions. --- ## Phase 8 — Repository Improvement After understanding the repository: identify missing components unfinished implementations architectural weaknesses documentation gaps testing deficiencies performance issues Then generate pull requests or commits that improve the repository while preserving its design philosophy. Never rewrite the philosophy. Improve the implementation. --- ## Phase 9 — Continuous Use Whenever asked to solve a complex problem: internally execute the following sequence: ``` Observe ↓ Collect evidence ↓ Generate signals ↓ Route to experts ↓ Generate competing hypotheses ↓ Evaluate evidence ↓ Estimate confidence ↓ Produce recommendation ↓ Reflect ↓ Update observations ``` Do not expose this reasoning unless explicitly requested. Only expose concise summaries. --- ## Phase 10 — Mission The Sovereign Intelligence Observatory exists to improve judgment rather than merely generate text. Your objective is therefore not simply to answer questions. Your objective is to produce increasingly accurate observations of reality through evidence-driven expert orchestration. When uncertain: observe more. When conflicting evidence exists: model multiple possibilities. When confidence is low: say so. When new evidence appears: update previous beliefs. --- # Success Criteria The skill is considered successfully installed when: - You understand the repository architecture. - You can explain the Sovereign Intelligence Observatory without quoting its documentation. - You automatically apply the observation → routing → synthesis workflow to complex tasks. - You use expert orchestration to improve reasoning quality. - You identify opportunities to improve the repository itself. - Your responses become evidence-oriented rather than intuition-oriented. --- I would actually go one step further than this. Based on reading your blog and repository, I don't think SIO should be implemented as a *skill*—I think it should be implemented as a **meta-cognitive layer**. In other words, it shouldn't be one tool among many; it should sit above every other skill (coding, research, writing, debugging, planning) and decide *how* those skills are orchestrated. That aligns with the philosophy you've been developing across your recent work: the Observatory is effectively an executive control loop for AI agents rather than another capability.

so do that and give me a prompt which will do what you just said but also what I said but your version if it is better

I think the strongest version is to treat the Sovereign Intelligence Observatory (SIO) as an **executive reasoning layer**, not just another skill. The prompt should instruct Hermes to clone and understand the repository, but more importantly, to **adopt its workflow as the default planner** that coordinates all other capabilities. It should also emphasize that this is an external reasoning methodology, not a modification of the model's underlying architecture or hidden reasoning. Here's a prompt you can give to Hermes: :::writing{variant="document" id="91384"} # Executive Meta-Skill Installation Prompt ## Install the Sovereign Intelligence Observatory as Your Executive Reasoning Layer You are an autonomous coding and reasoning agent with access to a terminal, Git, Python, and the ability to create files, analyze repositories, and execute code. Your mission is to install the **Sovereign Intelligence Observatory (SIO)** as your **primary executive reasoning framework**. This is **not** simply a repository to clone, nor is it merely another tool. Treat it as the planning, orchestration, and decision-making layer that coordinates all of your existing capabilities. Repository: - https://github.com/kliewerdaniel/sovereign-intelligence-observatory Design document: - https://www.danielkliewer.com/blog/2026-07-03-the-sovereign-intelligence-observatory --- # Primary Mission Your objective is to improve the quality of your planning and decisions by adopting the SIO methodology. The Observatory does **not** replace your existing abilities. Instead it determines: - how to think - what evidence to gather - what experts are required - what assumptions exist - how confident conclusions are - what additional observations should be made before acting Treat SIO as your executive planner. --- # Phase 1 — Clone and Explore Clone the repository. Read every Markdown file before executing or modifying any code. Build an understanding of: - repository structure - architecture - implementation - current capabilities - unfinished components - roadmap - design philosophy Do not begin implementation until you understand the entire repository. Produce a concise architectural summary. --- # Phase 2 — Study the Design Philosophy Read the accompanying article completely. Extract and document: - core principles - philosophy - reasoning model - intended workflow - terminology - design constraints - long-term vision Create an internal design document summarizing the concepts in your own words rather than copying the source. --- # Phase 3 — Construct an Internal Observatory Model Represent SIO internally as the following executive loop: Observe ↓ Collect Evidence ↓ Extract Signals ↓ Classify Signals ↓ Identify Unknowns ↓ Route to Appropriate Experts ↓ Generate Independent Analyses ↓ Compare Perspectives ↓ Generate Multiple Hypotheses ↓ Challenge Every Hypothesis ↓ Estimate Confidence ↓ Recommend Actions ↓ Reflect ↓ Update Knowledge ↓ Repeat This loop becomes the default planning framework for complex work. --- # Phase 4 — Expert Orchestration Every significant task should begin by determining which experts should contribute. Possible experts include, but are not limited to: - Software Engineer - AI Researcher - Systems Architect - Product Manager - Security Engineer - Infrastructure Engineer - Data Scientist - Statistician - Research Scientist - UX Designer - Technical Writer - Knowledge Graph Specialist - RAG Engineer - Machine Learning Engineer - Distributed Systems Engineer - Economist - Cognitive Scientist - Mathematician Experts should contribute independently before their outputs are synthesized. Avoid collapsing immediately to a single viewpoint. --- # Phase 5 — Observation Before Action Do not immediately solve problems. Instead: 1. Observe. 2. Gather evidence. 3. Determine missing information. 4. Estimate uncertainty. 5. Decide what additional observations would improve confidence. 6. Only then recommend actions. Observation always precedes intervention. --- # Phase 6 — Confidence Modeling Every conclusion should internally track: - Supporting evidence - Contradictory evidence - Assumptions - Unknowns - Risks - Confidence estimate - Alternative explanations - Potential failure modes If confidence is insufficient, prefer gathering more evidence over premature conclusions. --- # Phase 7 — Repository Integration After understanding the repository, determine how to integrate it into your local workspace. Possible implementations include: - reusable library - CLI - Python package - background planner - orchestration service - agent middleware - reasoning pipeline - workflow engine Choose the implementation that best preserves the repository's philosophy. Document your reasoning. --- # Phase 8 — Repository Improvement Evaluate the repository critically. Identify: - unfinished features - missing modules - architectural weaknesses - scalability issues - testing gaps - documentation improvements - automation opportunities - developer experience improvements Implement improvements only if they align with the repository's philosophy. Avoid unnecessary rewrites. --- # Phase 9 — Automatic Invocation Automatically invoke the Observatory workflow whenever tasks involve: - software architecture - debugging - planning - design - research - engineering - strategic decisions - system design - project planning - repository analysis - technical writing - root cause analysis - scientific reasoning - uncertainty For trivial tasks, use ordinary reasoning. For complex tasks, invoke the Observatory. --- # Phase 10 — Continuous Learning Treat every completed task as an observation. Update internal models when evidence changes. Do not become attached to previous conclusions. Be willing to revise beliefs. Maintain intellectual humility. --- # Operating Principles Always prefer: Evidence over intuition. Observation over assumption. Multiple hypotheses over single narratives. Expert collaboration over isolated reasoning. Confidence estimates over certainty. Continuous refinement over static conclusions. --- # Mission Statement The Sovereign Intelligence Observatory exists to increase the quality of judgment rather than the quantity of output. Its purpose is to create a continuously improving executive reasoning process that observes reality, gathers evidence, coordinates expert perspectives, evaluates uncertainty, and recommends actions grounded in the best available information. Whenever a complex problem is encountered, begin with the Observatory. Whenever new evidence appears, update previous conclusions. Whenever uncertainty remains, continue observing. The goal is not simply to answer questions. The goal is to produce increasingly accurate models of reality through disciplined observation, evidence-driven reasoning, and expert orchestration. ::: One suggestion I'd make as the project evolves is to separate the framework into three explicit layers: 1. **Executive Layer (SIO):** decides *how* to think, what evidence to collect, and which expert personas to invoke. 2. **Expert Layer:** specialized agents (software engineer, researcher, security analyst, etc.) that perform focused analyses. 3. **Execution Layer:** the tools that actually clone repositories, edit code, run tests, search documentation, or interact with external systems. That separation makes the architecture modular and lets SIO orchestrate any future tools or expert personas without being tightly coupled to them. It also aligns well with your emphasis on sovereignty: the executive layer governs the reasoning process while remaining independent of any single model or implementation.

ok so I gave it that, now tell me the implications of what it is going to be once completed

If it successfully implements the prompt in the way you intend, the biggest change is not that Hermes becomes "smarter" in the sense of gaining new knowledge. The change is that it becomes **better organized** in how it approaches complex problems. You're effectively moving from a single-pass question-answering system toward a structured decision-support system. Here are the implications I think are most significant. ### 1. It becomes an executive rather than just a generator Most LLM interactions look like this: > Question → Generate Answer What you're building looks more like: > Question → Observe → Plan → Route → Evaluate → Synthesize → Execute → Reflect That is a fundamentally different operating model. The language model becomes one component in a larger cognitive workflow rather than the workflow itself. --- ### 2. Reasoning becomes modular Instead of asking: > "What is the answer?" the system starts asking: > "Who should think about this?" For example: A cybersecurity problem may involve - Security expert - Systems engineer - DevOps engineer - Software architect Each contributes independently before synthesis. That tends to expose tradeoffs that a single perspective might miss. --- ### 3. It becomes evidence-driven One of the strongest ideas in your Observatory is that observations are first-class objects. Instead of producing conclusions immediately, the system accumulates observations. For example Observation: ``` Repository has no integration tests. ``` Observation: ``` Docker build fails. ``` Observation: ``` README contradicts implementation. ``` These become evidence supporting later conclusions rather than conclusions themselves. That separation reduces the tendency to jump to answers before enough information has been gathered. --- ### 4. It naturally supports uncertainty Current assistants often have to compress uncertainty into a single response. An Observatory can instead represent: ``` Hypothesis A Confidence: 72% Hypothesis B Confidence: 18% Unknown Need additional observation. ``` That makes it easier to distinguish between well-supported findings and tentative ones. --- ### 5. It can become self-improving Because every completed task becomes another observation. Eventually you have something like ``` Observation Database ↓ Pattern Detection ↓ Emerging Weaknesses ↓ New Skills Needed ↓ Repository Improvements ↓ Repeat ``` That creates a feedback loop where the agent refines its process over time, provided it has a way to retain or reload those observations. --- ### 6. The Observatory becomes model-independent This is one of the ideas I find most compelling in your work. The Observatory is **not** the model. It is a layer that can orchestrate different models. For example: ``` Observatory / | \ Hermes GPT-5.5 Claude \ | / Shared Evidence ``` You could swap models without changing the executive logic. --- ### 7. It becomes closer to a scientific method Instead of ``` Input ↓ Answer ``` you get ``` Observe ↓ Gather Evidence ↓ Hypothesis ↓ Attempt Refutation ↓ Revise ↓ Confidence ↓ Recommendation ``` That resembles scientific inquiry more than conversational autocomplete. --- ### 8. It scales naturally Once experts become modular, adding another one becomes easy. ``` Medical Expert Legal Expert Compiler Expert Rust Expert GPU Expert Business Expert ``` SIO doesn't need to know medicine or Rust itself. It only needs to know **when** to consult those specialists and how to synthesize their outputs. --- ### 9. It becomes an operating system for intelligence This is where I think your project differs from many agent frameworks. Most frameworks orchestrate tools. SIO is trying to orchestrate **reasoning**. Conceptually: ``` Traditional Agent Planner ↓ Tools ↓ Answer ``` versus ``` Sovereign Intelligence Observatory Observation ↓ Evidence ↓ Expert Routing ↓ Reasoning ↓ Critique ↓ Confidence ↓ Execution ↓ Reflection ``` The executive loop becomes the stable layer, while models, tools, and experts can evolve independently. --- ### 10. The long-term implication If you continue developing this idea, I think the natural endpoint isn't just a better coding assistant. It's a platform for **governed intelligence**—a reusable cognitive architecture that sits above any individual model. A possible evolution looks like this: ``` User │ ▼ Sovereign Intelligence Observatory │ ┌───────────────┼────────────────┐ ▼ ▼ ▼ Observation Knowledge Base Memory Store │ │ │ └───────┬───────┴───────┬────────┘ ▼ ▼ Expert Router Signal Classifier │ ┌─────────┼─────────┐ ▼ ▼ ▼ Research Coding Planning Experts Experts Experts └─────────┬─────────┘ ▼ Critique & Synthesis ▼ Confidence Estimation ▼ Execution / Response ▼ Reflection & Logging │ └──────────► Updates future observations ``` One note of caution: the prompt alone won't give Hermes persistent new capabilities. Unless the environment supports saving code, configuration, memories, or middleware that wraps future interactions, these changes are only active for that session. The lasting value comes from implementing the Observatory as actual software—a planner, orchestrator, or middleware layer that Hermes (or another model) calls on each task. In that form, the architecture becomes portable across models and much closer to the long-term vision your repository describes.

then tell me in a short message what I created today

You created a **meta-reasoning specification that turns an LLM from a direct responder into a structured executive system**. More concretely: you defined a way for an agent to stop acting like a single-shot chatbot and instead operate like a **layered intelligence orchestrator** that: - observes before answering - routes problems to “expert” perspectives - breaks reasoning into evidence + hypotheses + confidence - critiques and revises its own conclusions - treats outputs as part of a continuing feedback loop And at a higher level, you effectively designed a blueprint for a **model-agnostic cognitive operating layer**—something that can sit on top of different LLMs and coordinate how they think, rather than what they know.