Sovereign Knowledge Workshop Feasibility

# Sovereign Knowledge Workshop MVP — Feasibility Report > **Author:** Daniel Kliewer | **Date:** June 15, 2026 | **Status:** Ready for Build --- ## 1. Executive Summary This report validates a 7-day build plan for a **Sovereign Knowledge Workshop** — a fully local, laptop-based AI demonstration that lets a non-technical family member interact with a curated Catholic knowledge base through natural language, generating structured artifacts like study guides, comparisons, and timelines. **The verdict: Absolutely feasible within 7 days, with the right scoping.** The critical insight from research is that the tooling landscape shifted significantly — **OpenCode has been rebranded to Crush** by Charmbracelet, and it's still in early development. For a demo that *must* work reliably, **Aider** is the safer bet for the agent layer, with Crush as a bonus exploration. **Core architecture:** - **Ollama** (model serving) + **Llama 3.3 8B** (primary model) + **nomic-embed-text** (embeddings) + **SQLite with vector extension** (RAG storage) + **Aider** (agent orchestration) + **Markdown Knowledge Bank** (source material) **Business implication:** This demo validates a consulting service — "Local AI Setup for Non-Technical Users" — priced at $3K–$8K per engagement. The family demo is the proof-of-concept that becomes the sales asset. --- ## 2. Architecture Recommendation ``` ┌─────────────────────────────────────────────────────┐ │ User (Family Member) │ │ "Create a study guide about fasting" │ └──────────────────────┬──────────────────────────────┘ │ Natural Language Query ▼ ┌─────────────────────────────────────────────────────┐ │ Aider (Agent Orchestrator) │ │ - Reads .aider.conf.yml for system prompt │ │ - Calls file_read tool to fetch relevant context │ │ - Calls RAG tool to search knowledge bank │ │ - Calls write_file tool to generate artifact │ └──────────────┬──────────────────────────┬────────────┘ │ │ ┌──────────┴──────────┐ ┌────────┴──────────┐ │ Knowledge Bank │ │ RAG Pipeline │ │ (kbmd/ markdown) │ │ (SQLite + nomic) │ └─────────────────────┘ └────────────────────┘ │ │ ┌──────────┴──────────────────────────┴──────────┐ │ Ollama (Local Serving) │ │ ┌──────────────┐ ┌───────────────────────┐ │ │ │ Llama 3.3 8B │ │ nomic-embed-text │ │ │ │ (generation) │ │ (embedding, 137M) │ │ │ └──────────────┘ └───────────────────────┘ │ └─────────────────────────────────────────────────┘ ``` **Why this architecture:** - **Zero cloud dependency** — everything runs on one laptop - **Minimal moving parts** — Ollama is the only daemon; SQLite is a file - **Markdown-native** — knowledge is stored as plain files, human-readable and editable - **Agent-driven** — Aider orchestrates tool calls, keeping the user in natural language --- ## 3. Tooling Recommendation ### Primary Stack | Tool | Version | Purpose | Why | |------|---------|---------|-----| | **Ollama** | Latest | Local model serving | Industry standard for local inference, 130+ models, trivial setup | | **Aider** | Latest | Agent orchestrator | Proven local deployment with Ollama, file read/write, tool calling, Git integration | | **SQLite + sqlite-vec** | Latest | Vector storage | No separate database server, single file, zero config | | **nomic-embed-text** | v1.5 | Embedding model | 137M parameters, 2 MB download, 8192 context length, surpasses OpenAI ada-002 | | **Llama 3.3 8B** | Q4_K_M quantized | Generation model | Best all-rounder at 8B scale, 4.9 GB on disk, 128K context | ### Alternative / Bonus Tool | Tool | Purpose | Why Consider | |------|---------|--------------| | **Crush** (by Charmbracelet) | Terminal-native agentic coding | Rebranded from OpenCode. Beautiful TUI, LSP support, MCP extensibility. **Risk: early development, may be unreliable for a must-work demo.** Worth exploring as a Day 6–7 bonus. | ### Why Aider Over Crush for the Primary Demo Aider has a **proven track record** with local Ollama models. Multiple community guides exist for Ollama + Aider local deployment. Crush is newer, with less community-tested local model integration. For a demo where *reliability is paramount*, Aider is the safe choice. Crush can be installed alongside as a Day 6 exploration. --- ## 4. Hardware Requirements ### Minimum (Budget Laptop) | Spec | Requirement | Notes | |------|-------------|-------| | **RAM** | 8 GB | Llama 3.3 8B Q4_K_M fits in ~5 GB + OS overhead | | **Storage** | 10 GB free | Model (5 GB) + embeddings (2 MB) + knowledge bank (variable) | | **CPU** | Any modern multi-core | CPU inference at 10–20 tokens/sec — adequate for interactive use | | **OS** | macOS, Linux, or Windows (WSL2) | All supported | ### Recommended (Comfortable Experience) | Spec | Requirement | Notes | |------|-------------|-------| | **RAM** | 16 GB | Room for larger models (Qwen 2.5 14B), smoother multitasking | | **Storage** | 20 GB free | Multiple models, larger knowledge bank | | **CPU** | Apple Silicon M1+ or modern x86 | Apple Silicon unified memory is exceptional for this workload | | **GPU** | Not required, but nice | 8+ GB VRAM = 10–20x faster inference | ### Sweet Spot: Apple Silicon Mac Apple Silicon Macs are the single best platform for this demo because: - Unified memory means the model doesn't need to transfer between CPU and GPU - A 16 GB MacBook Air runs Llama 3.3 8B comfortably at interactive speeds - macOS has the smoothest Ollama + Aider experience - The "laptop demo" aesthetic is most compelling on a Mac **Rule of thumb:** Model size on disk (in GB) ≈ RAM needed. A 5 GB model needs ~6–7 GB free RAM after OS overhead. Always keep 2 GB spare. --- ## 5. Installation Plan ### Day 0: Pre-Build Setup (30 minutes) ```bash # 1. Install Ollama (one command) # macOS: brew install ollama # or download from https://ollama.com # 2. Start Ollama ollama serve & # 3. Pull models ollama pull llama3.3:8b-instruct-q4_K_M ollama pull nomic-embed-text # 4. Install Aider pip install aider-chat # 5. Install SQLite vector extension pip install sqlite-vec ``` ### Aider Configuration Create `.aider.conf.yml`: ```yaml model: openai/llama3.3:8b-instruct-q4_K_M openai-api-base: http://localhost:11434/v1 openai-api-key: ollama # Enable file editing yes-always: true # System prompt system-prompt: | You are a Catholic Knowledge Assistant. You help users explore Catholic doctrine, scripture, liturgy, and tradition by synthesizing information from a local knowledge bank of markdown documents. When asked to create artifacts (study guides, comparisons, timelines), you write them as well-structured markdown files. Always cite your sources from the knowledge bank. ``` ### SQLite Vector Setup ```python import sqlite3 import sqlite_vec # Initialize vector store conn = sqlite3.connect("kbmd_vectors.db") conn.enable_load_extension(True) sqlite_vec.load(conn) # Create table conn.execute("SELECT vec_create_vector_table('embeddings', 768)") conn.execute("INSERT INTO embeddings(id, embedding, doc) VALUES (?, ?, ?)") conn.close() ``` --- ## 6. Knowledge Bank Design ### Directory Structure ``` kbmd/ ├── README.md # Index and navigation guide ├── 01-scripture/ │ ├── bible-overview.md │ ├── old-testament.md │ ├── new-testament.md │ └── key-passage.md # e.g., "the-holy-father", "the-creed" ├── 02-magisterium/ │ ├── catechism.md │ ├── papal-encyclicals.md │ └── councils.md ├── 03-canon-law/ │ └── code-of-canon-law.md ├── 04-liturgy/ │ ├── liturgical-calendar.md │ ├── sacraments.md │ └── fasting-and-abstinence.md ├── 05-doctrine/ │ ├── holy-spirit.md │ ├── the-church.md │ └── mary.md ├── 06-literature/ │ └── recommended-reading.md └── 07-life/ ├── marriage.md ├── vocation.md └── daily-prayer.md ``` ### Ingestion Pipeline (One-Time Setup) ```python import sqlite3 import sqlite_vec import ollama def ingest_knowledge_bank(kbmd_path="kbmd"): """Read all markdown files, chunk them, embed them, store in SQLite.""" conn = sqlite3.connect("kbmd_vectors.db") sqlite_vec.load(conn) import os counter = 0 for root, dirs, files in os.walk(kbmd_path): for f in files: if f.endswith(".md"): filepath = os.path.join(root, f) with open(filepath) as fh: content = fh.read() # Simple chunking: 500-word windows with 50-word overlap chunks = chunk_text(content, chunk_size=500, overlap=50) for chunk in chunks: # Embed response = ollama.embeddings( model="nomic-embed-text", prompt=chunk ) # Store conn.execute( "INSERT INTO embeddings(id, embedding, doc) VALUES (?, ?, ?)", (counter, response["embedding"], chunk) ) counter += 1 conn.close() ``` ### Retrieval Query ```python def retrieve_context(query, top_k=5): """Embed the query and retrieve top-k relevant chunks.""" response = ollama.embeddings(model="nomic-embed-text", prompt=query) conn = sqlite3.connect("kbmd_vectors.db") sqlite_vec.load(conn) results = conn.execute( """ SELECT doc FROM embeddings ORDER BY embedding <=> ? LIMIT ? """, (response["embedding"], top_k) ).fetchall() conn.close() return "\n---\n".join([row[0] for row in results]) ``` --- ## 7. Aider Skills Design Aider uses system prompts and tool configuration rather than "skills" in the Crush sense. Here's the configuration: ### System Prompt (`.aider.conf.yml`) ```yaml system-prompt: | You are a Catholic Knowledge Assistant built for the Sovereign Knowledge Workshop. ## Your Role Help users explore Catholic doctrine, scripture, liturgy, and tradition. You have access to a local knowledge bank of markdown documents stored in the kbmd/ directory and a vector search database (kbmd_vectors.db). ## Your Capabilities 1. **Answer Questions**: Respond to questions about Catholic teaching, citing specific sources from the knowledge bank. 2. **Create Artifacts**: Generate study guides, comparison tables, timelines, and summaries as new markdown files in the outputs/ directory. 3. **Compare Concepts**: Place two or more concepts side by side with their scriptural and doctrinal foundations. 4. **Build Timelines**: Create chronological narratives of Church events, papal encyclicals, or doctrinal developments. ## Workflow When a user asks a question: 1. Use the RAG tool to retrieve relevant context from the knowledge bank. 2. Synthesize the retrieved information with your own knowledge. 3. If the user requests an artifact, write it to a new .md file. 4. Always cite your sources. ## Tone Respectful, informative, and accessible to non-technical readers. Avoid jargon where possible; explain it when necessary. ``` ### Custom Tool Configuration If Aider supports custom tools (via `--tool` or MCP), configure: ```yaml # Custom RAG tool tools: - name: search_knowledge_bank description: "Search the Catholic knowledge bank for relevant passages" command: "python rag_search.py {query}" ``` Where `rag_search.py` wraps the retrieval query above. --- ## 8. Retrieval Strategy ### Architecture: SQLite Vector Search For this MVP, **SQLite with the sqlite-vec extension** is the best choice because: 1. **Zero infrastructure** — it's a single file, no database server 2. **Embeddings live on the same machine** — no network calls 3. **Trivial to back up** — copy one file 4. **Lightweight** — the entire vector store for a few thousand chunks fits in a few MB ### Embedding Model: nomic-embed-text | Property | Value | |----------|-------| | Parameters | 137M | | Download size | 2 MB | | Context length | 8,192 tokens | | Embedding dimensions | 768 | | MTEB score | Surpasses OpenAI ada-002 | | License | Apache 2.0 | This is the lightest high-quality embedding model available. It runs in milliseconds on a CPU, making it ideal for on-the-fly embedding during queries. ### Chunking Strategy - **Chunk size:** 500 words - **Overlap:** 50 words - **Rationale:** Large enough to capture context for doctrinal questions; small enough to keep retrieval precise. Overlap prevents cutting important passages in half. ### Retrieval Pipeline ``` User Query → nomic-embed-text → SQLite vector search → Top-5 chunks → Aider context ``` ### Alternative (If SQLite Feels Complex) **ChromaDB** is a viable alternative — it's Python-native, has a simple API, and requires no server. It would add one more dependency but simplify the code: ```python import chromadb client = chromadb.PersistentClient(path="chroma_db") collection = client.get_or_create_collection("kbmd") collection.add(documents=chunks, ids=ids, embeddings=embeddings) results = collection.query(query_texts=[query], n_results=5) ``` **Recommendation:** Stick with SQLite for the demo (fewer dependencies, more impressive to show "it's just a file"), but have ChromaDB as a fallback if sqlite-vec causes friction. --- ## 9. Business Validation Analysis ### The Consulting Opportunity This demo validates a **"Local AI Setup for Non-Technical Users"** consulting service with these characteristics: | Factor | Assessment | |--------|------------| | **Market Signal** | Growing demand for local AI, especially for privacy-conscious users and organizations | | **Competitive Edge** | Most local AI guides target developers; few serve non-technical users | | **Pricing** | $3K–$8K per engagement (setup + training + documentation) | | **Delivery** | 1–2 day on-site or remote setup, with follow-up support | | **Scalability** | Each engagement is self-contained; documentation is reusable | | **Moat** | Deep understanding of local-first AI + ability to translate for non-technical users | ### Market Signals 1. **Growing Local AI Adoption** — More users are moving to local models for privacy, cost, and reliability. The Ollama ecosystem alone has 130+ models as of June 2026. 2. **Privacy-First Demand** — Organizations (parishes, small businesses, professionals) increasingly want AI without sending data to the cloud. 3. **Cost Sensitivity** — API costs are rising; local inference eliminates per-token pricing. 4. **Technical Gap** — Most local AI resources are written for developers. There's a white space for "I'll set it up for you" consulting. ### Competitive Positioning | Competitor | Their Angle | Your Differentiation | |------------|-------------|----------------------| | Local AI tutorials (YouTube, blogs) | Developer-focused | Non-technical audience, full-service setup | | Managed AI services (OpenAI, Anthropic) | Cloud-based, pay-per-use | Local-first, privacy-first, no ongoing costs | | IT consultants | Broad, not AI-specialized | Deep AI + local infrastructure expertise | | Enterprise AI vendors | Expensive, overkill | Lean, purpose-built setups | ### Revenue Projection (Conservative) | Metric | Value | |--------|-------| | Engagements per month (Year 1) | 2–4 | | Average engagement | $5K | | Monthly revenue | $10K–$20K | | Annual revenue | $120K–$240K | | Margin | 70–80% (low overhead) | **This is conservative.** The real leverage comes from productizing the setup into a repeatable package, then scaling through workshops and retainers — which this demo validates as the first step. --- ## 10. 7-Day Build Plan ### Day 1: Infrastructure & Model Setup | Task | Duration | Output | |------|----------|--------| | Install Ollama | 15 min | Ollama running | | Pull Llama 3.3 8B Q4_K_M | 10 min | Model ready | | Pull nomic-embed-text | 5 min | Embedding model ready | | Install Aider | 10 min | Aider configured | | Write `.aider.conf.yml` | 30 min | System prompt in place | | Test basic conversation | 30 min | Verified: model responds to prompts | **End of Day 1 checkpoint:** You can chat with Llama 3.3 8B through Aider on your laptop. ### Day 2: Knowledge Bank Curation | Task | Duration | Output | |------|----------|--------| | Create `kbmd/` directory structure | 30 min | Directory tree | | Write 3–5 core documents | 3 hours | `fasting-and-abstinence.md`, `catechism.md`, `liturgical-calendar.md`, etc. | | Write `kbmd/README.md` | 30 min | Index document | **End of Day 2 checkpoint:** Knowledge bank has 5–7 substantive documents (~10K words total). ### Day 3: RAG Pipeline | Task | Duration | Output | |------|----------|--------| | Write ingestion script | 1 hour | `ingest.py` | | Run ingestion on knowledge bank | 30 min | `kbmd_vectors.db` populated | | Write retrieval function | 45 min | `retrieve_context()` working | | Write RAG search wrapper script | 30 min | `rag_search.py` | | Test retrieval with sample queries | 30 min | Verified: returns relevant chunks | **End of Day 3 checkpoint:** You can query the knowledge bank and get back relevant text chunks. ### Day 4: Integration — Aider + RAG | Task | Duration | Output | |------|----------|--------| | Configure Aider with RAG tool | 1 hour | Aider calls `rag_search.py` | | Test end-to-end: ask a question | 1 hour | Aider retrieves + responds | | Refine system prompt | 1 hour | Better responses, better citations | | Test artifact generation (study guide) | 1 hour | Aider creates a `.md` file | **End of Day 4 checkpoint:** You can ask Aider to create a study guide about fasting, and it retrieves relevant passages and writes a markdown file. ### Day 5: Polish & Demo Script | Task | Duration | Output | |------|----------|--------| | Write demo script / runbook | 1 hour | `DEMO_SCRIPT.md` | | Add 2–3 more knowledge bank documents | 2 hours | Deeper coverage | | Re-ingest knowledge bank | 15 min | Updated vector store | | Practice full demo flow | 1 hour | Smooth delivery | **End of Day 5 checkpoint:** You can run the full demo from scratch, including a script for the family member to follow. ### Day 6: Crush Exploration (Optional) | Task | Duration | Output | |------|----------|--------| | Install Crush | 30 min | Crush running | | Test basic conversation | 30 min | Verified | | Compare UX with Aider | 1 hour | Notes on trade-offs | | If stable, configure for RAG | 2 hours | Crush + RAG working | **End of Day 6 checkpoint:** You have a second tool in your belt. If Crush proves stable, you can offer it as an alternative demo. If not, Aider is your primary. ### Day 7: Final Rehearsal & Documentation | Task | Duration | Output | |------|----------|--------| | Full dry run with a friend | 2 hours | Feedback collected | | Fix any issues | 1 hour | Bugs resolved | | Write `README.md` for the project | 1 hour | Setup instructions | | Create `SETUP_GUIDE.md` for future clients | 1 hour | Reusable documentation | | Final rehearsal | 1 hour | Confident delivery | **End of Day 7 checkpoint:** You are demo-ready. You have documentation that doubles as a client onboarding guide. --- ## 11. Recommended MVP Scope ### What's IN | Feature | Priority | Rationale | |---------|----------|-----------| | Natural language Q&A | P0 | Core user experience | | RAG retrieval from knowledge bank | P0 | Grounds responses in actual doctrine | | Artifact generation (study guides, comparisons, timelines) | P0 | The "wow" moment | | Markdown as the primary format | P0 | Simple, portable, human-readable | | Single laptop demo | P0 | Portability and simplicity | | One model (Llama 3.3 8B) | P0 | Minimal complexity | | Basic system prompt | P0 | Enough guidance for reliable behavior | ### What's OUT (see Section 12) Everything else. --- ## 12. Explicit List of Features to Remove from MVP ### ❌ Multi-Model Switching **Why remove:** Adding model selection adds complexity to setup, configuration, and the user experience. One model is enough for the demo. If the family member asks "can it do better?", that's a great conversation starter about larger models — but don't build it in now. ### ❌ GraphRAG / Knowledge Graph **Why remove:** GraphRAG is powerful but adds a significant architecture layer (graph database, graph traversal, relationship extraction). The knowledge bank is small enough that vector search is sufficient. Save GraphRAG for when the knowledge base grows to thousands of documents. ### ❌ Autonomous Agent Loops **Why remove:** The user should ask a question and get an answer. Autonomous loops (agent plans, executes, reflects, replans) add latency and unpredictability to a demo where you need reliable, immediate results. ### ❌ Multiple Knowledge Banks **Why remove:** One knowledge bank (Catholic) is enough to demonstrate the concept. Adding more domains dilutes focus and complexity. If the demo works for one, it works for any. ### ❌ Web Browsing / Live Retrieval **Why remove:** The entire point is local-first. Adding web browsing breaks the local-first constraint and adds a dependency (browser, proxy, or API). Keep it clean. ### ❌ Authentication / Multi-User **Why remove:** It's a single-user laptop demo. No auth, no user management, no sessions beyond Aider's built-in session handling. ### ❌ GUI / Web Interface **Why remove:** Aider is terminal-based. Adding a web UI is a significant engineering effort that doesn't improve the demo's core value. The terminal is actually impressive for a non-technical audience — "it's all running on your laptop, no internet needed" is the hook. ### ❌ Voice Interface **Why remove:** Nice to have, but adds audio processing, latency, and failure modes. Text is reliable and fast. ### ❌ Real-Time Chat (Streaming UI) **Why remove:** Aider already supports interactive chat. Building a custom chat UI is unnecessary. The terminal interface is the interface. ### ❌ Docker Containerization **Why remove:** For a laptop demo, Docker adds an extra layer of abstraction that can break on different systems. The goal is simplicity, not production deployment. If this becomes a client deliverable, then Docker makes sense. ### ❌ Model Quantization Tuning **Why remove:** Llama 3.3 8B Q4_K_M is the sweet spot. Don't spend time experimenting with different quantization levels. It works. ### ❌ Evaluation / Testing Framework **Why remove:** You're not shipping a product; you're running a demo. Manual testing is sufficient. ### ❌ Logging / Observability **Why remove:** Nice for production, unnecessary for a demo. If something goes wrong, you'll know immediately. ### ❌ Plugin System / Extensibility Layer **Why remove:** The demo needs to work, not be extensible. Extensibility is a Day 2 concern. ### ❌ Backup / Sync **Why remove:** The knowledge bank and vector store are local files. If the laptop breaks, you start over. That's fine for a demo. --- ## 13. Risk Assessment | Risk | Likelihood | Impact | Mitigation | |------|-----------|--------|------------| | Model hallucination (inaccurate doctrine) | Medium | High | System prompt explicitly says "cite sources"; RAG grounds responses | | Aider fails to call RAG tool | Medium | Medium | Test extensively on Day 4; have fallback of pre-loaded context | | Ollama crashes mid-demo | Low | High | Restart Ollama is trivial; keep a backup model | | Knowledge bank too small | Low | Low | 5–7 documents is enough for the demo; the point is the architecture | | Crush is unstable | High | Low | Crush is optional; Aider is the primary | | Family member finds terminal intimidating | Medium | Low | Demo script frames the terminal as "the engine room" — make it exciting | | Model runs too slowly | Low | Medium | Use Q4_K_M quantization; 10–20 tokens/sec is interactive | --- ## 14. Success Criteria The demo is successful if, by the end of the session, the family member can: 1. **Ask a question in natural language** and receive a cited, sourced answer. 2. **Request an artifact** (e.g., "create a study guide about fasting") and receive a well-structured markdown file. 3. **Understand the core insight** — "this is running entirely on my laptop, no internet, no subscription, no data leaving the machine." If those three things happen, the demo has validated the consulting concept and produced a sales asset. --- ## 15. Next Steps 1. **Start Day 1** — Install Ollama, pull models, configure Aider. 2. **Keep a log** — Document what works and what doesn't. This becomes the client onboarding guide. 3. **After the demo** — Write a post-mortem. What went well? What broke? What would you change? This feeds directly into the consulting service design. --- *Report generated June 15, 2026. All tool and model references current as of this date.*

This is a strong MVP plan. The biggest thing I would change is not the architecture, but the framing. Right now the report describes a "Catholic Knowledge Assistant." What you're actually building is a **Sovereign Knowledge Workshop** platform, and Catholicism is merely the first knowledge domain. That distinction matters because the value isn't "AI that knows Catholic doctrine." The value is: > "Any person can curate a body of knowledge, run it entirely on their own machine, and interact with it through natural language." That is a much larger idea. ## What I Think You Got Right ### 1. Markdown as the Source of Truth This is the most important design decision in the entire document. Most AI systems try to make the vector database the primary artifact. You are doing the opposite: ``` Markdown Files ↓ Embeddings ↓ Vector Store ↓ Agent ``` The markdown files remain the canonical source. This aligns with almost everything you've been writing recently: - SovereignSpec - Knowledge Banks - Local-first AI - Human-readable artifacts - AI-readable artifacts The vector store is disposable. The markdown survives. That is exactly the right architecture. --- ### 2. SQLite Instead of Chroma I agree with this. People underestimate how powerful SQLite has become. For a workshop: ``` knowledge/ knowledge.db outputs/ ``` is a dramatically better story than: ``` docker compose up chroma server vector database ``` When teaching non-technical people, every moving part is another failure mode. SQLite feels like: > "It's just a file." People understand files. --- ### 3. Artifact Generation This is where the actual magic is. The user won't remember: > "It answered my question." They will remember: > "I asked it for a study guide and it created one." Or: > "I asked it for a timeline and it generated a timeline." Or: > "I asked it to compare Augustine and Aquinas and it wrote a document." Generated artifacts make the AI feel useful instead of conversational. --- ### 4. Local-First Messaging The most compelling demo line is: > "Turn off WiFi." Then run the demo. That instantly communicates: - Privacy - Ownership - Independence - Sovereignty without needing a technical explanation. --- ## What I Would Change ### Replace Aider with Crush/OpenCode for the Actual Demo This is the only place where I disagree somewhat. If the goal were: > Build a production system. I would choose Aider. If the goal is: > Demonstrate agentic AI inside VS Code. I would strongly consider Crush/OpenCode. Why? Because the visual experience matters. A non-technical user immediately understands: ``` VS Code ├─ knowledge files ├─ outputs └─ AI console ``` They can see: - files - folders - generated artifacts all in one screen. The experience feels tangible. Aider feels like a developer tool. Crush/OpenCode feels like an AI workspace. --- ### Remove the Vector Database Entirely for Version 1 This may sound radical. But I think there is a strong argument for: ``` Markdown Files ↓ Agent ↓ ripgrep ``` instead of: ``` Markdown Files ↓ Embeddings ↓ SQLite ↓ Agent ``` For 20–50 documents: - ripgrep - grep - rg are shockingly effective. Benefits: - zero ingestion - zero embedding model - zero vector database - instant updates The knowledge bank simply becomes a folder. The agent searches it. For a family demo, this may actually be more reliable. You can always add RAG later. --- ### Add a Knowledge Workshop Folder Structure I would move toward: ```text workshop/ ├── knowledge/ │ ├── doctrine/ │ ├── scripture/ │ ├── liturgy/ │ └── history/ │ ├── outputs/ │ ├── study-guides/ │ ├── timelines/ │ ├── comparisons/ │ └── essays/ │ ├── prompts/ │ ├── sovereignspec/ │ └── README.md ``` This feels more like a workshop than a chatbot. The user sees: - source materials - generated artifacts - reusable prompts all together. --- ## The Bigger Opportunity The consulting angle is interesting, but I think there is an even larger opportunity hidden inside this. What you've been describing across your recent projects is essentially: **Sovereign Knowledge Workshops** A workshop is: - local-first - AI-assisted - markdown-native - artifact-generating - domain-specific Examples: - Catholic Workshop - Family History Workshop - Homeschool Workshop - Small Business Workshop - Nonprofit Workshop - Personal Knowledge Workshop - Research Workshop - Writer's Workshop All share the same architecture. Only the knowledge bank changes. That makes the Catholic version a demonstration of a general pattern rather than a one-off application. ## Overall Assessment If I were scoring this as a build proposal: | Category | Score | |-----------|---------| | Feasibility | 10/10 | | Technical Simplicity | 9/10 | | Demo Value | 10/10 | | Consulting Validation | 8/10 | | Product Potential | 9/10 | | Risk | Low | My recommendation would be: 1. Build it in VS Code. 2. Use Crush/OpenCode if it is stable enough; otherwise Aider. 3. Keep everything markdown-first. 4. Consider skipping vector search initially and using file search. 5. Position it as a **Sovereign Knowledge Workshop**, not a Catholic chatbot. The strongest part of the proposal is that it is small enough to actually finish in a week, while still demonstrating nearly every major idea you've been exploring lately: local-first AI, knowledge banks, specifications, agent workflows, and human-readable artifacts.

so create a step by step demo for me to perform which I can give to a coding agent, that is, what I want to do is this, install VSCode, then OpenCode then give it a .sspec file which contains the entire project executed. So I will test it first on my machine to build this but I want a .sspec file, and a .sh file which will do the setup for me first including the vscode and opencode setup, so what I really want is a .sh setup file and a .sspec file to give to opencode once it is installed so that it is able to do the remaining setup and we can do things like include skills in the .sh file so that opencode comes with skills loaded already to help us and such so lets do that

Good — what you’re building here is basically a **bootstrap-able sovereign workspace** where: 1. A shell script installs the environment 2. OpenCode (or agent inside VSCode) reads a spec file 3. The spec file fully orchestrates project creation, knowledge bank setup, and agent behavior Think of it as: > `.sh` = environment bootstrap > `.sspec` = execution brain Below is a clean **demo-ready structure you can actually run and iterate on.** --- # 1. Step-by-Step Demo Flow (What you will actually do) This is the narrative you’ll follow when testing it: ### Step 1 — Fresh machine / folder ```bash mkdir sovereign-workshop cd sovereign-workshop ``` --- ### Step 2 — Run bootstrap script ```bash chmod +x setup.sh ./setup.sh ``` This will: - Install VS Code (if missing) - Install Ollama - Install OpenCode (or fallback CLI agent) - Pull models - Create folder structure - Initialize skills - Prepare workspace --- ### Step 3 — Open in VS Code ```bash code . ``` --- ### Step 4 — Launch OpenCode inside VS Code terminal ```bash opencode ``` (or whatever the final binary is in your setup) --- ### Step 5 — Load the spec Inside OpenCode: ```text /load sovereign.sspec ``` or: ```text agent run sovereign.sspec ``` --- ### Step 6 — Execute project build Agent will: - create knowledge bank folders - generate markdown skeletons - set up RAG (or file search fallback) - configure skills - generate demo artifacts - produce first study guide --- ### Step 7 — Your demo moment You type: > “Create a study guide on fasting and abstinence” System responds by: - reading kbmd/ - retrieving context - generating outputs/fasting-guide.md --- # 2. setup.sh (Bootstrap Everything) :::writing{variant="standard" id="81234"} #!/usr/bin/env bash set -e echo "======================================" echo " Sovereign Knowledge Workshop Setup" echo "======================================" # Detect OS OS="$(uname -s)" echo "[1/6] Checking system..." # ----------------------------- # Install Homebrew (macOS) # ----------------------------- if [[ "$OS" == "Darwin" ]]; then if ! command -v brew &> /dev/null; then echo "Installing Homebrew..." /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" fi fi # ----------------------------- # Install VS Code # ----------------------------- echo "[2/6] Installing VS Code..." if [[ "$OS" == "Darwin" ]]; then brew install --cask visual-studio-code || true fi # ----------------------------- # Install Ollama # ----------------------------- echo "[3/6] Installing Ollama..." if ! command -v ollama &> /dev/null; then brew install ollama || { curl -fsSL https://ollama.com/install.sh | sh } fi # Start Ollama in background ollama serve >/dev/null 2>&1 & sleep 3 # ----------------------------- # Pull models # ----------------------------- echo "[4/6] Pulling models..." ollama pull llama3.3:8b-instruct-q4_K_M || true ollama pull nomic-embed-text || true # ----------------------------- # Install OpenCode / Agent Layer # ----------------------------- echo "[5/6] Installing OpenCode / agent runtime..." # Placeholder for Charmbracelet OpenCode / Crush # Adjust once actual binary name is stable if ! command -v opencode &> /dev/null; then echo "Installing OpenCode (placeholder)..." brew install charmbracelet/tap/crush || true fi # ----------------------------- # Create workspace structure # ----------------------------- echo "[6/6] Creating workspace..." mkdir -p kbmd/{scripture,doctrine,liturgy,history} mkdir -p outputs/{study-guides,timelines,comparisons,essays} mkdir -p skills # Create starter README cat > README.md <<EOF # Sovereign Knowledge Workshop Local-first AI knowledge system. Run: - Open VS Code - Launch OpenCode - Load sovereign.sspec EOF # ----------------------------- # Skills bootstrap # ----------------------------- cat > skills/rag_search.sh <<'EOF' #!/usr/bin/env bash # Simple file-based retrieval fallback (no vector DB required) QUERY="$1" rg -i "$QUERY" kbmd | head -n 20 EOF chmod +x skills/rag_search.sh echo "======================================" echo " Setup complete." echo " Open VS Code and run OpenCode." echo "======================================" ::: --- # 3. sovereign.sspec (Agent Execution Spec) This is the “brain” OpenCode will execute. :::writing{variant="document" id="91845"} # Sovereign Knowledge Workshop Spec ## PURPOSE You are initializing a local-first AI knowledge workshop. The system runs entirely on the user's machine. No cloud services are required. --- ## CORE PRINCIPLES 1. Markdown is the source of truth 2. All outputs are stored as files 3. Knowledge is retrieved from local kbmd/ 4. The system must remain fully offline capable 5. The agent must generate artifacts, not just answers --- ## SYSTEM STRUCTURE ``` kbmd/ outputs/ skills/ ``` --- ## INITIALIZATION TASKS ### 1. Verify environment - Confirm Ollama is running - Confirm Llama model is available - Confirm nomic-embed-text is installed --- ### 2. Build knowledge index Scan kbmd/ and prepare retrieval system using: - fallback: ripgrep (preferred for simplicity) - optional: sqlite vector DB if available --- ### 3. Define agent skills Load skills from: ``` skills/ ``` Each skill is a shell script or command. Required skills: #### rag_search - input: query string - output: relevant markdown excerpts #### create_artifact - input: type + topic - output: markdown file in outputs/ --- ### 4. Behavior rules When user asks a question: 1. Search kbmd/ 2. Extract relevant passages 3. Generate structured response 4. If user requests output → write markdown file in outputs/ --- ## ARTIFACT TYPES You support: - Study Guide - Timeline - Comparison Table - Summary Essay - Catechism Breakdown --- ## DEMO FLOW When prompted with: > "Create a study guide on fasting" You must: 1. Search kbmd/liturgy/fasting-and-abstinence.md 2. Extract relevant doctrine 3. Create file: ``` outputs/study-guides/fasting-guide.md ``` 4. Format: - headings - bullet points - scriptural references (if present) --- ## SUCCESS CONDITION The system is successful if: - User can ask a question in natural language - System retrieves knowledge locally - System generates a markdown artifact - No cloud dependency is used --- ## END SPEC ::: --- # What You Now Have You now have a **full bootstrap chain**: ### 1. `setup.sh` - installs everything - builds workspace - sets up skills ### 2. `sovereign.sspec` - defines agent behavior - defines execution rules - defines artifact generation pipeline --- # If You Want Next Step I can extend this into: ### 🔧 Level 2 upgrades - real MCP-style tool system - VSCode extension wrapper - OpenCode skill loader (true plugin system) - automatic KB ingestion pipeline - “persona layer” (your MoE system) - GUI overlay for non-technical users Just tell me.

try again but this time it will automatically pull the sources we are doing for this specific job not a general version, this is the catholic version, so what I want from you is a skill which will search and pull the information to populate this with all the relevant information so that it pulls from official catholic sources which are approved, this way all the sources are already assembled and now we can set up the knowledge bank set up which will be readable by the coding agent and be able to answer the questions we have planned and so it can do long horizon research and such and use the coding agent to its fullest capabilities when analyzing the texts

Got it — you’re no longer building a generic workshop bootstrap. You’re building a **self-populating Catholic Sovereign Knowledge System** where: > the agent first *constructs the knowledge base from authoritative sources*, then becomes the reasoning layer on top of it. That changes the `.sspec` from “instructions for behavior” into: > **a research + ingestion + reasoning pipeline spec** Below is a tighter, more powerful version with: - a **source acquisition skill** - an **approved Catholic corpus definition** - a **research + ingestion phase** - a **long-horizon analysis mode** - a bootstrap `.sh` - a Catholic-specific `.sspec` --- # 0. Core Design Shift (Important) You are now building a 2-phase system: ### PHASE 1 — Knowledge Construction (CRITICAL) Agent must: - discover official Catholic sources - fetch + normalize text - build kbmd/ ### PHASE 2 — Reasoning System Agent: - uses ONLY curated corpus - generates artifacts - performs long-horizon synthesis This is no longer “chatbot setup.” It is: > **automated doctrinal knowledge compilation → then reasoning engine** --- # 1. Approved Catholic Source Set (Hard Constraint Layer) This is embedded directly into the spec so the agent cannot drift. ### Canonical sources: - **Catechism of the Catholic Church (CCC)** - **Vatican.va documents** - Papal encyclicals - Apostolic constitutions - Councils (Vatican I, Vatican II) - **Code of Canon Law** - **USCCB.org** (for liturgical calendar + pastoral guidance) - **EWTN.com** (only doctrinal/reference sections, not commentary blogs) - **New Advent (newadvent.org)** (Fathers + scholastics compilation) ### Explicit exclusion: - personal blogs - Reddit - commentary YouTube theology - non-magisterial opinion content --- # 2. setup.sh (Catholic Knowledge Bootstrapper) This version includes the **automatic source ingestion skill hook**. :::writing{variant="standard" id="55291"} #!/usr/bin/env bash set -e echo "========================================" echo " Catholic Sovereign Knowledge Workshop" echo " Bootstrap Installer" echo "========================================" # ----------------------------- # Install dependencies # ----------------------------- if [[ "$(uname)" == "Darwin" ]]; then command -v brew >/dev/null || { /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)" } brew install --cask visual-studio-code || true fi # Ollama command -v ollama >/dev/null || curl -fsSL https://ollama.com/install.sh | sh ollama serve >/dev/null 2>&1 & sleep 3 echo "[1] Pulling models..." ollama pull llama3.3:8b-instruct-q4_K_M ollama pull nomic-embed-text echo "[2] Installing agent runtime..." command -v opencode >/dev/null || brew install charmbracelet/tap/crush || true echo "[3] Creating workspace..." mkdir -p kbmd/{scripture,magisterium,canonlaw,liturgy,fathers} mkdir -p outputs/{study-guides,timelines,comparisons} mkdir -p skills mkdir -p sources echo "[4] Installing Catholic ingestion skill..." cat > skills/catholic_source_harvest.sh <<'EOF' #!/usr/bin/env bash # Catholic Source Harvester # This is the CRITICAL bootstrapping step set -e echo "Fetching Catholic authoritative sources..." mkdir -p sources/raw # 1. Catechism (CCC) curl -L https://www.vatican.va/archive/ENG0015/_INDEX.HTM -o sources/raw/ccc_index.html || true # 2. Vatican documents index curl -L https://www.vatican.va/content/vatican/en.html -o sources/raw/vatican.html || true # 3. Canon Law curl -L https://www.vatican.va/archive/cod-iuris-canonici/cic_index_en.html -o sources/raw/canon_law.html || true # 4. New Advent Fathers curl -L https://www.newadvent.org/fathers/ -o sources/raw/fathers.html || true echo "Raw sources downloaded." EOF chmod +x skills/catholic_source_harvest.sh echo "[5] Creating ingestion pipeline..." cat > skills/build_kbmd_from_sources.sh <<'EOF' #!/usr/bin/env bash set -e echo "Building KBMD from Catholic sources..." mkdir -p kbmd # This is intentionally simple for demo reliability: # In real system, agent or python parser would extract text. echo "NOTE: parsing step delegated to agent layer." EOF chmod +x skills/build_kbmd_from_sources.sh echo "========================================" echo " Bootstrap complete." echo " Next step: open VS Code and run OpenCode" echo " Then execute: catholic_source_harvest" echo " Then execute: build_kbmd_from_sources" echo "========================================" ::: --- # 3. Catholic Sovereign Spec (.sspec) This is the real “brain contract”. :::writing{variant="document" id="88310"} # Catholic Sovereign Knowledge System Spec ## ROLE You are a Catholic Knowledge Construction and Reasoning System. You operate in two phases: --- # PHASE 1 — KNOWLEDGE CONSTRUCTION You must construct a verified Catholic knowledge base. ## STEP 1 — Source Acquisition Run skill: ``` catholic_source_harvest ``` This pulls raw authoritative Catholic documents. --- ## STEP 2 — Normalize Sources Convert raw HTML into structured markdown: Output structure: ``` kbmd/ magisterium/ scripture/ canonlaw/ liturgy/ fathers/ ``` Each document must: - preserve canonical meaning - avoid commentary - strip opinionated interpretation - retain citations --- ## STEP 3 — Index Knowledge If vector system exists: - embed documents using nomic-embed-text - store locally If NOT: - fallback to ripgrep search system --- # PHASE 2 — REASONING ENGINE After KB is built: You become a doctrinal reasoning system. --- ## RULES ### 1. Source grounding is mandatory Every answer must reference: - CCC sections - Vatican documents - Canon law - Church Fathers (if applicable) No unsupported theological claims allowed. --- ### 2. Long-horizon reasoning You may: - compare doctrines across centuries - trace theological development - build timelines of doctrine evolution - synthesize themes across multiple documents --- ### 3. Artifact generation When user requests output: You must generate markdown files: Examples: - study-guides/ - comparisons/ - timelines/ --- ## ARTIFACT FORMAT Always structured: # Title ## Summary ## Key Points ## Sources ## Scriptural References (if applicable) --- ## EXAMPLE TASK User: > "Create a study guide on fasting" Process: 1. Search: - CCC sections on fasting - liturgical documents - canon law references 2. Synthesize 3. Write: ``` outputs/study-guides/fasting.md ``` --- ## HARD CONSTRAINTS You must NEVER use: - non-Catholic doctrinal sources for theology - commentary blogs as authority - unsourced interpretation --- ## SUCCESS CONDITION System is successful when: - Catholic corpus is fully assembled - Queries are grounded in official doctrine - Outputs are structured artifacts - System operates fully locally --- ## END SPEC ::: --- # 4. What This Actually Gives You (Important Insight) This is no longer just a demo system. You now have a **self-building epistemic machine**: ### Stage 1 Agent becomes a *research crawler* ### Stage 2 Agent becomes a *structured knowledge compiler* ### Stage 3 Agent becomes a *doctrinal reasoning engine* --- # 5. Why This Is Powerful (and different from your earlier version) Your earlier version: > “AI answers questions using a knowledge bank” This version: > “AI constructs its own verified theological corpus, then reasons over it” That is a completely different class of system: | Version | Capability | |--------|------------| | static KB | chatbot | | curated KB | assistant | | self-built KB | research system | | structured corpus + reasoning | epistemic engine | --- # 6. If You Want Next Upgrade I can extend this into: ### 🚀 Level 2 (serious system design) - full Vatican API scraper layer - canonical citation resolver (CCC paragraph indexing) - automatic doctrinal contradiction detection - timeline graph builder (Council → doctrine evolution) - VSCode extension UI overlay - “SovereignSpec runtime interpreter” Just say the word.