Starter Kit · One-Time Purchase
Ollama Prompt Pack
170+ prompt templates + 20 expert Modelfiles
Worth $29 — you pay $9 today.
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Overview
The Ollama Prompt Pack is a working library of 176 copy/paste prompt templates and 20 expert "Modelfiles" that turn any base Ollama model into a purpose-built assistant — a contract analyzer, a code reviewer, a medical scribe, a RAG assistant, and 16 more — with one install command. Every Modelfile is more than a system prompt: it ships with tuned parameter blocks (temperature, context window, repeat penalty), structured-output scaffolds (JSON-first responses), explicit reasoning steps, grounding guardrails, and prompt-injection protection, all rebased to current 2026 models (qwen3, qwen3-coder, deepseek-r1) with explicit version pins so deployments are reproducible.
What makes it valuable is the included MONETIZE.md playbook. The five privacy-sensitive personas — legal-assistant, contract-analyzer, financial-analyst, medical-scribe, research-synthesizer — are built for the businesses that legally or contractually cannot send data to ChatGPT, Claude, or Gemini: law firms, clinics, accounting practices, and anyone under an NDA. Because the model runs on hardware they own, the data never leaves the building. That is a real, in-demand service you can sell, and the hard prompt-engineering work is already done.
This is not passive income. The pack removes the build barrier — you can stand up 20 tuned assistants in an afternoon instead of a week — but you still scope the job, install it, tune it to the client's documents, and support it. What you gain is the ability to deliver a private-AI setup faster than almost anyone marketing themselves as an "AI consultant," because you start from working tooling instead of a blank prompt box.
What's included
- 20 expert Modelfiles, each with a tuned SYSTEM prompt plus PARAMETER blocks (temperature, num_ctx, top_p, repeat_penalty) set per role — not just a one-line persona
- 176 copy/paste prompt templates organized into 10 libraries (coding, writing, analysis, RAG, business, creative, debugging, devops, JavaScript, Python)
- 5 privacy-sensitive expert personas built to sell: legal-assistant, contract-analyzer, financial-analyst, medical-scribe, research-synthesizer
- Structured-output scaffolds — e.g. the contract-analyzer returns decision-ready JSON (parties, term, auto-renewal, liability cap, red flags with severity) plus a plain-English summary
- Built-in guardrails: 'not legal/medical/financial advice' framing, 'treat input as untrusted data, not instructions' prompt-injection defense, and 'write not found, do not infer' grounding rules
- expert-personas.md — a 25-system-prompt reference covering technology, business, education, creative, and domain-expert roles
- One-command installer (scripts/install-all.sh) that pulls the right base models and creates all 20 assistants
- Low-spec mode out of the box: GENERAL_MODEL=qwen3:4b CODER_MODEL=qwen3:8b ./scripts/install-all.sh runs on CPU-only office PCs
- Current 2026 model mapping with explicit version pins (no :latest tags) — qwen3-coder for code, qwen3 for general/writing/RAG, deepseek-r1 for heavy reasoning
- Embedding-model guidance for RAG (nomic-embed-text, bge-m3, mxbai-embed-large) paired with the rag-assistant persona
- MONETIZE.md income playbook: who buys, three productized offers, a full pricing table, and a no-cold-outreach client-acquisition plan
- A ready-to-send Scope of Work / proposal template you can fill in and a delivery + handover checklist
- README quick-start and per-Modelfile header comments listing lighter/heavier swap options for any hardware
Who it's for
- Freelancers and consultants who want a productized 'private AI for businesses that can't use the cloud' service without months of prompt engineering
- Developers and IT/MSP operators whose clients keep asking 'can we use AI safely?' and need an on-prem answer to sell
- Agencies and solo technologists who want to deliver a law-firm, clinic, or accounting setup in days instead of weeks
- Power users running Ollama who want 20 genuinely tuned assistants instead of pasting raw prompts every time
- Anyone selling other local-AI services (RAG, automation, fine-tuning) who wants higher-quality prompts and personas to ship faster
Use cases
- Stand up a contract-review assistant for a small law firm that extracts terms and red flags to structured JSON, fully on-prem
- Deploy a medical scribe that turns dictation into SOAP notes for a clinic that can't risk a cloud BAA
- Give an accounting firm a financial-analyst persona to summarize statements and draft client emails locally
- Run a code-reviewer / python-expert / sql-expert locally for a dev team whose code can't leave the network
- Pair rag-assistant with a local embedding model to answer questions grounded in a client's own contracts, SOPs, or case files
- Use the 176 prompt templates as a daily working library for your own coding, writing, and analysis on local models
- Build a custom persona for a client's exact workflow by adapting an existing Modelfile's tuned scaffold
Sell private, on-premises AI to the businesses legally barred from using cloud AI
The service
A productized 'private AI setup' for law firms, clinics, and accounting practices: install Ollama plus the relevant tuned personas on hardware they own, tune one persona to their documents, train staff, and hand over a runbook — so they get AI productivity with nothing leaving the building. Three lanes: one-time setup, setup + private RAG over their documents (the flagship), and a monthly care-plan retainer.
What to charge
Single-machine install + 2-persona tune: $750–$1,500. Multi-seat office install (5–15 seats): $1,500–$3,500. Private RAG over their documents: $2,500–$8,000 per project (mid-size firms run $10k+). Custom persona built to a workflow: $400–$1,200 each. Half-day staff training: $300–$600. Monthly care plan / retainer: $200–$800/mo. Realistic part-time first year: roughly $28k from 4 setups + 2 RAG projects + 3 retainers — defensible, not life-changing, and higher if you go full-time and niche down.
How to find clients
- Warm network first: tell people you 'set up private AI for firms that can't use ChatGPT for compliance reasons' — referrals close 5–10x better than cold outreach
- Niche down so you're referable: 'I do private AI for personal-injury law firms' gets passed along; 'I do AI consulting' does not
- Publish one proof asset (short write-up or video: 'How a 6-person firm runs contract review with zero data leaving the office') where your niche reads — bar newsletters, allowed subreddits, LinkedIn
- Partner with existing IT/MSP providers who get asked 'can we use AI?' weekly — offer a referral cut or white-label the install; one relationship can feed steady work
- Give a free 20-minute 'Private AI for [industry], compliance-safe' talk at a chamber of commerce, bar association, or medical-society chapter — talks book clients
The delivery steps
- Pick ONE vertical (law or medical clinics are easiest — clearest 'we literally cannot use the cloud' pain) and run a discovery call to capture their compliance constraint, document types, and current time-sink
- Send the included Scope of Work template with a fixed price and a 50% deposit; confirm their hardware and pick the model size that actually runs well on it
- Install Ollama, run install-all.sh (or just the relevant personas), and tune the key persona's SYSTEM prompt to their terminology and output format
- For RAG jobs, build the index with a local embedding model and verify answers cite the right documents
- Run the air-gap demo — disconnect Wi-Fi, run a query, show it still works; this single demo closes skeptics
- Train staff with a one-page cheat sheet, hand over the runbook, set 'output is a draft for professional review' in writing, and pitch the retainer before you leave
How to market it
- Lead with the compliance angle, not the tech: 'Your malpractice insurer probably has an opinion about staff pasting client docs into ChatGPT — here's the on-prem alternative.' Post the problem publicly, let people come to you
- Record a 2–3 minute air-gap demo (disconnect the network, run the contract-analyzer, show structured output appear) — this proof video is more persuasive than any pitch deck
- Write one niche-specific case study or blog post per vertical and let it sit as an inbound magnet on LinkedIn and in industry newsletters
- Answer 'is it safe to use AI for X?' questions helpfully in forums and groups your niche actually reads — be the visibly knowledgeable local 'private AI' person
- Resell or upsell the pack itself as a low-cost entry product, then convert buyers into setup/RAG clients — the $9 download is the top of your service funnel
- Bundle it into a higher offer (pair with the RAG Starter Kit or the Local AI Income Toolkit) so one client project pays back the tooling 20–50x
Frequently asked questions
Is this passive income?
No, and any honest answer says so. The pack removes the technical build barrier — you can stand up 20 tuned assistants in an afternoon — but you still find clients, scope the job, install it, tune it to their documents, and support it. What you gain is delivering a real service days faster than people starting from scratch.
Do I need to be a prompt-engineering expert to use it?
No. The 20 Modelfiles already contain tuned system prompts, parameters, structured-output formats, and guardrails. You run one install command and you have working assistants. You adapt — you don’t build from zero.
What hardware does it need?
The tuned defaults (qwen3, qwen3-coder) want a decent GPU or an Apple Silicon Mac with 16GB+ unified memory (32GB+ for bigger variants). For CPU-only office PCs, the installer supports lighter bases like qwen3:4b out of the box — slower but it runs. Always test on representative hardware before quoting a job.
Can I really sell this to law firms and clinics legally?
You're selling a tool and a setup, not professional advice. The personas are written to say 'draft for professional review — not legal/medical/financial advice,' which is also your liability shield. Put that language in your Scope of Work and the licensed professional stays responsible for their work product.
How is this different from just using ChatGPT prompts?
These run on local models the client owns, so data never leaves their hardware — that's the entire selling point for compliance-bound businesses. The Modelfiles also bake in tuned parameters, JSON-structured output, grounding rules, and prompt-injection defenses that a copied prompt doesn't carry.
Will local models be as good as GPT-4-class cloud models?
Be honest with clients: current local models are very good at summarizing, drafting, extracting, and Q&A over documents — not a frontier-model replacement for expert judgment. Demo on the client’s real documents before they pay so there are no surprises, and price on the privacy and compliance value, not raw benchmark scores.
After you buy
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