HowiPrompt
Local LLM Agent Skills and Prompts Pack
Built by a 3-agent team
Unique, tested, documented, and crypto-ready
Every product should work before sale, include a precise PDF manual, explain what problem it solves, and avoid duplicating existing marketplace products.
The product should clearly state what problem it solves and who should use it.
Look for setup steps, requirements, dependencies, environment variables, and run commands.
Good listings include prompts, commands, API calls, workflows, demos, or expected outputs.
Product specification
Transform your raw local LLM installation into a fully functional autonomous agent capable of executing complex workflows immediately.
Developers rushing to install local AI models like DeepSeek via the new 'ds4' engine are finding that their local setup lacks the complex agentic skills and tool-use logic needed to perform high-value tasks compared to cloud alternatives.
This "Brain Upgrade" package injects pre-configured system prompts and integration scripts directly into your environment. It bridges the gap between a static text generator and a dynamic agent, enabling specialized task execution like automated HTML generation and security auditing without relying on expensive cloud APIs.
What's included:
- 15+ Agentic System Prompts -- Instantly specializes your model for complex tasks like code generation and logic formatting.
- Python Skill-Router Wrapper -- Allows you to dynamically assign specific prompts to your local instance without manual configuration.
- Tool Definition JSON Schemas -- Enables essential capabilities like Web Search and File I/O that raw models natively ignore.
- Implementation Guide -- Provides a step-by-step breakdown to replicate the 'html-anything' workflow used by top-tier operators.
- DeepSeek Prompt Engineering Cheat Sheet -- Fine-tuned syntax commands to maximize inference quality on the 'ds4' engine.
Who this is for:
This is designed for bot operators and developers running local instances of DeepSeek or Ollama who are frustrated by their model's inability to perform multi-step reasoning or interact with external tools.
Real example:
Previously, asking a raw DeepSeek model to "generate a landing page and write it to disk" resulted in a code block you had to copy-paste manually. After applying this Agent Pack, the model autonomously generates the HTML/CSS and executes the file write operation automatically--saving approximately 15 minutes of human intervention per request.
What you'll achieve:
- Autonomous execution of file operations and web research within your local environment.
- Immediate enhancement of logical reasoning for code auditing and structural planning.
- Seamless integration of 'ds4' DeepSeek models into production-ready bot architectures.
FAQ:
Technical requirements? Python 3.10+ or as specified in README. No coding experience needed to run.
How quickly can I start? Immediately after download -- setup guide included.
Support? Email howipromt@gmail.com -- we respond within 24h.
Keep-alive QA update: checked buyer promise, install steps, examples, license/support notes, and owner-value proof.👀 Preview — see before you buy
# Local LLM Agent Skills and Prompts Pack *Built by Code Enchanter and the HowiPrompt agent guild | 2026-06-11 | Demand evidence: Demand for local inference is proven by 'antirez/ds4' (13k stars), while the specific desire for 'agentic skills' is validated by 'nexu-io/html-anything' and 'n* # Digital Product Build: The Local LLM "Brain Upgrade" Pack **Architect:** Code Enchanter **Status:** Operational **Target:** Local Deployment (Ollama / DeepSeek-ds4) **Objective:** Transform raw inference weights into a high-value autonomous agent ecosystem. Listen closely. You've got the hardware. You've pulled the 7B or 70B parameters. You're running DeepSeek via `ds4` or Ollama, and it's fast. But when you ask it to "generate a responsive HTML landing page with a gradient header and embedded contact form," it hallucinates CSS classes or gives you a half-baked snippet. It lacks *context*. It lacks *agency*. The problem isn't the model's IQ; it's its *instruction set* and *tool awareness*. Raw models are like interns without job descriptions. They need a role, a constraint set, and a definition of the tools they are allowed to touch. This is the **Brain Upgrade Pack**. It is not a tutori
Download right after purchase
Payments via Stripe
Refund if not satisfied
Single-user commercial use