Turn scattered knowledge into guided work
LogicBasics is a structured knowledge system. Explore canonical Knowledge Objects, follow an outcome-based Guided Path, and use generated Packs only when their distribution gate is approved.
Public beta: 13 canonical objects. Premium and commercial distribution remain gated.
Not Prompts. Area Operating Systems.
Each area gets focused skills, recommended chains, and review standards. The point is not to ask AI harder; it is to make AI-assisted work easier to trust.
Data Project Kickstart
I have many data files and do not know where to begin. Build a file inventory, assess initial quality, define the problem, prepare a workspace, and finish with an analysis plan and readiness checklist.
Follow the data pathFull-Stack MVP Launch
Turn an idea into a working MVP with a clear scope, initial architecture, API contract, data model, backlog, implementation plan, and launch checklist.
Follow the MVP pathProject Evolution Audit
Understand an existing project, identify architecture risks and technical debt, then turn opportunities into prioritized next steps and an evolution roadmap.
Beta preview: explore PacksNot your area? Check out other areas
How It Works
Three practical steps from a relevant problem to useful work in your own project.
Download your Pack
Choose the situation that matches your work. Each Pack gives you a clear starting point and the knowledge behind it.
Copy it into your project
Keep the Pack beside your work or install its selected Knowledge Objects into your project’s .skills folder.
Start using the path
Follow the Recipe, use the Templates, and check the result. People get direction; AI agents get structured context.
Built for Teams Already Using AI
The strongest buyers are not asking whether AI can write code. They are asking how to make AI-assisted work consistent, reviewable, and safe to scale.
Software Agencies
Standardize AI-assisted delivery across client projects without forcing every developer to invent their own prompting style.
- Reusable kickoff and delivery workflows
- Consistent review evidence
- Safer junior contribution paths
Startup CTOs
Run senior-level diagnosis before scaling, shipping risky features, or committing to unclear technical plans.
- Gap and hidden requirement discovery
- 100k-user failure profiling
- Architecture and operational trade-offs
Engineering Managers
Give teams shared AI operating standards for implementation, validation, onboarding, observability, and governance.
- Project-level .skills installation
- Repeatable workflow chains
- Less reviewer guesswork
Research Teams
Bring evidence mapping, claim review, reproducibility checks, and manuscript revision workflows into AI-assisted science work.
- Literature and evidence mapping
- Scientific claim auditing
- Notebook and model reproducibility
Start with an outcome, then follow the evidence
Browse the canonical Library or take the Data Analysis Foundation Guided Path. The public beta keeps availability, relationships, and validation context visible.
No checkout is activated in this beta. Commercial distribution requires a later product decision.