Area guide
Front-end
Accessible components, forms, responsive layouts, design tokens, and Storybook coverage.
atomic-component-factory -> a11y-compliance-auditor -> storybook-automator
AI Engineering Operating Library
A new way to work with AI across every technical area
Logic Basics gives teams 64 structured skills for Codex, Claude Code, Cursor, Copilot, ChatGPT, and similar agents, covering delivery, architecture, security, data, research, scale readiness, onboarding, and diagnosis. The premium pack is distributed through Lemon Squeezy for a simple one-time purchase.
Portable .skills folder pattern. Works across the tools your team already uses.
.skills/master-skills
1# Logic Basics
2# AI engineering operating library
3
4copy .skills/scalability-failure-profiler.md
5copy .skills/gap-discovery-facilitator.md
6
7// 64 ready-to-use skills available
8// 12 workflow chains available
9// Review evidence required: YES
10// Codex, Claude, Cursor, Copilot: READY
64
Total skills
35
Premium skills
12
Workflow chains
8
Categories
It is not a Prompt Pack
Logic Basics is a new way to work with AI: reusable operating procedures for each area of engineering and research, with validation evidence built in.
64 Ready-to-Use Skills
Structured Markdown skills for frontend, backend, database, data science, research, fullstack, utility, and project management work.
Base and Premium Tiers
Use base skills for focused repeatable tasks and premium skills for higher-risk architecture, governance, scale, and diagnosis.
Diagnostic Growth Workflows
Answer questions like what is missing, what am I not seeing, what breaks at 100k users, and how should juniors start.
Didactic Usage Guide
Clear instructions for copying skills into a project-level .skills folder and prompting Codex, Claude, Cursor, Copilot, or ChatGPT.
Validation and Catalog
A generated skills-catalog.json and validator script keep metadata, README coverage, version tracks, and skill structure aligned.
Recommended Chains
Pre-build diagnosis, scale readiness, junior ramp-up, feature delivery, security review, and data quality workflows are ready to run.
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.
Area guide
Back-end
REST contracts, webhooks, event-driven flows, authentication, consistency, and observability.
webhook-contract-guardian -> event-driven-orchestrator -> observability-stack-composer
Area guide
Database
Migrations, query tuning, ORM modeling, rollback strategy, CDC, and data movement.
migration-safety-checker -> sql-query-tuning-doctor -> database-version-rollback
Area guide
Data Science
EDA, experiment evaluation, KPI stories, feature stores, forecasting, and governed ML work.
exploratory-data-analysis-guide -> ab-test-evaluator -> ml-experiment-tracking-governor
Area guide
Research
Literature evidence, protocols, notebooks, scientific claims, grants, physics, and bioinformatics.
literature-evidence-mapper -> scientific-claim-auditor -> grant-manuscript-reviewer
Area guide
Project Management
Gap discovery, hidden requirements, readiness, capacity, dependencies, roadmaps, and onboarding.
gap-discovery-facilitator -> hidden-requirement-radar -> scope-readiness-checker
Area guide
Utility
Testing, commits, docs, environment config, CI/CD, Docker, observability, and scale readiness.
unit-test-coverage-forcer -> cicd-security-hardener -> conventional-commit-helper
Area guide
Fullstack
Vertical slices across API, data, UI, state, validation, tests, security, and release notes.
vertical-slice-feature-craft -> unit-test-coverage-forcer -> cicd-security-hardener
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
How It Works
A small project-level skill pack turns vague agent requests into repeatable team behavior.
01
Choose the Workflow
Start from a real engineering question: what is missing, what breaks first, how do we test this, or how should a junior begin?
02
Install Into .skills
Copy selected Markdown skills into a project-level .skills folder so every agent and teammate can reuse the same operating procedure.
03
Require Evidence
Ask the agent to follow the skill workflow and return decisions, changed files, validation evidence, risks, and next steps.
The Difference Is Specificity
Weak prompts create generic output. Skill-guided prompts force assumptions, trade-offs, validation evidence, risks, and a reviewable result.
Weak prompt
Can this app handle 100k users?
Skill-guided prompt
Apply scalability-failure-profiler. State traffic assumptions, rank bottlenecks, list missing metrics, and propose mitigation order.
Workflow chain
Pre-build diagnosis: gap-discovery-facilitator -> hidden-requirement-radar -> scope-readiness-checker.
Browse All Workflows
Use these chains when one skill is too narrow. Each card includes the recommended sequence and a copyable starter prompt.
New fullstack feature
When: Use when a feature touches API, persistence, UI, tests, and release safety.
vertical-slice-feature-craft -> unit-test-coverage-forcer -> storybook-automator -> cicd-security-hardener
Use this workflow: vertical-slice-feature-craft -> unit-test-coverage-forcer -> storybook-automator -> cicd-security-hardener to run the new fullstack feature workflow for [feature]. Return changed files, tests run, validation evidence, risks, and changelog notes.
Incremental modernization
When: Use when improving old code without changing behavior or taking a risky rewrite path.
legacy-code-modernizer -> docstring-artisan -> conventional-commit-helper
Use this workflow: legacy-code-modernizer -> docstring-artisan -> conventional-commit-helper to run the incremental modernization workflow for [module]. Preserve behavior, modernize in small steps, add useful docstrings where they reduce maintenance risk, then prepare a conventional commit and changelog summary.
Data performance
When: Use when slow data access may require schema, query, ORM, or caching changes.
prisma-typeorm-generator -> sql-query-tuning-doctor -> cache-strategy-selector
Use this workflow: prisma-typeorm-generator -> sql-query-tuning-doctor -> cache-strategy-selector to run the data performance workflow for [query or endpoint]. Review ORM model/query shape, diagnose SQL performance, then recommend caching only where it has clear invalidation and consistency rules.
Secure external integration
When: Use when connecting to third-party systems, webhooks, queues, or async delivery.
webhook-contract-guardian -> event-driven-orchestrator -> observability-stack-composer
Use this workflow: webhook-contract-guardian -> event-driven-orchestrator -> observability-stack-composer to run the secure external integration workflow for [integration]. Validate contracts, idempotency, retries, event orchestration, failure handling, observability, and operational alerts.
Delivery planning
When: Use before committing teams to dates, dependencies, sprint goals, or roadmap order.
backlog-refinement-facilitator -> sprint-capacity-planner -> cross-team-dependency-mapper -> roadmap-priority-orchestrator
Use this workflow: backlog-refinement-facilitator -> sprint-capacity-planner -> cross-team-dependency-mapper -> roadmap-priority-orchestrator to run the delivery planning workflow for [initiative]. Refine backlog items, estimate capacity, map cross-team dependencies, and recommend roadmap priority with trade-offs and risks.
Data science delivery
When: Use when analysis must become a decision, metric, dashboard, or governed experiment.
exploratory-data-analysis-guide -> ab-test-evaluator -> kpi-dashboard-storyliner -> ml-experiment-tracking-governor
Use this workflow: exploratory-data-analysis-guide -> ab-test-evaluator -> kpi-dashboard-storyliner -> ml-experiment-tracking-governor to run the data science delivery workflow for [dataset or experiment]. Produce EDA findings, experiment validity checks, KPI dashboard story, and tracking/governance recommendations.
Scientific research review
When: Use when papers, grants, or manuscripts need evidence mapping, claim calibration, reviewer objections, and revision strategy.
literature-evidence-mapper -> scientific-claim-auditor -> grant-manuscript-reviewer
Use this workflow: literature-evidence-mapper -> scientific-claim-auditor -> grant-manuscript-reviewer to run the scientific research review workflow for [paper, grant, or research question]. Map the evidence, audit claim strength and causal language, identify missing controls or citations, then recommend manuscript or grant revisions with validation evidence.
Computational science reproducibility
When: Use when research data, notebooks, simulations, or calculations need provenance, rerun evidence, unit checks, and sanity validation.
research-data-cleanroom -> computational-notebook-reproducer -> physics-model-sanity-checker
Use this workflow: research-data-cleanroom -> computational-notebook-reproducer -> physics-model-sanity-checker to run the computational science reproducibility workflow for [dataset, notebook, or simulation]. Preserve raw data, document cleaning, make the notebook rerunnable, check model assumptions or units, and return validation evidence plus residual risks.
Project start and evaluation
When: Use when starting a project, evaluating a client request, or deciding whether work is ready to build.
project-kickoff-charter-builder -> scope-readiness-checker -> technical-due-diligence-assessor -> delivery-feasibility-evaluator
Use this workflow: project-kickoff-charter-builder -> scope-readiness-checker -> technical-due-diligence-assessor -> delivery-feasibility-evaluator to run the project start and evaluation workflow for [project]. Build the kickoff charter, assess scope readiness, perform technical due diligence, and evaluate delivery feasibility.
Diagnostic pre-build review
When: Use when someone asks 'what is missing?' or 'what necessity am I not seeing?' before implementation.
gap-discovery-facilitator -> hidden-requirement-radar -> scope-readiness-checker
Use this workflow: gap-discovery-facilitator -> hidden-requirement-radar -> scope-readiness-checker to run the diagnostic pre-build workflow for [idea or plan]. Identify visible gaps, hidden requirements, missing owners, risk areas, validation gaps, rollout needs, and readiness blockers.
Scale readiness review
When: Use when asking what breaks first at 100k users or before a growth event.
scalability-failure-profiler -> cache-strategy-selector -> observability-stack-composer
Use this workflow: scalability-failure-profiler -> cache-strategy-selector -> observability-stack-composer to run the scale readiness workflow for [system]. State traffic assumptions, rank first failure points, decide whether caching is justified, and define missing metrics, dashboards, alerts, and load-test evidence.
Junior ramp-up
When: Use when bringing juniors into a project without exposing them to unclear or high-risk work.
junior-onboarding-path-builder -> backlog-refinement-facilitator -> conventional-commit-helper
Use this workflow: junior-onboarding-path-builder -> backlog-refinement-facilitator -> conventional-commit-helper to run the junior ramp-up workflow for [team or project]. Create a 30/60/90 path, safe starter tasks, review checkpoints, mentorship rhythm, backlog items, and commit conventions.
Choose the Pack That Matches Your Workflow
Start with the base materials, then upgrade to the complete premium library when you want every skill, workflow chain, and area guide in one purchase.
Starter
Community Pack
Selected base skills, catalog browsing, usage guide, and install instructions for individual developers.
Free / lead magnet
Best First Product
Pro Workflow Pack
Full library, premium diagnostics, scale readiness, governance workflows, and .skills installation guidance, delivered through Lemon Squeezy.
$29/one-time
Make AI Coding Output Easier to Trust
Install a small skill pack into your project, point your agent at the workflow, and require validation evidence before accepting the result.
One-time digital product checkout powered by Lemon Squeezy.