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Oleksandr Bezdieniezhnykh c51cb9b4a5 Enhance coding standards and testing rules

- Updated coderule.mdc to emphasize readability, meaningful comments, and maintainability.
- Revised testing.mdc to set a 75% coverage threshold for business logic and clarified expected results requirements.
- Improved clarity in git-workflow.mdc regarding commit message formatting and length.
- Added completeness audit requirements in research steps and quality checklists to ensure thoroughness in test specifications.

Made-with: Cursor

2026-04-17 20:28:55 +03:00

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Comparison & Analysis Frameworks — Reference

General Dimensions (select as needed)

Goal / What problem does it solve
Working mechanism / Process
Input / Output / Boundaries
Advantages / Disadvantages / Trade-offs
Applicable scenarios / Boundary conditions
Cost / Benefit / Risk
Historical evolution / Future trends
Security / Permissions / Controllability

Concept Comparison Specific Dimensions

Definition & essence
Trigger / invocation method
Execution agent
Input/output & type constraints
Determinism & repeatability
Resource & context management
Composition & reuse patterns
Security boundaries & permission control

Decision Support Specific Dimensions

Solution overview
Implementation cost
Maintenance cost
Risk assessment
Expected benefit
Applicable scenarios
Team capability requirements
Migration difficulty

Decomposition Completeness Probes (Completeness Audit Reference)

Used during Step 1's Decomposition Completeness Audit. After generating sub-questions, ask each probe against the current decomposition. If a probe reveals an uncovered area, add a sub-question for it.

Probe	What it catches
What does this cost — in money, time, resources, or trade-offs?	Budget, pricing, licensing, tax, opportunity cost, maintenance burden
What are the hard constraints — physical, legal, regulatory, environmental?	Regulations, certifications, spectrum/frequency rules, export controls, physics limits, IP restrictions
What are the dependencies and assumptions that could break?	Supply chain, vendor lock-in, API stability, single points of failure, standards evolution
What does the operating environment actually look like?	Terrain, weather, connectivity, infrastructure, power, latency, user skill level
What failure modes exist and what happens when they trigger?	Degraded operation, fallback, safety margins, blast radius, recovery time
What do practitioners who solved similar problems say matters most?	Field-tested priorities that don't appear in specs or papers
What changes over time — and what looks stable now but isn't?	Technology roadmaps, regulatory shifts, deprecation risk, scaling effects

2.3 KiB Raw Blame History