AI Development Quality Discipline
skill · reused
Evidence record
- What is verified
- Uses in published workflows: 1
- Revision
- Version 0.1.5
- Validation status
- Public-sanitized publication; no independent validation is claimed.
What this is and why
Five working disciplines for AI-assisted development: decomposition into methods of at most fifty lines, tests before code with edge-only mocking, an approved architecture skeleton before implementation, one-method-at-a-time focus, and deliberate context management. It presumes gathered requirements and an explicit definition of done before coding starts. The work leaves behind pre-written tests, a named test for every clause of a success criterion, and a clean linter run after each TDD cycle.
Where it was applied and what it helped deliver
Metadata and provenance
- id
- datarim-skill-ai-quality
- type
- skill
- version
- 0.1.5
- origin
- created in Arcanada
- lifecycle
- public_sanitized
- tags
- skill, talo-0029
The source is the private knowledge repository; only the sanitized public projection reaches this site.
verified · datarim-skill-ai-quality · v0.1.5