principle-foundational-thinking
Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.
- License 许可
- MIT
- 最近更新
- 2026-11-04
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01 前言 Front Matter
- name
- principle-foundational-thinking
- description
- Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.
- user-invocable
- false
03 SKILL.md 正文
Foundational Thinking
Structural decisions protect option value. Code-level decisions protect simplicity.
Data structures first. Get the data shape right before writing logic. Define core types early, trace every access pattern, and choose structures that match the dominant paths.
At code level, DRY the structure, not every line. Types and data models should converge. Three similar statements still beat a premature abstraction. Prefer explicit over clever. Test behavior and edge cases, not line counts.
Concurrency corollary. Before sharing state between actors, ask "what happens if another actor modifies this concurrently?" If not "nothing", isolate.
Scaffold first. If something helps every later phase, do it first. Ask "does every subsequent phase benefit from this existing?" CI, linting, test infrastructure, and shared types are scaffold. Sequence for option value: setup before features, tests before fixes. Keep commits small and single-purpose.
Each increment should land a coherent abstraction or deepen one that exists. Do not spread a new capability across callers as special-case coordination.
Subtraction comes before scaffolding. Remove dead code first, then lay foundations.
04 同仓库更多 Skills
work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, stacked PRs, or keeping tests and docs with code.
why
Use for 'why does X work this way', 'why we picked Y', design rationale, regressions, postmortems, or data-backed thresholds. Discovers available MCPs and queries each evidence category (source control, issue tracker, long-form docs, real-time chat, infrastructure observability, error tracking, product analytics warehouse) in parallel, then returns a cited read on decisions and tradeoffs. Use how for runtime behavior.
what-did-i-get-done
Summarize authored commits over a user-specified time period into a concise update
unslop
Cut AI tells from any writing.
typescript-best-practices
TypeScript best practices. Use when reading or editing any .ts or .tsx file.
thermo-nuclear-code-quality-review
Run an extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Use for a thermo-nuclear code quality review, thermonuclear review, deep code quality audit, or especially harsh maintainability review.