principle-type-system-discipline
Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.
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- 2026-09-20
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01 前言 Front Matter
- name
- principle-type-system-discipline
- description
- Apply when designing types, reviewing a function signature, or writing code in any statically-typed language. Make illegal states unrepresentable, brand semantic primitives, parse external data at boundaries, refuse to lie to the compiler, exhaust variants, derive from authoritative schemas.
02 资源文件 1
- 许可证LICENSE1.0 KB
03 SKILL.md 正文
Type System Discipline
The type checker is a proof assistant. Use it to eliminate impossible states, mismatched primitives, and unhandled variants at compile time. Anything you let through as runtime data becomes a runtime failure the compiler could have stopped.
Applies to any typed language. Skills like typescript-best-practices ground it in specific syntax.
The patterns:
- Make illegal states unrepresentable. Model variants as sum types: discriminated unions in TypeScript, enums with payloads in Rust/Swift/Kotlin, sealed classes in Scala, ADTs in Haskell/OCaml. Don't model state as a bag of optional fields where contradictory combinations compile. A subtle anti-pattern worth naming:
{ completed: boolean; completedAt?: Date }admitscompleted: true; completedAt: undefined, which is meaningless. Derive the boolean from a single source likecompletedAt !== null, or model the variants explicitly as{ kind: 'open' } | { kind: 'done'; at: Date }. If a bug forces the question "wait, can this combination actually happen?", the type is too loose. - Brand semantic primitives.
UserIdandOrderIdare strings underneath but should not be interchangeable. Newtypes in Rust, opaque types in Swift, value classes in Kotlin, phantom types in Haskell, branded intersections in TypeScript. Validate once at creation, trust the type downstream. - External data is untyped until parsed. RPC payloads, JSON, IPC messages, CLI args, config files, environment variables, database rows. Have a parse function at every boundary that turns unstructured input into the typed model. Read
brain/principles/boundary-discipline.mdfor validation placement. - Don't lie to the type system. Casts, unsafe coercions, and assertion functions that bypass the compiler are runtime crashes waiting to happen. If the compiler can't prove a fact, prove it (validate, narrow, refine the model) or accept that the cast is a hazard. The cast you bury today is the postmortem you write next week.
- Exhaustive matching is the compiler's job. When you match on a sum type, the compiler must fail compilation if a new variant is added without handling. Use the idiom your language provides:
never-typed binding in TypeScript, unannotatedmatchin Rust,-Wincomplete-patternsin Haskell, sealed-class match exhaustiveness in Kotlin. - Derive types from authoritative schemas. When a protocol buffer, OpenAPI spec, GraphQL schema, database migration, or design-system token file defines a shape, derive from it instead of hand-rolling a parallel type. Manual duplication drifts. Read
brain/principles/encode-lessons-in-structure.md. - Prefer compile-time over runtime. Every runtime assertion, null check, and
instanceofis admitting the type system isn't carrying its weight. Push the check up to the type.
The tests:
- "Can I write a comment explaining when this combination of fields is valid?" If yes, the type is too loose. Split it into a sum type.
- "Do two of my function arguments share a primitive type but mean different things?" Brand them.
- "Where did this
any, thisas, thisassertNotNullcome from?" Trace it to the boundary and validate there instead. - "If a new variant is added next month, will the compiler tell the next agent where to add a case?" If no, the match isn't exhaustive.
- "Is this type duplicating a shape another file owns?" Derive instead.
04 同仓库更多 Skills
writing-shape
Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.
writing-fragments
Grilling session that mines the user for fragments — heterogeneous nuggets of writing (claims, vignettes, sharp sentences, half-thoughts) — and appends them to a single document as raw material for a future article. Use when the user wants to develop ideas before imposing structure, or mentions "fragments", "ideate", or "raw material" for writing.
writing-beats
Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.
stop-slop
Remove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.
humanizer
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, and filler phrases.
feedback
Prepare, deliver, and debrief timely interpersonal feedback using a question-first coaching flow, observable behavior, owned experience, and concrete impact. Use when drafting feedback, coaching managers, rehearsing a feedback conversation, praising specific behavior, or reviewing whether feedback landed.