radical-simplification

Cognitive moves for collapsing complexity — reframe, clarify, reduce, decompose, invert, constrain, transfer, generalize, audit — distilled from the working method of mathematicians, physicists, and software engineers known for turning hard problems into simple solutions (Pólya, Feynman, Hamming, Brooks, Knuth, Dijkstra, Lamport, Tao, Grothendieck, Munger, Hofstadter). Use when stuck on a complex problem, when a proposed design feels overengineered, when reviewing code that has accreted accidental complexity, or when the user wants an elegant solution to a hard engineering or product problem. Triggers on phrases like "this feels too complicated", "we are going in circles", "there must be a simpler way", "interview me about this plan", "find the underlying problem", and on stuck-state moments where forward search has run out and the agent needs a different angle.

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Repository: pproenca/dot-skills
Last source activity: May 27, 2026 at 12:57
Detected SKILL.md language: English
Stars: 194
Forks: 17

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drizzle-nextjs-postgres

Drizzle ORM against PostgreSQL inside a Next.js App Router app. Covers client construction (globalThis singleton across HMR, serverless pool sizing, `prepare:false` behind PgBouncer/Supavisor, driver choice when you need interactive transactions, `server-only`), reads in Server Components under Next.js 16 Cache Components (`use cache` superseding `unstable_cache` and the `revalidate`/`dynamic` segment configs, Suspense boundaries, React `cache()` dedupe), Server Actions (authorization inside the action, `updateTag` vs `revalidateTag`, `after()`), Postgres schema types (timestamptz, identity vs serial, jsonb, numeric-as-string, bigint modes), drizzle-kit migrations (generate vs push, CONCURRENTLY outside the migrator, NOT VALID constraints, rename prompts), transactions and pooled connections, and Postgres query traps (keyset pagination, count cost, driver-dependent `db.execute()` shape, NOT IN nulls, prepared statements). Use when writing or reviewing Drizzle + Postgres code in Next.js.

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relational-data-modeling

Corrects the wrong defaults a model has when designing a relational schema — the DDL decisions an experienced engineer makes differently. Use when creating or reviewing tables, migrations, ER models, or ORM schema definitions. Covers identity and keys (surrogate vs natural, identity vs serial, uuidv7, composite keys that make cross-tenant references impossible), relationships (polymorphic foreign keys the database cannot enforce, referential actions, unindexed FK columns, disjoint subtypes), invariants the engine can prove instead of application code (EXCLUDE, partial unique indexes, CHECK limits, deferrable cycles, NOT VALID), types (timestamptz, exact money, range types, enum vs lookup table), derived and encoded data (generated columns, JSONB as an escape hatch), and time (events vs in-place updates, soft-delete flags that silently disable constraints, temporal keys). NOT for query tuning, index selection for read paths, or connection pooling.

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relational-database-design

Distills a logical relational-database design methodology into rules an agent applies while designing or reviewing a schema. Covers the design sequence (mission → tables → fields → keys → relationships → business rules → views → integrity review), one-subject-per-table decomposition, atomic single-valued fields, candidate/primary/foreign keys, relationship types with deletion rules and participation, the four levels of data integrity, database-vs-application business rules, validation tables, views for derived data, and the flat-file / spreadsheet / RDBMS-driven antipatterns to avoid. The structure is logical and RDBMS-agnostic, and normalized by construction. Use when designing a new relational schema, reviewing or refactoring an existing one, resolving redundant or repeating data, choosing keys, modeling relationships, or deciding where a constraint belongs.

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Radical Simplification — Cognitive Moves for Collapsing Complex Problems

Distillation of the documented working method of mathematicians, physicists, and engineers who consistently turn complex problems into simple solutions. The skill is not a step list — it is a toolbox of cognitive moves, each correcting a specific wrong default a capable model has when faced with complexity. The moves are mostly orthogonal; pick the one that matches the symptom.

This is the thinking layer that sits above refactoring (code-simplifier), metric design (deterministic-metric-design), and reviews (design-review). Those skills apply a methodology to a known shape of artifact. This skill is the methodology itself — how to arrive at the simple shape in the first place.

When to Apply

Use this skill when:

The user says the problem feels too complicated, that the team is going in circles, or that there must be a simpler way

A proposed design has accreted parameters, dependencies, or branches and feels overengineered

A plan has been drafted but unresolved decisions remain, and the agent is about to commit to assumed answers