metric-validation-harness

Empirically validates a software metric before trusting or optimizing it — point it at any candidate metric (a command that takes a path and prints one number) plus a corpus, and it runs experiments that try to falsify each property a good metric must have. Checks determinism (same input, same number across runs and hash seeds), invariance to cosmetic edits (also an anti-gaming probe), monotonicity under construct-increasing edits, discrimination, robustness on edge inputs, near-linear tractability, and construct validity (convergent, discriminant vs LOC, predictive AUC, lift over a baseline). Trigger whenever someone proposes, reviews, tunes, or ships a metric, score, or index, asks "is this metric any good", suspects a score tracks LOC or jumps between runs, or builds a deterministic optimization target. It is the empirical companion to the deterministic-metric-design skill and is read-only.

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Repository: pproenca/dot-skills
Letzte Quellaktivität: 5. Juli 2026 um 07:23
Erkannte Sprache von SKILL.md: Englisch
Sterne: 194
Forks: 17

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.

npx skills add https://github.com/pproenca/dot-skills --skill metric-validation-harness

Der Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.

Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

Datei-Explorer

27 Dateien

SKILL.md

Quellanweisungen · Schreibgeschützte Vorschau

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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.

2026-07-24

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.

2026-07-23

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.

2026-07-23

Alle aus diesem Repository ansehen

Metric Validation Harness

Point this harness at a candidate metric and a corpus, and it runs experiments that try to falsify each property a trustworthy, optimizable metric must have. It is the empirical companion to deterministic-metric-design: that skill tells you to prove monotonicity, invariance, determinism, and construct validity; this skill runs the experiment and reports PASS/FAIL, each result mapped to the design-skill category it checks.

Read-only. It computes and reports; it never modifies your metric, the corpus, or any external state. Safe to run unsupervised.

When to Apply

Someone proposes, reviews, tunes, or ships a metric / score / index and you need evidence it is sound

A score "feels off" — you suspect it tracks LOC, jumps between runs, or saturates

You are about to let an agent optimize a metric and need to know it can't be gamed by cosmetic edits

You built a candidate per deterministic-metric-design and want to empirically confirm the properties you argued for

You are choosing between two metrics and need to know which actually predicts the outcome (and beats a trivial baseline)