Dot Skills

nginx C module debugging guidelines based on the official nginx development guide. This skill should be used when debugging nginx C module crashes, memory bugs, request flow issues, or production problems. Triggers on tasks involving segfault analysis, coredump debugging, GDB inspection, memory leak detection, request phase tracing, AddressSanitizer setup, or nginx module troubleshooting.

Refactoring UI design patterns for Tailwind CSS applications. This skill should be used when writing, reviewing, or refactoring HTML with Tailwind utility classes to improve visual hierarchy, spacing, typography, color, depth, and polish. Triggers on tasks involving UI cleanup, design review, Tailwind refactoring, component styling, or visual improvements.

nginx C module directive design guidelines for creating admin-friendly configuration interfaces. This skill should be used when designing nginx module directives — deciding what to expose vs hardcode, naming conventions, scope placement, default values, variable design, and validation patterns. Triggers on tasks involving ngx_command_t design, directive naming, configuration API design, nginx module public interface, or directive deprecation.

React Native Elements UI component library best practices for performance, theming, and proper component usage. Use when building React Native apps with RNE, configuring themes, optimizing lists with ListItem, or reviewing RNE component code.

React Testing Library best practices for writing maintainable, user-centric tests. Use when writing, reviewing, or refactoring RTL tests. Triggers on test files, testing patterns, getBy/queryBy queries, userEvent, waitFor, and component testing.

Apply the Theory of Constraints (Goldratt's Five Focusing Steps) to find and fix the single bottleneck that caps a process, workflow, pipeline, or Agent Skill/plugin's throughput. Use whenever optimizing speed, lead time, cost, or token/context budget of a system — CI/build pipelines, dev value streams (idea→review→merge→ship), an Agent Skill's trigger/context flow, or runtime code paths — ESPECIALLY when you don't know where to optimize, a local speedup didn't improve the whole, work piles up at a stage, everything is busy but little ships, adding capacity didn't help, or a policy/rule (not a resource) is the limiter. It locates the constraint with measurement, then prescribes exploit → subordinate → elevate → repeat, and stops you optimizing non-constraints (the "mirage of the non-bottleneck"). Trigger even when the user just says "speed this up", "why is this slow", or "make this more efficient" without mentioning constraints or bottlenecks.

Inventing deterministic metrics — turning a fuzzy property like 'maintainability', 'risk', or 'how reducible this code is' into a deterministic, computable number an agent can trust and optimize. Covers the path from construct to adoption — operationalizing the construct, confronting computability limits (Kolmogorov, Rice) with sound proxies, picking the right measurement scale, proving properties (monotonicity, invariance, the Weyuker/Briand axioms), guaranteeing determinism, establishing construct validity (not just LOC in disguise), and hardening against Goodhart-style gaming when an agent optimizes the metric. Trigger when designing, reviewing, or validating a quantitative metric, score, measure, or index — and even when the user doesn't say 'metric' but wants to quantify, score, rank, or measure code/behavior, build a deterministic optimization target, or invent a measure for something previously unquantified (e.g., behavior-preserving codebase-size reduction).

Use this skill to gate iOS SwiftUI design and UX with a pass/fail adversarial review — a single blind reviewer subagent judges screens, diffs, or features against 56 decidable HIG-derived rules, working backwards from mandatory rendered evidence, simulator screenshots (light, dark, accessibility text size) and interaction recordings tiled into filmstrips. Covers navigation/IA (tab bars, five tabs max, system Back), modality and flow grammar (sheets, alerts, unsaved-content protection), layout (44pt targets, safe areas, Dynamic Type), color and contrast, typography floors, Liquid Glass, feedback states (empty, loading, error, launch), motion — filmstrip-primary except the spring-curve and Reduce Motion checks, so code alone never prescribes an unseen animation, and gratuitous decoration fails — plus haptics and craft. Fixes must be the minimal change, removal preferred over addition. Trigger before merging user-facing SwiftUI work. Verdicts only, never fixes; targets without a SwiftUI UI surface abort.

Rendering and perception layer for codebase-as-geohash-map visualisations — choosing what to encode on colour/size/position, picking perceptually honest colour scales (viridis/OKLCH, not rainbow), drawing tens of thousands of cells on Canvas2D + WebGL/deck.gl inside a 16ms frame budget, placing and decluttering labels, GPU or spatial-index picking, camera animation and level-of-detail crossfades, and keyboard plus screen-reader accessibility for the canvas. Sits on top of geohash-spatial-code-maps, which owns the geohash encoding, projection, tiling, and navigation math. Also covers nature-inspired rendering — Voronoi, circle packing, phyllotaxis, metaball hulls, edge bundling. Trigger even when the user does not say "visualisation" — if the work involves drawing, colouring, labelling, animating, or making navigable a code map, spatial heatmap, or large cell/point layer on the web, this is the skill.

Guided, scripted pipeline for running JSX/TSX/React codemods safely across large legacy codebases. Use when you need to scaffold a codemod, dry-run it, validate its findings, and apply it across many files (50 to 100k+) without breaking the build. Walks the full inner/outer loop with the Codemod CLI (JSSG, ast-grep, workflows). Triggers on large-scale refactor, legacy React migration, codemod a prop/API change, migrate components across the codebase, run a codemod safely, batched/resumable codemod apply, dry-run a codemod.

Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with dev/staging/prod environments, registry-based promotion, and served models. Walks seven phases with a developer who may have zero MLflow 3 experience — assess the codebase (scripted read-only audit), model the registry domain (per-environment model names, aliases, gates), stand up tracking per environment, restructure training code to MLflow 3 idioms, wire evaluation-gated promotion, serve and smoke-test, then run the ongoing MLOps loop. Use when asked to set up MLflow, migrate to MLflow 3, productionize model training and serving, or design a dev/staging/prod MLOps cycle. Pairs with the sibling mlflow-3 rule pack for every API decision.

Same behaviour in fewer, clearer lines — covers the judgment gaps that linters cannot catch (reinvention, wrong frame, hidden duplication, derived state, procedural rebuilds, speculative generality, defensive excess, type-system underuse). Trigger when reviewing, refactoring, or simplifying code — and even when the user doesn't explicitly ask for "simplification" but is reviewing code, refactoring, or asking "is there a shorter way to write this?". Complements knip/eslint/ruff/tsc by focusing on the conceptual modelling layer those tools cannot see.

Corrects the wrong defaults a model has when standing up self-hosted SonarQube Server as a continuous gate on AI-generated code, verified against docs.sonarsource.com in July 2026 (Server 2026.3, LTA 2026.1). Use when configuring SonarQube, designing a quality gate, or wiring a scan step into CI for AI-assisted work. Covers the setups that run green while measuring almost nothing — the fudge factor skips duplication and coverage conditions below 20 new lines and is on by default; duplication is never measured on test code; no `new_cognitive_complexity` metric exists to gate on; `sonar.host.url` now defaults to sonarcloud.io rather than localhost; a shallow clone degrades new-code attribution to timestamps. Also covers AI Code Assurance — the project flag, gate qualification, the deprecated Copilot autodetection — and that Community Build cannot analyze pull requests at all. NOT for SonarQube Cloud, IDE connected mode, or authoring custom analyzer rules.

Responsive UI transformation patterns for Tailwind CSS applications. This skill should be used when making interfaces responsive, refactoring layouts for multiple screen sizes, or reviewing responsive Tailwind code. Triggers on tasks involving breakpoint strategy, layout adaptation, responsive spacing, fluid typography, mobile navigation, touch interaction, responsive media, or data table responsiveness.

Use whenever you face a webpack-build problem that ends with needing to write a plugin — 26 production-shaped recipes covering bundle-size budgets, forbidden architectural imports, env-var validation, secret-leak prevention, build-info injection, asset manifests, license walking, SRI hashes, virtual modules (Vite-style), filesystem routing (Next.js-style), generated barrels, runtime-driven TS types, library replacement (react→preact), debug-stripping, conditional polyfills, dynamic banners, feature flags, build-duration regression tracking, desktop/Slack notifications, changed-chunks diffing, browser auto-open, gzip/brotli pre-compression, image optimization, type-based dist layout, empty-chunk cleanup, cache-busting query strings. Each recipe is a complete working plugin drawn from production patterns at Next.js, Storybook, webpack-contrib. Trigger when the user asks how to do X with webpack or whether a plugin exists for X. Companion to webpack-plugin-authoring.

WXT browser extension performance optimization guidelines. This skill should be used when writing, reviewing, or refactoring WXT browser extension code to ensure optimal performance patterns. Triggers on tasks involving WXT, browser extensions, content scripts, service workers, messaging, and extension APIs.

Use this skill to gate TypeScript and React application code with a pass/fail adversarial review of design-pattern usage — a single blind reviewer subagent judges a diff or file set against 18 decidable rules covering implicit state machines (boolean-flag lifecycles, useEffect chains, stored derived state, non-exhaustive union matches) and over-engineered OO/enterprise ports (getInstance singletons, factory and builder classes, single-method strategy classes, State/Visitor hierarchies, event buses inside a React tree, pass-through repositories, DI containers, single-implementation interfaces, component inheritance, logic-only HOCs, static-only classes, trivial accessors). Trigger it before merging TS/React work, when asked to gate or pass/fail pattern usage, or as a check that agent-authored code is not porting Java/C# idioms. It renders verdicts only; for teaching-style guidance use implementation-design-patterns or implementation-functional-patterns.

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.

Scaffolds Drizzle ORM + SQLite boilerplate — a new `drizzle.config.ts`, a singleton client with the right pragmas, per-table schema files with explicit primary keys/indexed foreign keys/relations()/inferred types, per-table repository modules with `.returning()` + `inArray()` + `.onConflictDoUpdate()`, or drizzle-zod validators. Produces convention-enforced templates for three drivers (better-sqlite3, libsql/Turso, bun:sqlite). Trigger even when the user doesn't say "scaffold" — phrases like "add a table for X", "set up Drizzle in this project", "wire up SQLite", "create a CRUD module for X", or "bootstrap the DB layer" should pull this in. Pairs with the `drizzle-sqlite` skill, which covers the 45 rules these templates encode — read it when an exception is required.

Use this skill when building an Elixir macro or a declarative DSL — a schema/spec/route/workflow language in the shape of Ecto.Schema, Absinthe, Plug.Router, or ExUnit — with quote/unquote, __using__, module attributes, and @before_compile. It corrects the wrong defaults a model makes once it commits to metaprogramming — putting logic inside the quote block, re-evaluating unquoted expressions, forgetting Macro.escape, fighting hygiene with var!, generating code ad hoc instead of accumulating declarations, and validating the DSL at runtime rather than at compile time. Applies to writing or reviewing macro/DSL code. NOT for deciding whether to use a macro at all — that gate belongs to staff-level-elixir and adversarial-elixir, which say prefer plain functions.

Comprehensive Expo React Native feature development guide. This skill should be used when building mobile app screens, navigation, data fetching, authentication, deep linking, or native UX patterns with Expo. Triggers on tasks involving Expo Router, React Native components, mobile forms, or app configuration.

Inngest event-driven functions in a Next.js (App Router) project — creating the client, the /api/inngest route handler, typed events with Zod schemas, durable event-triggered functions, scheduled cron functions, or fan-out orchestrators. Trigger when scaffolding Inngest patterns — and even if the user just says "add a background job", "process this async", "schedule X every hour", or "send an event when Y happens" in a Next.js codebase. Also trigger when reviewing existing Inngest code to enforce event-naming, function-id stability, and step-idempotency conventions.

Next.js 16 bundle-size and build-time optimization — runs a data-driven iteration loop: measure baseline → analyze top offenders → apply ONE recipe → re-measure → verify nothing broke (build + types + tests + no regression) → commit or revert. Built for Next.js 16 with Turbopack default, and falls back to webpack-mode tooling when the project hasn't migrated yet. Triggers on phrases like "First Load JS is huge", "bundle size", "build takes too long", "page is slow to TTI", "reduce bundle", "tree-shake", or when the user shares output from `next experimental-analyze` / `@next/bundle-analyzer` — even if the user doesn't say "optimize" explicitly.

Scaffolds React 19 / React 19.2 code in TypeScript — components, Server Component pages, client islands, form actions with useActionState, context providers, custom hooks, reducers, or document metadata + resource hints. Generates production-grade code that follows React 19 idioms (ref-as-prop, <Context value={...}>, useActionState, inline metadata, useSyncExternalStore) and refuses deprecated React 18 patterns (forwardRef, <Context.Provider>, useFormState, react-dom/test-utils). Trigger even when the user says "create a component", "new page", "add a form", "new hook", or "scaffold X" without explicitly mentioning React 19.

Processing, transforming, or moving datasets that may exceed RAM on a single low-compute box — covers memory discipline (streaming, generators, dtype shrinkage), I/O access patterns (sequential vs random, mmap, async), data formats (Parquet vs CSV vs JSON, predicate pushdown), chunking & batching, spill-to-disk (external merge sort, DuckDB/Polars), pipelining (bounded queues, backpressure, checkpointing), codec selection (zstd/lz4/gzip), concurrency for I/O-bound workloads (asyncio, threads, prefetch), and observability (iowait vs CPU%, rows/sec, py-spy/strace). Trigger on "process a large file", "stream this", "out-of-core", "OOM kill", "this is slow", or code with `pd.read_csv` of multi-GB files, `requests.get(...).content` on big bodies, `BytesIO` on unbounded inputs, per-row INSERTs, sequential `requests.get` loops, falling `tqdm` rates — even if I/O or memory isn't mentioned. Complement to computer-science-algorithms.

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.

Writing webpack 5 plugins — hook selection (compiler vs compilation, tap vs tapAsync, processAssets stages), the asset pipeline (emitAsset, source classes, info metadata, source maps), watch-mode and persistent caching (file/context/missing/buildDependencies), plugin lifecycle (constructor purity, multi-compiler isolation, shutdown cleanup), schema-utils validation, WebpackError reporting, jest-worker parallelism, and compatibility patterns (compiler.webpack namespace, peerDependencies, getCompilationHooks WeakMap). Patterns are drawn from production plugins like mini-css-extract-plugin, terser-webpack-plugin, compression-webpack-plugin, and Next.js's webpack plugins. Trigger when writing, reviewing, or debugging webpack 5 plugins — even if the user doesn't explicitly mention "best practices" — anytime an `apply(compiler)` method is being written, hooks are being tapped, or a plugin imports from `webpack-sources`, the rules in this skill apply.

Implementation guide for the 22 Gang of Four design patterns in TypeScript, distilled from refactoring.guru. Use this skill when writing, refactoring, or reviewing TypeScript that exhibits a pattern-shaped problem — class-explosion from inheritance, conditionals switching on type, tight coupling to concrete classes, tree-shaped models, runtime algorithm selection, undo/redo, snapshot-and-restore, state-dependent behavior, subscriber notification, or hiding subsystem complexity. Each pattern entry includes intent, problem, solution, applicability (when to use AND when NOT to use), a runnable TypeScript example, implementation steps, pros/cons, and relations to sibling patterns. Trigger even when no pattern is named — cues like "class getting unwieldy," "giant switch," "swap implementations at runtime," "combinatorial subclasses," "need undo," or "traverse a tree" are pattern-shaped. Covers all 5 Creational, 7 Structural, and 10 Behavioral GoF patterns.

Use this skill when writing, debugging, or reviewing ast-grep patterns, YAML rules, or codemods against TypeScript or React (.ts/.tsx) code — searching for JSX elements, props, hooks, imports, or type constructs, and rewriting them. Covers the TS/React-specific traps — the tsx-vs-typescript language split, JSX and TypeScript node kinds, fragment matching, rewrites, and the @ast-grep/napi API. Complements the general-purpose ast-grep skill (rule mechanics) — reach for this one whenever the target code is TypeScript or React.

Geohash encoding/decoding in TypeScript or Rust — bit interleaving, the base32 alphabet, precision and cell geometry, neighbour/adjacency computation, proximity and bounding-box queries, and geohash-backed spatial indexing. Also covers the "codebase as a navigable 2D map" pattern — projecting a codebase into a coordinate plane, geohashing it so prefixes become business-domain regions, and navigating it like Google Maps (zoom, tiles, level-of-detail, clustering, deep links). Trigger when implementing, reviewing, or debugging geohash work — even when the user does not say "geohash" but the work involves spatial hashing, Morton/Z-order codes, proximity search on lat/lon, or mapping and visualising code structure spatially. Contains 42 impact-ordered rules with TypeScript and Rust examples.

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.

Reverse-engineering a Sketch file (or Figma export with similar shape) into pixel-perfect React + CSS — the iteration mental model, tree reconstruction, layout inference algorithms, geometry math, visual-regression diffing, and the style/typography/path conversions that make "improvement without regression" enforceable. Trigger even if the user doesn't explicitly mention "algorithms" but is converting a design source into web code, building a design-to-code pipeline, or struggling to make incremental fidelity improvements without breaking previously-converted output.

Scaffolds Expo (React Native) iOS screens that follow Apple Human Interface Guidelines by construction — list, detail, form, modal sheet, native tabs layout, and settings screens, each wired with native navigation, FlashList, safe-area insets, semantic colors, SF Symbols, haptics, and empty/loading states. Generated code follows the expo-ios-hig rules so it passes expo-ios-hig-verify without rework. Each screen is TSX for Expo Router, not Swift. Trigger whenever the user wants to create, add, generate, or scaffold a new Expo screen, route, tab layout, or form for iOS — even if they don't mention HIG.

OpenAI Codex Rust coding patterns distilled from the codex-rs workspace. Use this skill whenever writing, reviewing, or refactoring Rust code — especially for async agents, CLI tools, sandboxing, secret handling, Ratatui TUIs, JSON-RPC protocols, tokio-based services, or any codebase that needs defensive panic discipline. Trigger even when the user does not explicitly mention Codex, because the patterns generalize to any production Rust workspace. Covers async cancellation, error enum design, process sandboxing, DNS-rebinding defense, credential hardening (zeroize/mlock/ctor), Cargo workspace architecture, wiremock-based fakes, insta snapshot testing, OpenTelemetry tracing, and Ratatui rendering.

React data-fetching patterns at scale — recommender carousels, infinite feeds, pages with many parallel fetches, dashboards. Covers request orchestration (parallelism, batching, deduplication), cache strategy (keys, normalization, staleTime, SWR), backend protection (concurrency caps, debounce/throttle, jittered retries, circuit breakers), prefetching (route loaders, hover/intent, idle, server hydration), failure resilience (AbortController, timeouts, error boundaries, stale fallback, idempotent mutations), and feed/carousel patterns (virtualization, cursor pagination, summary/detail split). Includes 5 ready-to-use scaffolding templates (resource query hook, carousel data loader, infinite feed, hover-prefetch link, request collapser). Trigger when building, reviewing, or refactoring React components that fetch data — even if the user doesn't explicitly mention "performance" or "scale".

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.

Use when implementing, reviewing, or debugging a Gherkin acceptance-test pipeline with mutation testing. Covers parser, JSON IR, generator, runtime, step handlers, test runner, mutator, value mutation rules, execution, result classification, reporting, project layout, conformance, and agent setup. Based on Uncle Bob's Acceptance Pipeline Specification. Trigger even when the user mentions Gherkin parsing, acceptance test generation, mutation testing for acceptance tests, or building a portable test pipeline.

Choosing or implementing an algorithm or data structure — asymptotic complexity, data-structure selection, sorting & searching, dynamic programming, graph algorithms, divide & conquer, greedy algorithms, string/sequence algorithms, and the at-scale toolbox (Bloom filters, HyperLogLog, Count-Min Sketch, reservoir sampling, consistent hashing, external merge sort, Aho-Corasick, MinHash/LSH). Trigger on tasks involving "what's the right algorithm for…", performance-critical code, code with nested loops over the same input, recursive solutions, shortest-path / scheduling / matching / DP problems, code review for accidental O(n²) blowup, and any "how do I do X at scale / on a stream / without enough RAM" question — even if the user doesn't explicitly mention "algorithm" or "complexity."

Personalisation and recommendation systems for a two-sided trust marketplace built on AWS Personalize — event tracking, dataset and schema design, two-sided matching, cold start, feedback loops, bias control, recipe selection, serving-time re-ranking, observability, and a diagnostic playbook for existing systems. Trigger when designing, building, debugging, reviewing, or improving such a system — and even when the user does not explicitly mention "AWS Personalize" but is working on recommendations, ranking, search, homepage personalisation, or anything that matches seekers and providers across a trust-based catalog.

Opinionated, backend-agnostic Next.js 16 (App Router) architecture — authorization at the data layer, server-side loading with cache()+Promise.all, mutations through next-safe-action + typed route handlers, client/server boundaries ('use client' at leaves + TanStack Query), forms with RHF + Zod, UI via shadcn/ui + Tailwind + Base UI + next-intl, request handling in proxy.ts (Next.js 16's renamed middleware), and a Turbo monorepo of @app/* packages confining the backend behind one data-access package. Examples use Supabase but every rule states the transferable principle. Use when writing, reviewing, or refactoring Next.js 16 code. Trigger on server actions, route handlers, RSC vs 'use client' placement, TanStack Query, RHF/Zod, proxy.ts, or monorepo package layout — even when the user doesn't say 'patterns' or 'best practices'.

Compose new Rails backend pages and refactor existing Rails UI to use premium blocks from templates/application-ui. Use when requests mention ERB views, Rails partials, admin/dashboard screens, Tailwind UI assembly, or replacing custom markup with existing premium blocks while preserving behavior, accessibility, and Turbo/Stimulus hooks.

Use this skill to gate Phoenix LiveView realtime UIs with a pass/fail adversarial review — a single blind reviewer subagent judges a diff or file set against 32 decidable rules covering state ownership and lifecycle (patch vs remount, callback load placement, connected-mount guards, form recovery), realtime data flow (broadcast placement, scoped topics, presence mechanisms), async responsiveness (blocking external calls, socket-copying closures, lifecycle-owned tasks, rendered failure states), render and wire efficiency (the constructs that silently disable HEEx change tracking), streams (growing collections in assigns, the stream DOM contract, bounded infinite scroll), component and context boundaries, client trust (per-event authorization, scoped lookups, live_session boundaries, revocation disconnects), and mechanism-presence interaction feedback (JS commands, in-flight feedback, debounce, hook contracts, overlay focus). Verdicts only, never fixes.

Advanced TypeScript — type-level programming, library/DSL APIs, declaration merging, modern language features at depth (decorators, using, const T, NoInfer, variance), and feature implementation patterns built on advanced types. Trigger on tasks involving recursive conditional types, infer patterns, mapped-type key remapping, variadic tuples, fluent builders with phantom state, schema-first inference (Zod/Valibot), end-to-end-typed API clients, finite state machines, module augmentation, and library-publishing concerns. Trigger even when the user does not say "advanced" — if the work involves type-level algorithms, library-author API design, or going beyond surface-level uses of TS 5.x features, this is the skill. Assumes the reader has absorbed the `typescript-refactor` skill — this one extends those patterns at depth, never restates them.

TypeScript's functional answers to the 22 Gang of Four classes — factory functions (Factory Method, Abstract Factory, Prototype, Memento), module-scope singletons, fluent immutable builders, wrapper functions (Adapter, Facade), native Proxy, WeakMap caches (Flyweight), discriminated unions with exhaustive match (State, Visitor, Composite), event emitters and signals (Mediator, Observer), pipelines and composition (CoR, Decorator), stream methods (Iterator), closures-as-commands, higher-order strategies, lambda placement. Use when reviewing TypeScript that has a class-shaped problem the GoF catalog solves with a hierarchy but where idiomatic TS reaches for a function, a tagged union, or a data structure. Each rule names the GoF pattern(s) it replaces and when the class form still wins. Trigger on "factory class", "singleton getInstance", "state machine class", "observer pattern", "AST visitor", "where do I put this lambda". Sibling to implementation-design-patterns.

Algorithmic complexity (Big-O) review — finding nested loops, N+1 queries, exponential recursion, quadratic string builds, and other accidental complexity blowups. Covers Python, JavaScript/TypeScript, Java, Go, and similar languages. Use whenever writing, reviewing, or refactoring code where Big-O matters. Trigger even when the user doesn't mention "Big-O" explicitly — if they're reviewing code for performance, refactoring a hot path, asking "why is this slow," or working with data that scales (loops, recursions, collections, ORM access), apply this skill to classify the time/space complexity and suggest the fix. Especially trigger on tasks like "review for performance," "find slow code," "make this faster," "this code is O(n²)," or when reading code that processes collections.

Scaffolds Expo / React Native design system components that obey the expo-design-system rules by construction — a variant-driven pressable primitive, a slot-based card surface, a typed text primitive, a labeled form field, a FlashList entity screen, a theme token group, and a Storybook variant catalog. Generated code uses Unistyles v3 variants instead of style props, ref-as-prop, design tokens, built-in accessibility, and web/iOS parity (`_web` hover/focus/cursor on interactive primitives), so it follows expo-design-system without rework. Output is TSX/TS using react-native-unistyles. Trigger whenever the user wants to create, add, generate, or scaffold a new shared UI component, primitive, design token group, or screen for the clinic mobile app — even if they don't mention the design system.

Expo React Native performance optimization guidelines. This skill should be used when writing, reviewing, or refactoring Expo React Native code to ensure optimal performance patterns. Triggers on tasks involving React Native components, lists, animations, images, or performance improvements.

Methodology for starting a new library-reference distillation skill — one that turns an external library (nuqs, zod, framer-motion, msw, react-hook-form, emilkowal-animations) into an idiomatic-usage rule pack — or evolving one against a new upstream release. Distills the conventions empirically shared across shipped library-ref skills in this repo — the source-priority ladder (docs → blog/changelog → issues → types → examples), version pinning that inverts with API velocity, the universal 4-tier category ladder (CRITICAL setup → HIGH isolation → MEDIUM composition → LOW edge cases), the 4-slot When-to-Apply template, the failure-gap exemplar heuristic (privilege production lessons over API restatement), and metadata.references[] as cite-set checksum. Triggers on "I want to write a skill for library X", "refresh against new upstream", "where should I source rules from", "what categories should this skill have", and on /dev-skill:new for a library-reference distillation.

Mapping out an unfamiliar codebase via NLP and graph algorithms — 40 algorithms across topic modelling, semantic embeddings, code graphs, repository mining, clone detection, IR-based bug localization, identifier linguistics, and complexity metrics. Trigger when hunting bugs across many files, scoping a new feature, identifying domain entities, or analyzing commit history — even if the user doesn't explicitly mention algorithms — apply when they ask "where does X live in this codebase?", "what is this codebase about?", "find duplicated logic", "what changed recently?", "who owns this code?", or "is this function risky?".

Migrating a JavaScript codebase to TypeScript — converting .js files to .ts, adding types to existing JS, or tightening a loosely-typed TS project toward strict mode. Covers tsconfig and allowJs strategy, incremental strict-flag ratcheting (noImplicitAny, strictNullChecks, noUncheckedIndexedAccess), typing public surfaces, replacing `any` and unsafe casts with `unknown` and narrowing, validating runtime boundaries (JSON, env, API responses), converting CommonJS to ESM and prototypes to classes, and the build/CI changes a migration needs. Trigger even when the user only says "add types", "turn on strict mode", or "convert this file to TypeScript", and especially on a mixed JS/TS repo. Distinct from general TypeScript refactoring — this is the migration act itself, performed file by file while keeping the build green.

JSON HTTP API design rules distilled from Stripe — resource modeling, identifier schemes, URL structure, request/response wire format, pagination, errors, idempotency, versioning, naming, webhooks, and authentication. Triggers on tasks involving OpenAPI specs, API design reviews, schema decisions, endpoint shaping, error envelope design, webhook delivery, or any "is this API well-designed" question. Apply when designing, reviewing, or refactoring a JSON HTTP API — even when the user doesn't mention Stripe by name, since the rules are general API-design principles distilled from the industry's most-copied reference.

Mapping an unfamiliar codebase into feature/business domains — answering "what is this about", "which files implement feature X", "where is the architectural spine", or reviewing a refactor that crosses module boundaries. 47 algorithms across 9 categories — graph construction (omnipresent filter, multilayer, SCC), lexical preprocessing (Samurai, TF-IDF), community detection (Leiden, Infomap, SBM, MCL, Walktrap, spectral, HDBSCAN), architecture recovery (Bunch+MQ, ACDC, Limbo, Reflexion, DSM), topic modelling (LDA, LSI, NMF, HDP), evolutionary coupling (Gall, ROSE), information-theoretic (NCD, MI, MDL, naturalness), centrality (PageRank, HITS, betweenness, TextRank), validation (MoJoFM, ARI/NMI, resolution limit, consensus, co-change prediction, ablation). Trigger without explicit "clustering" mention — codebase grokking, dependency mapping, domain extraction, architecture-recovery validation all apply.

Designs new features, extensions, or modifications to Uncle Bob's Acceptance Pipeline Specification — new mutation strategies, Gherkin syntax support, report formats, pipeline stages, IR fields, or handler patterns. Trigger when someone asks "how would I add X to the acceptance pipeline" or discusses spec-level changes to the parser, generator, runtime, mutator, or reporter components — even if they don't explicitly say "feature design." Works in tandem with the acceptance-pipeline-catalog skill, which provides the baseline spec reference.

Search and recommendation system planning for a two-sided trust marketplace built on OpenSearch — user-intent framing, product-surface architecture, index design, query understanding, retrieval strategy, ranking, search-plus-recs blending, measurement, and a dashboard-and-alerting layer for ongoing decision making. Triggers on tasks involving marketplace search, homefeeds, ranking, relevance tuning, OpenSearch query DSL, analyzers, synonyms, golden sets, NDCG, A/B testing, or diagnosing an existing retrieval system. Use this skill BEFORE marketplace-personalisation when planning new work; hand off when the diagnosed bottleneck is personalisation-specific.

Implementation guide for the 22 Gang of Four design patterns in idiomatic modern Python (3.10+), distilled from refactoring.guru. Use when writing, refactoring, or reviewing Python with a pattern-shaped problem — class-explosion from inheritance, conditionals switching on type, tight coupling to concrete classes, tree-shaped models, runtime algorithm selection, undo/redo, state-dependent behavior, or hiding subsystem complexity. Each entry leads with the Pythonic form (functions, dataclasses, Protocol, singledispatch, match, generators, copy/replace) and falls back to the class-based GoF structure only when identity, state, or dispatch require it. Includes intent, applicability (when to use AND when NOT to), a runnable example, steps, pros/cons, and relations. Trigger even when no pattern is named — cues like "too many constructor params," "giant if/elif," "swap behavior at runtime," "need undo," or "walk a tree" are pattern-shaped. Covers all 5 Creational, 7 Structural, and 10 Behavioral GoF patterns.

Pre-member journey of a two-sided trust marketplace — from anonymous landing through onboarding, registration, and the paid-membership paywall. Covers anonymous signal inference, what pet owners specifically need to validate before paying (safety, availability, competence, effort, local cost comparison), what pet sitters specifically need to validate (opportunity, first-stay path, daily commitment, hidden costs), information-asymmetry closure, progressive profile building, social proof, conversion psychology, onboarding intent capture, identity stitching, and pre-member measurement. Triggers on tasks involving visitor-to-member conversion, anonymous personalisation, onboarding flow design, paywall timing, pre-member ranking, or any question about what a pet owner or pet sitter needs to see before paying. Use this skill BEFORE marketplace-personalisation and marketplace-search-recsys-planning.

Feature engineering for marketplace recommenders — what to extract from raw marketplace assets (listing photos, owner-entered listing metadata, sitter wizard responses) to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders. Covers asset auditing, first-principles feature decomposition, vision-feature extraction (CLIP, room-type, amenities, aesthetics), listing text and metadata encoding, sitter wizard design, derived-composition patterns for i2i / u2i / u2u (ANN shelves, two-tower, mutual-fit), feature quality governance (training-serving parity, drift, PII), and incremental value proof (ablation A/B, kill reviews, feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.

Search relevance and ranking on OpenSearch/Elasticsearch for a two-sided marketplace — candidate retrieval (hybrid BM25 + kNN, RRF, two-tower EBR), base relevance (BM25F, multi_match, LambdaMART), quality signals (Wilson lower bound, Bayesian average, rank_feature saturation/sigmoid), personalization (listing/user/session embeddings), spatial/temporal decay (gauss/exp), marketplace balance (conversion-weighted ranking, supply fairness, Pareto multi-objective), bias correction (IPS, click models, Thompson sampling), empirical evaluation (judgment sets, NDCG, ablation, A/B sizing, CUPED, regression suites), and diversity (MMR, DPP, max-per-host). Triggers on function_score, rank_feature, script_score, kNN, hybrid query, learning-to-rank, two-sided ranking, exposure fairness, NDCG, A/B testing, judgment set construction, ranking ablation, or "why is my OpenSearch ranking bad". Applies to Elasticsearch too — same APIs.

Caching strategies in front of AWS OpenSearch (Elasticsearch) or AWS Personalize — search, recommenders, multi-recommender pages, anon vs logged-in traffic. Covers ROI decision (TPS/minProvisionedTPS, Zipf, amplification), key design (canonicalisation, cohort vs user, solution-version pinning, bucketing), personalisation boundary (anon/logged split, fan-out coalescing), strategies (cache-aside, refresh-ahead, write-through, batch precompute, L1+L2), TTL (volatility, soft/hard, jitter, event-driven invalidation), stampede protection (single-flight, XFetch, stale-while-revalidate, circuit breaker), observability (hit-rate, cost-per-1k, cardinality drift, log-replay), defensive caching (negative, Bloom filter), and tier composition (LRU, ElastiCache Redis, CloudFront, OpenSearch request/filter cache). Triggers on cache hit rate, Personalize throttling, stampede, single-flight, L1/L2, ElastiCache sizing. Complements opensearch-function-scoring-algorithms.

Django backend patterns for recommendation services (AWS Personalize, Databricks Model Serving, internal microservices) and OpenSearch-backed search/feed endpoints. Covers fan-out orchestration (asyncio.gather, deadline propagation, partial results, async client reuse), external service protection (timeouts, circuit breakers, jittered retry, bulkheads, rate limits), OpenSearch query patterns (search_after, _source filtering, function_score, aliases, routing, bool.filter), result blending (score normalization, MMR, dedup, cold-start), Redis caching (stampede protection, model-versioned keys, two-tier, negative), resilience (partial-response envelope, stale-on-error, graceful degradation), async (sync_to_async, async ORM, uvicorn, contextvars, disconnect cancellation), and DRF response shape (cursor pagination, ETag, throttling). Use when building, reviewing, or refactoring such a Django backend. Triggers even without explicit "scale" cues. Includes 5 scaffolding templates.