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Make interactive Blazor components work correctly with prerendering. USE FOR fixing duplicate data loads, UI flicker during prerender-to-interactive handoff, null references during prerender, persisting state across prerender, disabling prerendering, excluding pages from interactive routing, or detecting whether a component is currently prerendering. DO NOT USE for choosing which render mode to use (see create-blazor-project) or general component authoring (see author-component).

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Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.

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Use when you need to review, improve, or write Java unit tests — including migrating from JUnit 4 to JUnit 5, adopting AssertJ for fluent assertions, structuring tests with Given-When-Then, ensuring test independence, applying parameterized tests, mocking dependencies with Mockito, verifying boundary conditions (RIGHT-BICEP, CORRECT, A-TRIP), leveraging JSpecify null-safety annotations, or eliminating testing anti-patterns such as reflection-based tests or shared mutable state. This should trigger for requests such as Review Java code for unit tests; Apply best practices for unit tests in Java code; Write fast JUnit unit tests for Java code; Improve Mockito-based Java unit tests; Refactor Java tests to isolate collaborators. Part of Plinth Toolkit

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Use when you need to refactor Java code to adopt modern Java features (Java 8+) — including migrating anonymous classes to lambdas, replacing Iterator loops with Stream API, adopting Optional for null safety, switching from legacy Date/Calendar to java.time, using collection factory methods, applying text blocks, var inference, or leveraging Java 25 features like flexible constructor bodies and module import declarations. This should trigger for requests such as Review Java code for modern Java development; Apply best practices for modern Java development in Java code; Modernize Java code with records pattern matching or switch expressions; Replace legacy idioms with modern Java features; Adopt Java 8+ language features safely. Part of Plinth Toolkit

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Use when you need to apply functional programming principles in Java — including writing immutable objects and Records, pure functions, functional interfaces, lambda expressions, Stream API pipelines, Optional for null safety, function composition, higher-order functions, pattern matching for instanceof and switch, sealed classes/interfaces for controlled hierarchies, Stream Gatherers for custom operations, currying/partial application, effect boundary separation, and concurrent-safe functional patterns. This should trigger for requests such as Improve the code with Functional Programming; Apply Functional Programming; Refactor the code with Functional Programming; Refactor Java code to use streams or Optionals safely; Improve immutability and pure functions in Java. Part of Plinth Toolkit

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Extract structured data from clinical notes with span-level provenance and null-safety. Use when users say "extract [variables] from this note", "abstract this chart", "pull structured data from these notes", "what does this note say about [field]", or when building a chart-abstraction, registry, or cohort dataset from unstructured clinical text.

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Define database models with clear naming, appropriate data types, constraints, relationships, and validation at multiple layers. Use this skill when creating or modifying database model files, ORM classes, schema definitions, or data model relationships. Apply when working with model files (e.g., models.py, models/, ActiveRecord classes, Prisma schema, Sequelize models), defining table structures, setting up foreign keys and relationships, configuring cascade behaviors, implementing model validations, adding timestamps, or working with database constraints (NOT NULL, UNIQUE, foreign keys). Use for any task involving data integrity enforcement, relationship definitions, or model-level data validation.

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Use when writing position-independent shellcode or a loader — PEB walking, API hashing, null-byte avoidance, encoders, loaders, PE-to-shellcode conversion, cross-platform shellcode

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Remove the discourse-level (structural) signs of AI writing that survive surface editing: stated lessons and moral-of-the-story closers, tidy single-track arcs, embodied-emotion performance ("chest tightened"), vague allusions instead of named references, unbroken linear structure, and shape convergence across pieces. Grounded in the StoryScope study (Russell et al. 2026): narrative structure alone detects AI text at 93.2% F1, and professional stylistic rewriting moved detection only 1.6 points. Use as the SECOND pass after the humanizer skill (which handles words and phrasing) whenever writing or revising LinkedIn posts, course lessons, blog posts, essays, newsletters, or emails that must read as human. Triggers: "humanize", "de-slop", "AI tells", "make this sound human", "structural pass", "deep humanize".

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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, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

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Provides patterns for testing utility classes, static methods, and helper functions. Validates pure functions, null handling, edge cases, and boundary conditions. Generates AssertJ assertions and @ParameterizedTest for string utils, math utils, validators, and collection helpers. Use when testing utils, test helpers, helper functions, static methods, or verifying utility code correctness.

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Provides edge case, corner case, boundary condition, and limit testing patterns for Java unit tests. Validates minimum/maximum values, null cases, empty collections, numeric overflow/underflow, floating-point precision, and off-by-one scenarios using JUnit 5 and AssertJ. Use when writing .java test files to ensure code handles limits, corner cases, and special inputs correctly.

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Provides patterns for unit testing Jakarta Bean Validation (JSR-380), including @Valid, @NotNull, @Min, @Max, @Email constraints with Hibernate Validator. Generates custom validator tests, constraint violation assertions, validation groups, and parameterized validation tests. Validates data integrity logic without Spring context. Use when writing validation tests, bean validation tests, or testing custom constraint validators.

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Add or revise source-level JSDoc for Shift APIs. Use this skill before writing or editing documentation comments for exported classes, methods, constructors, domain data structures, render frames, reactive state, or any API where caller intent, side effects, lifetime, ownership, or nullability are easy to misunderstand.

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Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.

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Decision framework for choosing between consent and legitimate interest as the lawful basis for processing. Covers power imbalance indicators, conditionality prohibition under Article 7(4), granularity requirements, the three-part LIA test (purpose, necessity, balancing), and practical decision trees for common scenarios.

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Technical architecture guide for building a multi-purpose consent preference center. Covers per-purpose granularity, easy withdrawal under Article 7(3), version history, audit trails, and IAB Transparency and Consent Framework v2.2 integration. Includes database schema, API design, and UI component specifications.

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Fix silent data loss when mutating Nostr replaceable events (Kind 0 profile, Kind 3 contact/follow list, Kind 10002 relay list, etc.) in client apps. Use when: (1) Following someone wipes the user's entire follow list, (2) Updating profile metadata loses existing fields, (3) Fresh browser session or mobile login causes data loss on first action, (4) Replaceable event mutation uses stale or null cached state. Root cause: Nostr replaceable events are full-replace (no partial update), so publishing based on stale/unloaded cache overwrites the canonical version. Applies to any Nostr client using React, Flutter, or similar reactive frameworks where query state may not be loaded when a mutation fires.

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Fix features broken by REST API responses that flatten Nostr events, losing tag data. Use when: (1) A feature works for WebSocket-loaded events but not REST API-loaded ones, (2) A model field (like sha256, textTrackRef) is null for REST API data but set for WebSocket data, (3) REST API returns denormalized fields (d_tag, video_url) but omits raw tags array, (4) hasSubtitles/hasFeature returns false for REST-loaded videos. Common in Nostr clients that use both REST APIs (for analytics/bulk queries) and WebSocket (for real-time events).

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Fix incorrect priority ordering when using denormalized aggregate columns. Use when: (1) Records are processed in wrong order despite ORDER BY on count/sum columns, (2) Top items by some metric aren't being selected first, (3) Aggregate columns show 0 or NULL for records that should have high values, (4) Priority queue processes low-value items before high-value ones. The root cause is often that denormalized columns (vine_count, loop_count, total_orders, etc.) weren't backfilled or maintained properly. Solution: JOIN with source tables to compute actual aggregates at query time.

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Fix ClickHouse query errors in Rust when using clickhouse-rs crate. Use when: (1) "string is not valid utf8" errors - typically FixedString columns need CAST to String, (2) "tag for enum is not valid" errors - typically Option<T> fields receiving non-NULL values, (3) Sum/count aggregations returning Float64 but Rust expects u64, (4) LEFT JOIN results with empty strings where Rust expects Option::None, (5) "not enough data, probably a row type mismatches a database schema" - query SELECT returns fewer columns than the Rust Row struct expects (common when multiple query functions share the same struct but one function is missing columns added later), (6) Garbled/corrupted data when INSERTing to FixedString(N) columns using String Rust type - silently corrupts data (no error!) because String adds a length prefix but FixedString expects exactly N raw bytes. Fix: use [u8; N] with #[serde(with = "BigArray")] from serde-big-array, (7) "the trait bound `[u8; 64]: serde::Serialize` is not satisfied" when using [u8; N] arrays in Row structs - need #[serde(with = "BigArray")] annotation from serde-big-array crate.

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Verify code correctness statically against specifications using type checking, contract verification, and formal methods. Use when: (1) Verifying type safety and null safety in Python, Java, or C/C++ code, (2) Checking design-by-contract specifications (preconditions, postconditions, invariants), (3) Validating code against formal specifications, (4) Ensuring code quality and correctness before runtime, (5) Finding potential bugs through static analysis. Supports Python (mypy, contracts), Java (javac, JML), and provides verification scripts and contract specification guidelines.

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Performs symbolic execution to detect potential errors by exploring execution paths, solving path constraints, and generating test inputs. Use when you need to analyze code for bugs like null dereferences, division by zero, buffer overflows, or assertion violations. Also use to generate test inputs that exercise different code paths, find edge cases, or explore all reachable program states. Supports Python, Java, and C/C++ through manual symbolic execution techniques and integration with tools like KLEE, angr, Z3, and Symbolic PathFinder.

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Analyze source code statically to detect potential functional bugs including null dereferences, incorrect condition checks, unreachable code, inconsistent state updates, logic errors, resource leaks, and type mismatches. Report suspicious code locations with detailed explanations, severity levels, and confidence assessments. Use when reviewing code for bugs, performing code audits, or when the user asks to find bugs, detect issues, analyze code for problems, or perform static analysis.

日本語の概要は準備中です。原文の説明を表示しています。