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Creates and configures NestJS modules, controllers, services, DTOs, guards, and interceptors for enterprise-grade TypeScript backend applications. Use when building NestJS REST APIs or GraphQL services, implementing dependency injection, scaffolding modular architecture, adding JWT/Passport authentication, integrating TypeORM or Prisma, or working with .module.ts, .controller.ts, and .service.ts files. Invoke for guards, interceptors, pipes, validation, Swagger documentation, and unit/E2E testing in NestJS projects.

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。

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.

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。

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.

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。

Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for parsing flags and subcommands, displaying progress bars and spinners, generating bash/zsh/fish completion scripts, CLI design, shell completions, and cross-platform terminal applications using commander, click, typer, or cobra.

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。

Builds, optimizes, and debugs cross-platform mobile applications with React Native and Expo. Implements navigation hierarchies (tabs, stacks, drawers), configures native modules, optimizes FlatList rendering with memo and useCallback, and handles platform-specific code for iOS and Android. Use when building a React Native or Expo mobile app, setting up navigation, integrating native modules, improving scroll performance, handling SafeArea or keyboard input, or configuring Expo SDK projects.

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。

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.

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。

Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.

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。

Patterns and templates for generating valid Omnigent agent directories. Load when ready to create files.

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。

Example skill demonstrating Anthropic SKILL.md format. Load when learning to create skills or testing the OpenSkills loader.

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。

Translates Splunk SPL queries to Axiom APL. Provides command mappings, function equivalents, and syntax transformations. Use when migrating from Splunk, converting SPL queries, or learning APL equivalents of SPL patterns.

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。

Designs and builds Axiom dashboards via API. Covers chart types, APL and metrics/MPL query patterns, SmartFilters, layout, and configuration options. Use when creating dashboards, migrating from Splunk, or configuring chart options.

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。

Worktrunk-specific guidance for tend CI workflows. Adds codecov polling, Rust test commands, labels, and review criteria on top of the bundled tend-ci-runner skills. Use when operating in CI.

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。

Expert guidance for integrating and building applications with shadcn/ui components, including component discovery, installation, customization, and best practices.

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。

Multi-layer workflow for creating complete panel features by analyzing existing patterns and generating coordinated server routes, services, UI components, registration, and styling files.

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。

Multi-layer workflow for creating complete panel features by analyzing existing patterns and generating coordinated server routes, services, UI components, registration, styling files, AND explicitly wiring new handlers into the server's dispatch table (supports both Express middleware and manual http.createServer switch/case patterns).

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。

Two-pass video concatenation workflow that avoids encoder compatibility issues by separating concat and audio mixing

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。

Audit generated Excel reports by enumerating populated output rows, exposing formulas, and checking that each subtotal range includes all visible detail rows.

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。

Generate game assets with fal (fal.ai) through the fal MCP server, the `um fal` CLI (REST) or `fal api`. Covers sprites and icons with transparent backgrounds, consistent variants and animation frames, pixel art, seamless textures, PBR material maps, image-to-3D models, remeshing, auto-rigging, sound effects, music, voice lines, and trailer or cutscene video. Use whenever a mod needs new art, audio or 3D models, or the user mentions fal, generating sprites, textures, models, SFX or music for a game.

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。

Turn generated or hand-made art into exactly what a game engine loads. Covers sprite cutout, trim and nearest-neighbour fitting to frame sizes, pixelation to a palette, sprite sheets and strips, player/team-colour masks, seamless textures, and rendering a 3D model into sprite frames from the game's own camera (isometric/RTS 8 or 16 headings, side view, top-down) with Blender. Use after generating assets, or when the user asks to make sprites, sprite sheets, animation frames, isometric unit sprites or icons for a game.

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。

Generates Mermaid diagrams from Trailmark code graphs. Produces call graphs, class hierarchies, module dependency maps, containment diagrams, complexity heatmaps, and attack surface data flow visualizations. Use when visualizing code architecture, drawing call graphs, generating class diagrams, creating dependency maps, producing complexity heatmaps, or visualizing data flow and attack surface paths as Mermaid diagrams.

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。

Builds and queries multi-language source and binary code graphs for security analysis. Includes pre-analysis passes for blast radius, taint propagation, privilege boundaries, entry point enumeration, proxy/unresolved-call tracking, type/reference queries, structural traversal, graph diffs, audit augmentation, declared cross-language/FFI/external links via `.trailmark/links.toml`, and SQL schema graphs. Use when analyzing call paths, mapping attack surface, finding complexity hotspots, enumerating entry points, tracing taint propagation, measuring blast radius, importing SARIF/weAudit/binary findings, linking source graphs across language or RPC boundaries, or building a code graph for audit prioritization. Feature-gate version-specific Trailmark APIs before using them; prefer `trailmark.parse.detect_languages()` or `--language auto` when the target language is unknown or polyglot.

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。

Translates Mermaid sequenceDiagrams describing cryptographic protocols into ProVerif formal verification models (.pv files). Use when generating a ProVerif model, formally verifying a protocol, converting a Mermaid diagram to ProVerif, verifying protocol security properties (secrecy, authentication, forward secrecy), checking for replay attacks, or producing a .pv file from a sequence diagram.

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。

Designs and improves fuzzing harnesses for C/C++ and Rust. Covers mapping raw bytes onto a target API, generating structured inputs, avoiding non-determinism and false crashes, and deciding what to fuzz together. Use when writing a first LLVMFuzzerTestOneInput or fuzz_target! harness, when a campaign finds nothing or reports crashes that will not reproduce, or when the target API needs structured rather than raw input.

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。

Mutation-driven test vector generation. Finds implementations of a cryptographic algorithm or protocol, runs mutation testing to identify escaped mutants, then generates new test vectors that deliberately exercise the uncovered code paths. Compares before/after mutation kill rates to prove vector effectiveness. Use when generating cryptographic test vectors, measuring Wycheproof coverage gaps, finding escaped mutants via mutation testing, creating cross-implementation test suites, or improving test vector coverage for crypto primitives.

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。