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Found 857 Skills
Create and validate solution design documents (SDD). Use when designing architecture, defining interfaces, documenting technical decisions, analyzing system components, or working on solution-design.md files in docs/specs/. Includes validation checklist, consistency verification, and overlap detection.
Generate structured product and technical documents through guided discovery. 8 document types: PRD, Brief, Issue, Task, User Story, RFC, ADR, TDD. Use when: defining products, reporting bugs, planning sprints, writing stories, proposing changes, recording decisions, designing systems. Triggers on "create PRD", "create issue", "report bug", "feature request", "create task", "create user story", "create RFC", "create ADR", "create TDD", "create document", "write doc".
Generate measurable learning outcomes aligned with Bloom's taxonomy and CEFR proficiency levels for educational content. Use when educators need to define what students will achieve, create learning objectives for curriculum planning, or ensure objectives are specific and testable rather than vague.
Connect the complete AI development workflow through documents. It covers domain modeling and code organization (DDD), behavior verification and automated testing (BDD), as well as AI development specification setting (Agent specifications). Use when (1) the project has .feature files, (2) the user asks to organize code by business features or define naming conventions, (3) creating or updating AGENTS.md / project rule files, (4) writing or implementing Gherkin scenarios, (5) starting a new project from scratch, or (6) the agent needs the full development lifecycle.
ArkType runtime validation with TypeScript-native syntax. Type-safe schemas using string expressions, morphs, scopes, and generics. Use when defining schemas, validating data, transforming input, or building type-safe APIs with ArkType.
Provides tool and function calling patterns with LangChain4j. Handles defining tools, function calls, and LLM agent integration. Use when building agentic applications that interact with tools.
Provides comprehensive Drizzle ORM patterns for schema definition, CRUD operations, relations, queries, transactions, and migrations. Proactively use for any Drizzle ORM development including defining database schemas, writing type-safe queries, implementing relations, managing transactions, and setting up migrations with Drizzle Kit. Supports PostgreSQL, MySQL, SQLite, MSSQL, and CockroachDB.
Configure session keys and policies for Cartridge Controller to enable gasless, pre-approved transactions. Use when defining contract interaction policies, setting spending limits, configuring signed message policies, or implementing error handling for session-based transactions. Covers SessionPolicies type, policy definitions, verified sessions, and error display modes.
Zig C interoperability skill. Use when calling C from Zig, calling Zig from C, using @cImport and @cInclude, running translate-c on C headers, defining extern structs and packed structs, matching C ABI types, or building mixed C/Zig projects. Activates on queries about @cImport, @cInclude, translate-c, extern struct, packed struct, Zig C ABI, calling C from Zig, exporting Zig to C, or bindgen equivalents.
Fuzzing skill for automated input-driven bug finding in C/C++. Use when setting up libFuzzer or AFL++ fuzz targets, defining fuzz entry points around parsing or I/O boundaries, integrating fuzzing into CI, managing corpora, or combining fuzzing with sanitizers. Activates on queries about libFuzzer, AFL, afl-fuzz, fuzz targets, corpus management, coverage-guided fuzzing, or OSS-Fuzz integration.
Zustand store data structure patterns for LobeHub. Covers List vs Detail data structures, Map + Reducer patterns, type definitions, and when to use each pattern. Use when designing store state, choosing data structures, or implementing list/detail pages.
Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).