Loading...
Loading...
Found 706 Skills
Auto-generate comprehensive API documentation with examples, schemas, and interactive tools.
Execute an approved engineering plan exactly as specified. Implement an MVP and tests without expanding scope or changing constraints.
Comprehensive documentation specialist covering API documentation, technical writing, design documentation, migration guides, and changelog generation. Use when creating OpenAPI/Swagger specs, generating SDKs, writing user guides, creating README files, documenting architecture, writing design specs, creating ADRs, writing migration guides, or generating changelogs from git commits. Handles versioning, examples, developer experience, and user-facing documentation.
Provides comprehensive uni-app-x component and API integration guidance. Use when the user needs official uni-app-x components or APIs, wants per-component or per-API examples, or needs cross-platform compatibility details for uni-app-x.
Daily triage of Wilma school notifications for Finnish parents. Fetches exams, messages, news, schedules, and homework — filters for actionable items, syncs exams to Google Calendar, and reports via chat. Requires the `wilma` skill and `gog` CLI (or `gog` skill from ClawHub) for calendar access.
Uncertainty-aware non-linear reasoning system with recursive subagent orchestration. Triggers for complex reasoning, research, multi-domain synthesis, or when explicit commands `/nlr`, `/reason`, `/think-deep` are used. Integrates think skill (reasoning), agent-core skill (acting), and MCP tools (infranodus, exa, scholar-gateway) in recursive think→act→observe loops. Uses coding sandbox for execution validation and maintains deliberate noisiness via NoisyGraph scaffold. Supports `/compact` mode for abbreviated outputs and `/semantic` mode for rich exploration.
Domain-Driven Design system for software development. Use when designing new systems with DDD principles, refactoring existing codebases toward DDD, generating code scaffolding (entities, aggregates, repositories, domain events), facilitating Event Storming sessions, creating bounded context maps, or performing code reviews with a DDD lens. Covers both strategic design (bounded contexts, subdomains, context maps, ubiquitous language) and tactical design (entities, value objects, aggregates, domain services, repositories). Supports all major architecture patterns (Hexagonal/Ports & Adapters, CQRS, Event Sourcing, Clean Architecture) with language-agnostic guidance and concrete examples in Python and TypeScript.
Use when clarifying fuzzy boundaries, defining quality criteria, teaching by counterexample, preventing common mistakes, setting design guardrails, disambiguating similar concepts, refining requirements through anti-patterns, creating clear decision criteria, or when user mentions near-miss examples, anti-goals, what not to do, negative examples, counterexamples, or boundary clarification.
Automatically detect and suggest appropriate MCP tools (context7, grep_app, web_search) based on user queries. This applies when queries contain documentation keywords (including English terms like how to use, docs, API, guide, tutorial and Chinese terms like 如何使用, 文档, 教程); code search keywords (including English terms like example, implementation, source code, github and Chinese terms like 例子, 示例, 实现, 源码); or latest information/bug fixing keywords (including English terms like latest, 2025, 2026, new, update, fix bug, error and Chinese terms like 最新, 更新, 修复 bug, 报错).
Professional Pydantic v2.12 development for data validation, serialization, and type-safe models. Use when working with Pydantic for (1) creating or modifying BaseModel classes, (2) implementing validators and serializers, (3) configuring model behavior, (4) handling JSON schema generation, (5) working with settings management, (6) debugging validation errors, (7) integrating with ORMs or APIs, or (8) any production-grade Python data validation tasks. Includes complete API reference, concept guides, examples, and migration patterns.
This skill should be used when the user asks to "bootstrap few-shot examples", "generate demonstrations", "use BootstrapFewShot", "optimize with limited data", "create training demos automatically", mentions "teacher model for few-shot", "10-50 training examples", or wants automatic demonstration generation for a DSPy program without extensive compute.
Writes an implementation plan with small steps, exact files to touch, and verification commands. Use before making non-trivial changes.