11 Plugins aus öffentlichen Claude Code marketplaces. Jedes bündelt Skills, Slash-Commands, Agents, Hooks oder MCP servers, die zusammen installiert werden.
Zeige 11 Plugins, die diesen Filtern entsprechen.
ai-native-toolkitbjcoombs/ai-native-toolkit1
universal-design-principlesDeibler/universal-design-principles1Reiche das repo mit .claude-plugin/marketplace.json ein und wir listen jedes plugin auf.
Marketplace einreichenActive coding discipline enforcer based on Karpathy's 4 principles: surface assumptions, simplify, make surgical changes, define verifiable goals. Ships 4 Python tools (complexity_checker, diff_surgeon, assumption_linter, goal_verifier), a review agent, /karpathy-check command, and pre-commit hook. All stdlib-only.
An umbrella collection of 23 skills + 1 hook for AI-driven development: agent self-configuration (MCP / hooks / settings / skills / plugins / cross-agent repository readiness / safe CLI installation), engineering practices (UX review and accessibility, maintainable complexity, prompt engineering, FPF problem solving, bug-fix protocol, planning gate, repository-history investigation, repository activity summary, C# refactoring), AI infrastructure (OpenRouter endpoint ranking and routing), research & docs (Semantic Scholar deep research, URL-to-Markdown, ubiquitous-language thesaurus), macOS & Windows health & cleanup, and niche utilities (clipboard, Windows QA). Plus a Claude Code Bash safety hook.
Multi-dimensional code health analysis with complexity, churn, and test coverage - identifies technical debt hot spots
Skills for AI-native development: /assess (codebase readiness scoring + complexity hotspot SVG) and /huddle (Six Thinking Hats deliberation with Fibonacci team sizing). Also bundles user-invoked personal workflow skills (/tm, /issues, /fix-pr, /fix-develop).
Audits legal AI outputs and workflows for honest compression: unnecessary complexity, false simplicity, excessive caveats, hidden uncertainty and poor cost per legally acceptable output.
Framework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.
Mental models, complexity reduction, memory, and learnability. Covers Hick's Law, Wayfinding, Progressive Disclosure, 80/20 Rule, Chunking, Mental Model, Recognition Over Recall, Consistency, Mimicry, and Iconic Representation.
designProduction code quality checks: SonarQube maintainability gates, Lizard complexity limits, Madge circular dependencies, and Knip dead-code detection. A critic agent (code-quality-critic) merges the tool reports, dedupes overlapping findings, and separates fix-now items from the backlog. Production code only; test-code hygiene lives in qa-test-review.
toolingClaude-Code-native orchestration skills: subagent fan-out, custom-agent authoring, PR review-thread resolution, and complexity/perf hotspot optimization.
Author custom Model Advisor checks, run compliance reviews against industry standards (MISRA, MAB, ISO 26262, DO-178C, AUTOSAR, SLCI), author persistent, requirement-traceable Simulink Test cases, inject faults for robustness testing, perform safety analysis (FMEA, FHA, FTA), detect design errors with root cause analysis using Simulink Design Verifier, fix SLDV incompatibilities, explain missing coverage objectives, and collect quality metrics (maintainability, complexity, testing, coverage) using metric.Engine
developmentAgent pipeline with deterministic quality gates: specifier, coder, cleaner, and hardener stages looped against complexity, coverage, and mutation-testing gates. Ships the /gauntlet command and five stage subagents
