12 Plugins aus öffentlichen Claude Code marketplaces. Jedes bündelt Skills, Slash-Commands, Agents, Hooks oder MCP servers, die zusammen installiert werden.
Zeige 12 Plugins, die diesen Filtern entsprechen.
copilot-primitives-claudegbb-acelerators/awesome-harness-primitives1
ai-skillsAmey-Thakur/AI-SKILLS1Reiche 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.
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).
Improve existing code without changing behavior: surgical refactoring, complexity reduction, repository-wide review, design pattern and SOLID checks, formatting conventions, linter autofix loops, tech-debt planning, and incremental delivery discipline.
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.
12 working methods for computer engineering: algorithmic-complexity, binary-data-representation, compilers-and-toolchains, concurrency-primitives, and more
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.
toolingReview and improve AI-generated software for architecture, security, APIs, databases, testing, complexity, UI quality, and AI slop.
developmentClaude-Code-native orchestration skills: subagent fan-out, custom-agent authoring, PR review-thread resolution, and complexity/perf hotspot optimization.
Refactor code to reduce cyclomatic complexity. Measures per-function complexity, refactors hotspots worst-first, reports before/after numbers. Keeps AI-generated code maintainable for humans.
