12 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 12 plugins that match these filters.
claude-code-plugins-plusjeremylongshore/tons-of-skills-marketplace1
evolution-skillscoolTheWorld/evolution-skills1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceTwelve ICSME-specific skills for the IEEE International Conference on Software Maintenance and Evolution, its single-round double-anonymous research track, and its Journal-First, Registered Reports, RENE, and ROSE-Festival artifact culture, grounded in the ICSME 2026 researchr call, IEEE Xplore, and dblp.
academicNightly offline self-evolution for this repo's Claude agent: harvests past Claude Code sessions (read-only), mines recurring tasks, replays them offline on your own API budget, and consolidates learnings into validated CLAUDE.md memory and SKILL.md skills behind a held-out gate, staged for review (never auto-applied). Verbatim vendor of microsoft/SkillOpt's stdlib-only skillopt_sleep engine + Claude Code plugin surface (MIT).
Chief Data Officer advisory for startups: AI training data audit (origin × class × use-case matrix with GDPR Art. 6 + EU AI Act citations), data product strategy picker (warehouse vs lakehouse vs mesh + 6-layer build-vs-buy + 12-month sequencing), data asset valuator (strategic value 0-10 + M&A multiplier with carve-out penalties + 3 ranked productization paths). 4 references answering one decision each: training rights, data product strategy, customer-data-as-asset, data team org evolution. Standalone-installable; also bundled in c-level-skills. Strategic only — does not duplicate engineering data skills.
Manage database migrations with version control, rollback capabilities, and automated schema evolution tracking
Modernize COBOL, DB2, VSAM, and JCL systems with the four-stage classic workshop: evidence-first archaeology, REQ-NNN lineage, bounded implementation, and hardened evolution, without the correction-loop and engineering-graph layer.
Modernize Software AG Natural and Adabas systems with the four-stage SIFAP workshop: evidence-first archaeology, REQ-NNN lineage, bounded implementation, and hardened evolution, without the correction-loop and engineering-graph layer.
Eleven agent skills for thinking better: ask, learn, solve, decide, know yourself. Every skill is user-invoked as /evolution:<name>.
Write, review, and maintain architecture decision records with clear context, alternatives, consequences, confirmation links, and lifecycle governance. Use when a consequential technical decision or its enforceable architectural constraint must remain understandable. Do not use for system-wide evolutionary architecture design, general observability, or operating a named test or CI tool.
Design, govern, document, review, and evolve consumer-facing APIs and event interfaces. Use when choosing REST/HTTP, GraphQL, RPC, events, webhooks, or streaming; writing OpenAPI or AsyncAPI contracts; assessing an API landscape, ownership, duplication, lifecycle, discoverability, retirement, gateways, service meshes, north-south or east-west traffic, routing, policy, observability, or failure boundaries; defining schemas, pagination, mutations, errors, idempotency, or compatibility; or planning versioning, deprecation, and migration. Do not use for product discovery, platform operations, full security assessment, ADR authoring, or delivery gates; route those to the neighboring specialist skills.
Build or refactor agent-facing CLI tools with non-interactive commands, stable --help and --json contracts, idempotent operations, and --dry-run previews. Use for CLI design, agent-friction refactors, output/exit-code debugging, or automation safety. Do not use for GUI/TUI design, conversational tools, MCP server design, or general API architecture; use api-design-and-evolution for the latter.
Use this skill to assess, design, and evolve data architectures, including data platforms, data products, data mesh adoption, event-driven data flows, governance, modeling, and migration decisions. Load it when teams need workload-grounded tradeoffs, ownership and quality agreements, or a current-to-target data architecture. Do not use it for pipeline or platform operations, implementation details, interface contract semantics, SQL tuning, or statistical modeling; route those to data-engineering, platform-engineering, api-design-and-evolution, postgres, or data-scientist.
Design and review software architectures from business drivers through system boundaries, tradeoffs, runtime behavior, evolution, and architecture practice. Use when choosing a greenfield or target architecture, comparing modular-monolith and service shapes, designing distributed consistency, replication, partitioning, coordination, ordering, transaction isolation, or failure behavior, defining architecture fitness evidence, or facilitating a consequential architecture review. Do not use for reverse engineering, API contract semantics, data-platform design, implementation, infrastructure operations, security lifecycle, or migration execution; route those to the named specialist skills.
