38 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 38 plugins that match these filters.
agent-startersneg55/agent-starter1
ai-agentsrjmurillo/ai-agents1
auto-taskpatforna/auto-task1
alexgreensh-repo-forensicsalexgreensh/repo-forensics1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceAll structured thinking skills in one install: root-cause analysis, trade-offs, pre-mortems, prioritization, estimation, creative and critical thinking, plus a selector that picks the right method.
12 first-party AI-agent collaboration & process skills. Invoke explicitly with /collaboration-skills:<skill>.
workflowSkills, hooks, templates, and engineering guides for bootstrapping AI-agent-friendly projects, with a per-project self-improvement loop. Bundles the /new-project and /adopt-project scaffolding skills plus commit, simplify, reflect, remember, and dream.
scaffoldingComplete project development toolkit: agents, reusable skills, and lifecycle skills (/spec to /ship) for Claude Code workflows
An opinionated set of AI-agent skills to automate a large slice of the software development lifecycle including planning, implementation, review, and shipping.
Offline security scanner for AI-agent repos, skills, plugins, and MCP servers. 29 scanners detect prompt injection, MCP tool poisoning, credential exposure, malicious lifecycle hooks, package IOCs, CVEs, CISA KEV exposure, and post-incident traces. Zero dependencies. Zero telemetry.
White-box bug hunting, AI-agent and LLM red-teaming, cloud identity and CI/CD trust, client-app trust surfaces across browser and editor extensions and Electron, wire-protocol and token trust across gRPC, WebSocket, and JWT, enterprise identity lifecycle across SCIM and JIT provisioning, directory sync, MFA and key lifecycle, and Kerberos delegation, infrastructure-as-code and container hardening, mobile app security, smart-contract and DeFi review across EVM and Move, skill and MCP supply-chain vetting, API and object-authorization depth, host privilege escalation, network-service and transport trust, firmware and embedded-software trust, web trust boundaries, authentication and session depth, rate-limiting and multi-tenant isolation, injection and resource-exhaustion depth, file-upload and content handling, defensive detection and logging, supply-chain and dependency risk, business-logic and access-control depth, the classic web-attack classes across XSS, template and command injecti
securityDetermines whether a specific AI-agent action, output, recommendation, or proposed commitment remains admissible for execution or institutional reliance under current authority, delegated scope, evidence, facts, policy, risk, escalation, and revocation conditions. Use this Skill before an enterprise or regulated AI agent executes, updates records, triggers workflows, communicates externally, or before an institution relies on an agentic output in a way that creates legal, financial, operational, regulatory, customer, employee, patient, citizen, market, contractual, or reputational consequence.
Инженерный skill для проектирования, реализации, отладки и оценки AI-агентов: архитектура, Harness, контекст, инструменты, память и evals.
productivityUse this skill to shape product or engineering work before committing time to it: set appetites instead of estimates, narrow raw ideas into bounded problems, sketch solutions at the right level of abstraction, de-risk rabbit holes, write pitches, bet with capped downside (circuit breaker), and govern builds with discovered scopes and scope hammering. Adapted from Basecamp's Shape Up and extended for human+AI-agent teams. Use when a raw idea, feature request, or "redesign X" grab-bag needs to become a bounded project before anyone builds; when planning how much work an idea is worth; or when delegated agent builds need budgets, kill criteria, and non-convergence rules. Do not use for discovering whether a problem is real (use product-discovery), for portfolio-level sequencing across quarters (product-roadmapping-and-portfolio), for formal specification after the bet is placed (spec-driven-development), or for task-level prioritization frameworks like RICE (product-methodology).
10 AI agent engineering skills: knowledge base injection, entity memory, multi-model routing, proactive intelligence, MCP servers, parallel orchestration
Create new agent skills from scratch, modify and improve existing skills, and measure skill performance through evaluation and benchmarking. Use when users want to create a skill, write a skill, build a new skill, edit or optimize an existing skill, run evals to test a skill, benchmark skill performance, or optimize a skill's description for better triggering accuracy. Also triggers when users say "turn this into a skill", "make a skill for X", "skill for doing Y", or ask about skill structure, skill format, or SKILL.md files.
Build, train, deploy, monitor, and operate Amazon SageMaker AI workloads.
Discover, compare, audit, repair, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs. Vendored verbatim from the Forward Future Loop Library (MIT).
Use for Six Thinking Hats or de Bono's parallel thinking, or when a decision or idea needs facts, gut feel, risks, benefits and new options kept apart: "look at this from all sides", "full perspective check". Skip for plain factual questions or single-angle hot takes.
Use for critical thinking, or when the user wants a claim, plan or decision checked for weak spots: "is this right?", "what am I missing?", "poke holes", "sanity check", "devil's advocate", "red team this". Skip for execution-only tasks and wording edits.
Use for systems thinking, or when a change may ripple beyond its target: "what are the side effects?", "why does this keep coming back?", "how do these teams or services affect each other?". Maps loops, delays and leverage points. Skip for single-step fixes.
Use for creative thinking, or when the user wants many fresh options before choosing: "give me ideas", "brainstorm names", "other ways to do this", "think outside the box". Diverges first, then picks. Skip when one answer is wanted or the task is pure execution.
Use for strategic thinking, or when the user must choose a direction under constraints: "where should we focus?", "how do we win?", "roadmap for next year", "what should we not do?". Ends in explicit bets and risks. Skip for step-by-step implementation.
Use for analytical thinking, or when a question needs breaking down and evidence: "why did metric X drop?", "what drives this?", "break this down", "what data do we need?". Builds a tree, hypotheses and evidence, ends with an answer and key uncertainty. Skip for quick verdicts.
