152 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 152 plugins that match these filters.
universal-design-principlesDeibler/universal-design-principles1
breaking-coding-chaosbo-cao/breaking-coding-chaos1
make-no-mistakesmomomuchu/make-no-mistakes1
agentic-pluginslaithalsaadoon/agentic-plugins1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
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Affordance, feedback, errors, and user control. Covers Fitts's Law, Affordance, Forgiveness, Feedback Loop, Errors, Constraint, Mapping, Expectation Effect, and Control.
designProvider-agnostic patterns for LLM function calling, tool loops, and agentic workflows
Specification frameworks for features that call a model — approach tier, model pinning, output contracts, budgets, failure modes and eval design. Load when a spec touches model calls, prompts, retrieval, tool calling, agent loops or evals.
Breaking Coding Chaos: four dual-loop skills (main + throughline + plan-spar + clean-cut)
productivityZero-unverified-work execution harness: frozen specs, budgeted loops, hard gates, independent verification, honest terminal states.
Flagship skills for AI coding agents. Includes workbench-builder: disposable localhost Bun.serve + bun:sqlite + React 19 + Astryx workbenches with a two-way human↔agent loop over SSE — eval viewers, PR review rooms, document-redline surfaces, grid/spreadsheet editors, multi-surface triage queues, and trace replays, with zero build config and no deploy.
developer-toolsGuide recurring human-relationship practices — success plans, health evidence, renewal and expansion signals, QBRs, handoffs, escalation, and closed-loop Voice of Customer. Do not use this skill for products without accounts, renewals, QBRs, or a customer-success team, including some internal tools, pure transactional products without recurring relationships, and public services without account-based engagement. Load only when the product context includes a recurring human relationship; decline or route away otherwise.
Design, build, evaluate, govern, monitor, evolve, and retire digital twins and federated twin universes for software systems, engineering processes, agentic software factories, infrastructure, and cyber-physical operations. Use when a task involves digital-twin architecture, digital thread, simulation, predictive maintenance, twin health, agent authority, or dark-factory design. Do not use for ordinary observability dashboards, static dependency graphs, generic AI governance, or operating one named infrastructure tool without a twin-specific representation and feedback loop.
Design, build, diagnose, and evolve agent harnesses: instructions, tools, execution environments, durable state, context management, verification, recovery, bounded autonomous loops, and software factories that repeatedly turn incoming work into accepted deliverables. Use for director/worker production systems, factory steering, unreliable coding agents, cross-session drift, premature completion, harness audits, or changes to the runtime around a model, including bounded typed-decision routing. Do not use for prompt rewriting alone, model training, general application architecture, or operating an already evaluated agent in production; route those concerns to their specialist skills.
Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. Covers supervisor, swarm, and hierarchical multi-agent patterns; subgraph composition; state management (checkpointers/stores); persistence; evals; and production debugging. Reach for this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns. Do not use this skill for unrelated requests; route to the nearest named specialist.
Design, diagnose, and evaluate coordination across multiple AI agents and heterogeneous harnesses: decomposition, topology, ownership, communication, distributed context, shared memory, integration, recovery, and collective failure patterns. Use for persistent agent teams, fleets, swarms, agent handoffs, coordination loops, or extracting portable lessons from OpenRig, HERDR and Block/Buzz. Do not use for a single agent's runtime, a named tool's installation or command manual, debate alone, or operating an evaluated production agent; route those to harness-engineering, the tool's documentation, agent-council, or agent-production-operations.
Optimize software performance through user-journey baselines, profiling, representative benchmarks, controlled experiments, and field verification. Use when asked to make an application, service, workflow, or hot path faster or more efficient, or to build the measurement loop first. Do not use for a reliability incident, capacity forecast, generic product A/B test, or operating a named telemetry or load-testing tool without an optimization objective.
Operate Playwright for browser automation end to end: author and debug E2E test suites (robust locators, network interception and mocking, parallel workers, accessibility snapshot checks), wire them into CI, and drive headless browsing and scraping with an extract -> validate -> save loop. Use when writing, running, fixing, or scraping with Playwright, when a Playwright CI failure or JSON report needs triage, or when the bundled pwrun script should analyze a run. Do not use for QA strategy or test framework selection (route to qa-methodology), for frontend component or architecture design (route to frontend-engineering), or for Cloudflare/DDoS-GUARD challenge bypass (use flaresolverr).
Compare intended product outcomes against observed results to close the launch-to-learning loop: collect post-launch evidence, distinguish expected from observed from uncertain from inferred claims, update assumptions, assess feature health, and choose among continue/improve/harvest/pivot/pause/retire — including retirement lifecycles with deprecation, migration, customer treatment, and retained reusable learning. Do not use for incident postmortems or root-cause analysis (routes to incident-learning or site-reliability-engineering); do not use for analytics instrumentation or metric dashboard design (routes to product-analytics-and-measurement); do not use arbitrary thresholds as universal retirement rules — decisions require human judgment and context.
Design, integrate, evaluate, self-host, and troubleshoot typed System One decision models including TypeSafe Jev, Convai Innovations Laya, CLM, and experimental Strands Decider. Use for Choice/Score/Noul judgments and experimental Jev image/Box queries inside deterministic software, app-control loops, routing, ranking, guardrails, calibration, semantic code linting and post-edit feedback, DevOps decision support, confidence-based escalation, or private open-model inference. Do not use for syntactic/style linting, exact policy or authorization, open-ended generation, or generic LLM serving without a bounded decision contract.
Build, develop, deploy, self-host, secure, and troubleshoot durable backend AI agents with Vercel Eve. Use when creating an Eve agent, adding tools, skills, subagents, channels, schedules, sandboxing, durable sessions, observability, or deploying Eve on Vercel or a Node host. Do not use for the separate Vercel AI SDK Agent APIs such as ToolLoopAgent or WorkflowAgent; use an AI SDK-specific skill for those.
10 Claude-specific skills: prompt architecture, context engineering, tool mastery, self-healing agents, verification loops, cognitive scaffolding
Factory for building production-grade agent skills: interviews you for purpose and gold standards, researches the domain, drafts the skill, autonomously improves it against an LLM-as-judge (or real-world) metric, and verifies it with an independent multi-agent panel — delegating to tribunal when installed. Ships a same-model-uplift benchmark standard (bare vs skill vs skill+panel, scored by execution, honest negatives required) and a publish checklist, and stops the improvement loop on a plateau inside judge variance instead of iterating toward an unreachable target. Extends the official single-pass skill creators. Orchestrates companion skills (autoresearch, premortem, handoff) — install those alongside it; the full batteries-included environment is the agent-skills-harness.
The full stack in one install: shaping, coding, dig and snapshot, a four-skill working loop
Media conversion, production, and download — file-to-Markdown, video/audio loops, Suno prompts, yt-dlp downloads
