197 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 197 plugins that match these filters.
shadowclonetheonly1me/shadowclone1
tizen-platformSamsung/tizen-agent-skills1
vibemasterliustack/vibemaster1
open-agent-stackOneWave-AI/open-agent-stack1Submit the repo with .claude-plugin/marketplace.json and we list every plugin.
Submit marketplaceUse this skill for local FFmpeg/FFprobe media inspection, remuxing, transcoding, filtering, evidence-bounded video review, transcript-assisted editorial plans, edit decision lists, podcast/audio cleanup, rendering, and output acceptance. It emphasizes explicit stream selection, source preservation, build-aware commands, bounded evidence, and verified new outputs. Do not use it for libav API programming, opaque whole-video understanding, automatic publishing, rights clearance, DRM circumvention, professional broadcast/color certification, or HyperFrames-authored compositions; route those tasks to their owning capabilities.
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.
Plan the implementation of an approved requirement or specification: produce an executable, dependency-aware delivery plan covering work breakdown, dependency mapping, critical path, ownership, parallelism and sequencing, rollout strategy, rollback and recovery paths, and verification against the original requirement. Supports cross-team, cross-repository, migration, and staged-rollout scenarios. Do not use for pre-approval discovery or needs-finding, authoring a specification from scratch, coding or implementation, the neckbeard issue-to-PR delivery flow itself, or any work whose prerequisite decision has not been approved — planning unapproved work is an explicit stop condition.
Design, create, or improve an infographic, visual explainer, illustrated guide, comparison poster, process graphic, or data story for any audience. Use for turning evidence and ideas into a clear, polished visual explanation, including choosing information structure, visual encoding, and a distinct aesthetic. Do not use for standalone decorative artwork, complete brand identity systems, statistical analysis alone, or ordinary document formatting.
Plan and execute production ML engineering work — classifier versus generative model selection, zero-shot baselines, coding specialization, model training and fine-tuning (LoRA/QLoRA), evaluation and eval-set design, quantization decisions, inference deployment, lineage, feature parity, drift response, and regression triage, experimental weight inspection and targeted editing, grounded in practical engineering patterns for production ML systems. Do not use for statistical modeling and experimental design (that's the data scientist) or for operating a specific inference engine (that's a tool skill such as llama-cpp or vllm).
Apply distilled coding principles from 14 classic software books to code review, refactoring, design, and implementation decisions. Do not use for language- or framework-specific tutorials, tool manuals, or tasks already governed by a project's established conventions.
Query, search, and download public datasets and civic information for the City of Raleigh. Use for live ArcGIS Hub catalog discovery, ArcGIS FeatureServer and MapServer queries, ImageServer imagery exports, official Raleigh geocoding, GoRaleigh transit feeds, guest-public development records, public RaleighNC.gov content, eSCRIBE public meetings, Raleigh fire reports and inspections, and the Raleigh-Wake ECC active incident feed. Do not use for private data, authenticated operations, payments, submissions, bulk crawling, or non-public portals.
Design and run a Spec-Driven Development (SDD) pipeline for AI software factories — where structured specifications are the input, AI agents generate the code, and quality gates enforce correctness at each phase: SPECIFY → DECOMPOSE → IMPLEMENT → VERIFY → DELIVER. Use when building or refining a spec-driven pipeline any AI coding tool (Claude Code, Cursor, Hermes Agent, Devin, OpenHands, droid) can follow, or when you need spec quality gates, phase-gate verdicts, NFR encoding, or format translation. Do not use for a single small change with a clear goal (classify first via bmad), for the control-plane protocol of intent contracts, autonomy gating, and failure routing around a pipeline (bmad), or for unvalidated problems (product-discovery).
Manage technical projects from initiation through delivery and closure: choose and tailor predictive, iterative, Scrum, flow-based, or hybrid approaches; coordinate teams and vendors; track milestones, risks, issues, dependencies, forecasts, scope changes, and recovery. Use when a team has no project manager, a TPM needs decision-ready analysis, a project is slipping, or commitments and handoffs need coordination. Use `technical-program-management` instead when multiple related projects, teams, vendors, shared capacity, or benefits must be coordinated toward one outcome. Adapt depth to the user's expertise. Do not use for a standalone coding task, product discovery or strategy, only writing an implementation plan, only diagnosing Kanban flow, operating a ticket tool, incident command, or a release go/no-go decision; route those to specialists.
Install, operate, and troubleshoot Unsloth Core, Studio, and Desktop for local inference and model training. Use for Unsloth environment setup, LoRA/QLoRA supervised fine-tuning, coding specialization, verifier audits, chat templates, GRPO and preference training, vision and audio workflows, decision models, local coding agents and tools, dynamic quantization, checkpoint recovery, multi-GPU training, and model export to GGUF or serving engines. Do not use for framework-neutral ML strategy or operating a standalone inference server unrelated to Unsloth.
Official Gradle skills for AI coding agents: audit a build against Gradle best practices, and upgrade the Gradle wrapper.
developmentSet up and maintain portable coding-agent skills with explicit local consent
Build, register and troubleshoot Tizen Action Framework providers for AI coding agents: pick a default Action category or author custom .action/.entity schemas, generate actionc/TIDL stubs for C#, C++, JavaScript or Flutter-Tizen/Dart, implement the generated ServiceBase, register provider metadata and verify with action-tool.
productivityThe full stack in one install: shaping, coding, dig and snapshot, a four-skill working loop
The full vibe-coding loop: scaffold an app or marketing site, animate it (GSAP, scroll, micro-interactions), make the fake data real, unstick it, de-AI it, ship it, demo it.
Goal-based planning and proof gate for production-grade AI coding agent work.
Offline cyber security agent for your apps and AI coding tools. Open R&D: measured in public, improved every release.
Coding agents hallucinate APIs. ContextMatic gives them curated, versioned API and SDK docs. Ask your agent to "integrate the payments API" and it guesses — falling back on outdated training data and generic patterns that don't match your actual SDK. ContextMatic solves this by giving the agent deterministic, version-aware, SDK-native context at the exact moment it's needed.
Task-aware working modes for coding agents — debug, fix, review, migrate, and more
Expert FinOps guidance for cloud, AI, SaaS and data-platform spend. Ask Claude how to reduce a bill, size a commitment, allocate shared costs or justify AI spend, and it answers with billing mechanics, decision rules and worked examples, not generic advice. Cloud: AWS, Azure, GCP, OCI, Kubernetes and on-premises or hybrid estates, including Savings Plans, Reserved Instances, Azure Hybrid Benefit and contract negotiation. AI: Anthropic, Bedrock, Azure OpenAI, Vertex AI, open-weight model APIs, AI coding tools, agent costs, self-hosted vs managed inference. Data platforms: Databricks, Snowflake, Microsoft Fabric. FinOps practice: allocation, showback and chargeback, forecasting, operating model, FOCUS multi-cloud cost data, tagging, anomalies, KPIs, workload onboarding, SaaS and IT asset management, GreenOps. Waste playbooks: 44 runbooks for waste patterns such as idle NAT gateways, orphaned disks, snapshot sprawl, unused reservations, oversized GPUs and coding-agent token waste. Each gi
