Agent skills you can check before you install
Public SKILL.md skills from GitHub repositories with 50+ stars and from the vendors themselves, scanned and scored. Install any of them with one command in Claude Code, Cursor, Codex, GitHub Copilot or Gemini CLI.
8,290 skills from 237 repositories · updated 54 seconds ago
578 skills
- modal-serverless-gpuOrchestra-Research/AI-Research-SKILLsServerless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.DevOpsNo warnings★ 13.8K↓ —
- phoenix-observabilityOrchestra-Research/AI-Research-SKILLsOpen-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.DevOpsNo warnings★ 13.8K↓ —
- serving-llms-vllmOrchestra-Research/AI-Research-SKILLsServes LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.DevOpsNo warnings★ 13.8K↓ —
- fine-tuning-openvla-oftOrchestra-Research/AI-Research-SKILLsFine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups. Use when reproducing OpenVLA-OFT paper results, training custom VLA action heads (L1 or diffusion), deploying server-client inference for ALOHA, or debugging normalization, LoRA merge, and cross-GPU issues.DevOpsNo warnings★ 13.8K↓ —
- langsmith-observabilityOrchestra-Research/AI-Research-SKILLsLLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.DevOpsNo warnings★ 13.8K↓ —
- nemo-evaluator-sdkOrchestra-Research/AI-Research-SKILLsEvaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.DevOpsNo warnings★ 13.8K↓ —
- llamaguardOrchestra-Research/AI-Research-SKILLsMeta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.DevOpsNo warnings★ 13.8K↓ —
- llama-cppOrchestra-Research/AI-Research-SKILLsRuns LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.DevOpsNo warnings★ 13.8K↓ —
- tensorrt-llmOrchestra-Research/AI-Research-SKILLsOptimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.DevOpsNo warnings★ 13.8K↓ —
- model-mergingOrchestra-Research/AI-Research-SKILLsMerge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.DevOpsNo warnings★ 13.8K↓ —
- knowledge-distillationOrchestra-Research/AI-Research-SKILLsCompress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.DevOpsNo warnings★ 13.8K↓ —
- cloudflare-one-migrationscloudflare/skillsAssess and plan migrations from existing VPN, SWG, or SASE platforms to Cloudflare One, including policy mapping, parity gaps, and rollout.OfficialDevOpsNo warnings★ 3.1K↓ —
- cloudflare-email-servicecloudflare/skillsImplement or troubleshoot Cloudflare Email Sending and Email Routing integrations and their delivery configuration.OfficialDevOpsNo warnings★ 3.1K↓ —
- sandbox-migrate-to-nextcloudflare/skillsMigrate Cloudflare Sandbox apps from stable @cloudflare/sandbox to @cloudflare/sandbox@next (SDK 1.0 preview). Use sandbox-next for apps already on the preview.OfficialDevOpsNo warnings★ 3.1K↓ —
- workers-best-practicescloudflare/skillsCloudflare Workers best practices for production applications. Use when writing, reviewing, or configuring Workers.OfficialDevOpsNo warnings★ 3.1K↓ —
- ml-pipelineJeffallan/claude-skillsDesigns and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.DevOpsNo warnings★ 12K↓ —
- devops-engineerJeffallan/claude-skillsCreates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.DevOpsNo warnings★ 12K↓ —
- offensive-active-directorySnailSploit/Claude-RedActive Directory attack methodology for internal network red team engagements. Covers reconnaissance (BloodHound, PowerView, ADExplorer), credential abuse (Kerberoasting, ASREProasting, NTLM relay, LLMNR/NBT-NS poisoning), privilege escalation (ACL abuse, GPO abuse, unconstrained/constrained delegation), lateral movement (Pass-the-Hash, Pass-the-Ticket, Overpass-the-Hash, WMI/WinRM/PsExec), persistence (Golden/Silver/Diamond Tickets, DCSync, DCShadow, AdminSDHolder, Skeleton Key), forest trust attacks, ADCS abuse (ESC1-ESC15), and modern MDI/Defender for Identity evasion. Use when assessing on-prem AD, hybrid AD/Entra ID environments, or ADCS deployments.DevOpsNo warnings★ 7.7K↓ —
- offensive-zigbee-thread-matterSnailSploit/Claude-RedZigbee, Thread, and Matter mesh-protocol attack methodology — IEEE 802.15.4 sniffing with TI CC2531 / CC2540 / Sonoff Zigbee Dongle E, KillerBee toolkit, Touchlink commissioning abuse with the well-known transport key, replay/injection attacks, Zigbee Cluster Library command abuse for door locks and bulbs, Thread network credential theft, Matter commissioning chain analysis, and 6LoWPAN/IPv6 routing exploitation. Use when targeting smart-home or commercial mesh deployments, Zigbee-based door locks, lighting, or sensor networks.DevOpsNo warnings★ 7.7K↓ —
- offensive-supply-chainSnailSploit/Claude-RedComprehensive offensive methodology for software supply chain attacks covering the full kill chain from reconnaissance through exploitation. Addresses dependency confusion across npm, PyPI, and NuGet ecosystems where internal registry override allows an attacker to inject malicious packages that shadow private dependencies. Covers typosquatting techniques for popular packages, compromised package injection via maintainer account takeover or social engineering, and build system attacks through Makefile injection, setup.py install hooks, and npm postinstall scripts. Extends into CI/CD artifact tampering where build outputs are replaced or modified in transit, code signing abuse through stolen or self-signed certificates, upstream repository compromise via commit injection or force-push to trusted repos, and container image supply chain attacks including base image trojaning and registry confusion. Maps to MITRE ATT&CK T1195.001 (Supply Chain Compromise: Compromise Software Dependencies and Development Tools) anDevOpsNo warnings★ 7.7K↓ —
- offensive-lorawan-sub-ghzSnailSploit/Claude-RedLoRaWAN and sub-GHz (433 / 868 / 915 MHz) attack methodology — LoRaWAN ABP/OTAA join attack, network/session key reuse, frame counter replay, downlink injection on TTN/Helium-style networks, sub-GHz protocol replay (KeeLoq garage doors, fixed-code remotes, TPMS spoofing, smart plug telemetry), HackRF / RTL-SDR / Flipper Zero workflows, signal analysis with Inspectrum / Universal Radio Hacker, and reconstruction of proprietary packet formats. Use for LoRaWAN deployments (smart cities, asset tracking, industrial telemetry), or any wireless device using the unlicensed 433/868/915 MHz bands (garage openers, doorbells, IoT sensors, RC equipment).DevOpsNo warnings★ 7.7K↓ —
- offensive-toctouSnailSploit/Claude-RedTime-of-Check / Time-of-Use (TOCTOU) race condition exploitation methodology across binary, kernel, filesystem, web, and container layers. Covers symbolic-link races (open/access/stat split), file-descriptor races, fopen/realpath traversal races, /proc and procfs races, FUSE-backed slow-fs races to widen the window, ptrace and signal races, kernel double-fetch / userspace pointer races, container/runc/symlink escape primitives, kubernetes admission/authz TOCTOU, web auth-vs-authz TOCTOU, JWT-claim TOCTOU at gateway vs service, payment/idempotency races, and modern race-amplification techniques (single-packet attack, slow loris, FUSE pause, cgroup freeze, scheduler shaping). Use when you've identified a 'check then act' pattern in code, when fuzzing for race conditions, or when exploiting concurrency bugs in privileged binaries / kernel / orchestrators.DevOpsNo warnings★ 7.7K↓ —
- kubernetes-specialistJeffallan/claude-skillsUse when deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster management.DevOpsNo warnings★ 12K↓ —
- salesforce-developerJeffallan/claude-skillsWrites and debugs Apex code, builds Lightning Web Components, optimizes SOQL queries, implements triggers, batch jobs, platform events, and integrations on the Salesforce platform. Use when developing Salesforce applications, customizing CRM workflows, managing governor limits, bulk processing, or setting up Salesforce DX and CI/CD pipelines.DevOpsNo warnings★ 12K↓ —