Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Cloud & DevOps
Use when debugging services on a user's dedicated server via SSH. Use when needing to run a command on the server, inspect pods, check container logs, view k8s resources, or run kubectl commands. Use when "service exec" is insufficient and you need server-level access. Use when user says "check my server", "run X on my server", "debug pod", "kubectl", "SSH into server", "check k8s", or "inspect cluster".
Alibaba Cloud Live Recording Diagnostic Skill. Use for diagnosing live stream record issues including missing recordings, file generation problems, unexpected recording behavior, and callback issues. Triggers: "live record", "live stream record diagnosis", "record not working", "record file missing", "record callback issue", "录制诊断", "直播录制问题".
Initialise a project's App Store Connect context using the `asc init` command. Use this skill when: (1) Saving app context to a project directory: "asc init", "pin my app ID", "set up project context" (2) Auto-detecting the app from an Xcode project: user says "run asc init in my project folder" (3) Searching by app name: "asc init --name 'My App'" (4) Explaining what .asc/project.json is used for (5) Reading saved project context to avoid running asc apps list every session (6) Saving review contact info: "asc init --contact-email ...", "set review contact", "save review contact"
Use when the user wants to prepare, create, or generate an AWS FIS (Fault Injection Service) experiment configuration. Triggers on "prepare FIS experiment", "create FIS experiment for [scenario]", "generate chaos experiment config", "准备 FIS 实验", "生成 [scenario] 混沌实验配置", "create experiment template for AZ power interruption", "set up fault injection test". Covers Scenario Library pre-built scenarios (AZ Power Interruption, AZ Application Slowdown, Cross-AZ Traffic Slowdown, Cross-Region Connectivity), custom single FIS actions (aws:rds:failover-db-cluster, aws:ec2:stop-instances, etc.), and SSM Automation-based fault injection for Amazon MSK (broker reboot) and ElastiCache Redis/Valkey (primary node reboot, replication group failover).
Help with SAP BTP ABAP Environment setup and development including service instance creation, ADT connectivity, communication arrangements, communication scenarios, inbound/outbound services, destination configuration, identity and access management, software components, and first-project scaffolding. Use when users ask about BTP ABAP Environment, SAP BTP ABAP, Steampunk, ABAP environment service instance, ADT connection to BTP, communication arrangement, communication scenario, communication system, outbound communication, inbound communication, destination service, software component, ABAP system on BTP, or cloud ABAP development setup. Triggers include "set up BTP ABAP", "connect ADT to BTP", "communication arrangement", "communication scenario", "create service instance", "ABAP on BTP", "outbound service", or "first ABAP project on BTP".
Use when the user wants to drive the CrowdSec Console **Service API (SAPI)** — the premium cloud REST API at admin.api.crowdsec.net — to programmatically manage blocklists (add/remove/bulk IPs, share, subscribe engines), allowlists, firewall/appliance integrations (Palo Alto, Fortinet, Cisco, F5, Sophos, pfSense/OPNsense…), remediation ROI metrics, and org-level decisions. Acts on the user's behalf with their API key. This is the cloud/API skill — for the local engine, cscli, and bouncers use the `crowdsec` skill.
Set up local development environments with production parity for DigitalOcean App Platform. Use when setting up local dev, adding devcontainer to a project, running App Platform apps locally, or configuring backing services (Postgres, Redis, Kafka, S3).
Analyze disk space usage, filesystem mounts, and storage allocation on Linux systems. Identifies large files and directories, checks partition usage, and reports inode consumption. Use when the user asks about disk full errors, free space, storage usage, du/df output, finding large files, or checking which directories consume the most space.
Create and review Cloudflare Durable Objects. Use when building stateful coordination (chat rooms, multiplayer games, booking systems), implementing RPC methods, SQLite storage, alarms, WebSockets, or reviewing DO code for best practices. Covers Workers integration, wrangler config, and testing with Vitest.
An engineering runbook — service overview, alerts table, dashboards links, common procedures with copy-pasteable commands, on-call rotation, and an incident-response checklist. Use when the brief mentions "runbook", "ops doc", "on-call guide", "SRE doc", or "运维手册".
Complete live streaming and protocol system for FFmpeg 7.1 LTS and 8.0.1 (latest stable, released 2025-11-20). PROACTIVELY activate for: (1) RTMP streaming to Twitch/YouTube/Facebook, (2) HLS output and adaptive bitrate (ABR), (3) DASH streaming setup, (4) Low-latency streaming (LL-HLS, LL-DASH), (5) SRT protocol configuration, (6) WebRTC/WHIP sub-second latency (FFmpeg 8.0+), (7) Protocol conversion (RTMP to HLS), (8) Multi-destination streaming, (9) nginx-rtmp integration, (10) Docker streaming services. Provides: Platform-specific stream commands, ABR ladder examples, encryption setup, latency optimization, WHIP authentication, production patterns. Ensures: Reliable live streaming with optimal quality and latency.
Use when designing multi-tenant OCI environments, setting up production landing zones, implementing compartment hierarchies, or establishing governance foundations. Covers Landing Zone reference architectures, compartment strategy, network topology patterns (hub-spoke vs multi-VCN), IAM structure, tagging standards, and cost segregation.
Fetch environment variables from a Contentstack Launch project, compare them with a local `.env.example` file, and patch the target environment to add any missing keys. Use when a Launch environment must match the keys defined in a local `.env.example` file.
Trigger a Launch deployment for a specific environment, then poll its status until completion. If the deployment fails or is cancelled, fetch the deployment log and diagnose likely causes with next-step recommendations. Use when you need to automate Launch deployments for a known project and environment, monitor progress, and surface failure diagnostics. This is appropriate for CI/CD or operator workflows that need a deterministic deploy status check and log-based troubleshooting.
Use this skill when the user wants to browse, filter, or investigate Nebula Logger data in the Salesforce UI. Covers the Logger Console app, list views on `Log__c` / `LogEntry__c` / `LogEntryTag__c` / `LoggerScenario__c` / `LoggerTag__c`, related-list navigation, the Log record page and its LWCs, and how admins and support engineers use the console day-to-day.
Use when a PinMe Cloudflare Worker needs R2 object storage, including secure file or image upload, streaming download, metadata lookup, deletion, listing, Range requests, or R2+D1 coordination. Guides AI to use PinMe's automatically injected env.R2 binding without R2 credentials or manual Wrangler configuration.
Guided setup assistant for self-hosting open-source Agenta and running an agent on it. Use when the user wants to self-host Agenta, set up an Agenta agent on their own machine or server, run an agent locally, or says "help me self-host Agenta", "set up an agent", "self-host and run an agent". Walks the user through the decisions, executes against the OSS self-host docs, and ends with a working, tested agent.
Initializes, configures, builds, publishes, deploys, undeploys, and locally tests Tilebox workflow projects and releases with tilebox.workflow.toml and dynamic runners. Use when scaffolding workflow projects, iterating on workflow code, release artifacts, deployment targets, cluster runners, or completing an implicit Earth observation project routed to Tilebox.
On-demand and reserved GPU clusters (H100, H200, B200) on Together AI with Kubernetes or Slurm orchestration, shared storage, credential management, and cluster scaling for ML and HPC jobs. Reach for it when the user needs multi-node compute or infrastructure control rather than a managed model endpoint.
Remote Python execution in managed sandboxes on Together AI with stateful sessions, file uploads, data analysis, chart generation, and notebook-like runs via the Sandboxes API. Reach for it whenever the user wants managed remote Python execution instead of local execution, raw clusters, or full model hosting.
UiPath platform ops via the uip CLI — use for ANY task hitting UiPath Cloud / Orchestrator / Studio Web / Integration Service / Data Fabric / LLM Gateway. Load BEFORE writing code that calls a UiPath API. Covers auth, folders, assets, queues, storage buckets, libraries, webhooks, triggers, processes, jobs, machines, users, roles, sessions, calendars, IS connectors/connections/activities, Data Fabric entities/records/files/choice-sets (`uip df`), BYO LLM and BYO guardrail (BYOG) configurations, context grounding, traces (spans + feedback / annotation), licensing. For historical job metrics/trends→uipath-insights. For 'why did X fail' / root-cause→uipath-troubleshoot. For org/tenant audit trail / audit logs / login history→uipath-admin. For `uip solution` lifecycle→uipath-solution. For PDD/SDD design→uipath-planner. For workflow code (.xaml/.cs)→uipath-rpa, .flow (incl. Data Fabric connector nodes)→uipath-maestro-flow, .bpmn→uipath-maestro-bpmn, agents (.py/agent.json)→uipath-agents, Test Manager→uipath-test.
Audit an existing VictoriaMetrics vmanomaly configuration against runtime capabilities and real data. Use when reviewing model-data fit, scheduler cadence, persisted-state compatibility, cold start, excessive detections, missing anomalies, output cardinality, or upgrade readiness. Trigger on vmanomaly config review, false-positive investigation, detection quality, model effectiveness, compatibility, and "is my anomaly detection working?" Use vmanomaly-config instead when building a new configuration from scratch.
Write GitHub Actions workflows with proper syntax, reusable workflows, composite actions, matrix builds, caching, and security best practices. Use when creating CI/CD workflows for GitHub-hosted projects or automating GitHub repository tasks.
Manage App Store review contact info and demo account settings using the `asc` CLI tool. Use this skill when: (1) Getting current review info: "asc version-review-detail get --version-id ID" (2) Setting review contact info before submission: "asc version-review-detail update --version-id ID ..." (3) Configuring demo account credentials for App Review team (4) User asks "set review info", "add review contact", "configure demo account", "fix reviewContactCheck" (5) Diagnosing a failed reviewContactCheck from asc versions check-readiness
Expert guidance for Azure DevOps CLI (az devops) - automation, pipelines, repos, boards, and artifacts management. Use when working with Azure DevOps, managing pipelines, creating work items, or when user mentions ADO, builds, releases, or Azure repos.
Writes and tunes Falco rule syntax for container escape detection - conditions, macros, lists, priorities, and output fields - covering host filesystem mounts, sensitive host path access, kernel module loading, and privileged capability abuse, including how to drive down false positives. Use when authoring or tuning a specific Falco rule for breakout behaviour, or triaging a noisy escape-related Falco alert. Keywords: Falco rule, macro, list, condition, priority, falco_rules.local.yaml, tuning, false positive. Do not use for deploying and operating Falco itself - use detecting-container-runtime-threats-with-falco; for tool-agnostic escape signals use detecting-container-escape-attempts.
Detects and prevents privilege escalation inside Kubernetes pods by combining admission control (OPA policies), runtime monitoring (Falco), and audit log analysis of security contexts, Linux capabilities, and syscall patterns. Use when investigating a pod running as root or privileged, hardening workloads against in-pod escalation, or hunting for containers exceeding their intended scope. Keywords: allowPrivilegeEscalation, runAsRoot, capabilities, securityContext, OPA, Falco, audit log. Do not use for escalation through RBAC and service-account permissions - use auditing-kubernetes-rbac-privilege-escalation.
Analyze real projects and generate or revise secure, verifiable GitHub Actions workflows; use when the user asks to create CI, test matrices, builds, releases, deployments, caching, Secrets, OIDC, PR automation, or to troubleshoot workflow configuration
Use when an outage, production incident, or significant service degradation has occurred and the team needs to write a structured blameless post-mortem. Triggers on phrases like "write a post-mortem", "incident review", "what went wrong", "outage report", "root cause analysis", or "RCA". Covers timeline reconstruction, contributing factor analysis, impact quantification, and action item generation with owners.
Operate OmniGraph graphs and deployments. Use for `.pg` schemas, `.gq` queries, OmniGraph CLI commands, `file://`/`s3://`/`az://` graph URIs, `cluster.yaml`, operator config, bearer-authenticated servers, graph-backed knowledge or memory, Blob values, embeddings, branches, commits, and change feeds. Apply especially before schema changes, bulk loads, and retries after uncertain remote outcomes.
Use this skill when building or operating internal developer platforms: infrastructure as code, CI/CD, container orchestration, service networking, secrets, and observability, or when shaping platform products, self-service interfaces, adoption evidence, and policy guardrails. Do not use it to define release process, promotion, rollout, or rollback policy; use release-engineering for that delivery model. Route named-tool configuration and troubleshooting to the corresponding operational skill, such as kubernetes or traefik for ingress-controller configuration.
Expert guide to end-to-end Python video pipelines combining FFmpeg, OpenCV, PyAV, ffmpegcv, Decord, VidGear, and Modal.com for scalable GPU-accelerated workflows. PROACTIVELY activate for: (1) FFmpeg+OpenCV in same pipeline (decode, process, re-encode); (2) color-format mismatches (BGR vs RGB across OpenCV, PIL, PyAV, FFmpeg); (3) frame dim ordering (HWC vs CHW) between OpenCV and ML frameworks; (4) audio stream loss in filter chains; (5) memory mgmt for long/large videos (streaming vs in-memory); (6) choosing ffmpegcv/Decord/VidGear/PyAV for performance; (7) GPU decode/encode on Modal.com; (8) parallel + chunk-based processing on Modal; (9) transcoding pipelines on serverless; (10) HLS generation on Modal; (11) upload -> process -> transcode -> HLS workflows; (12) batch sizing for GPU memory, pixel formats, large-video streaming. Provides: library selection matrix, integration recipes, Modal.com deployment examples, GPU pipeline tuning, production-ready workflow examples.
Test environment provisioning, infrastructure as code for testing, Docker/Kubernetes for test environments, service virtualization, and cost optimization. Use when managing test infrastructure, ensuring environment parity, or optimizing testing costs.
Use when the user needs system design, architecture decision records, scalability analysis, trade-off evaluation, or non-functional requirements planning. Triggers: new system design, technology selection, scaling strategy, ADR creation, infrastructure topology, service boundary definition.
Sets up test coverage reporting in a repository and configures upload to Codacy. Detects testing frameworks, CI/CD pipelines, and coverage gaps, then adds the missing pieces to generate and upload coverage reports. Use whenever the user wants to set up coverage, add coverage reporting, integrate coverage with Codacy, fix missing coverage uploads, troubleshoot coverage not showing up, or configure CI to send coverage data. Also trigger when the user mentions test coverage, code coverage, coverage reports, or wants to know why Codacy shows no coverage for their repo.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.