Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Cloud & DevOps
Inventory, classify, and pin every `uses:` reference in GitHub Actions workflows, composite actions, and reusable workflows to a verified commit SHA, then keep those pins fresh with Dependabot. Use when the user says "pin actions", "unpinned actions", "SHA pin", "supply chain for workflows", or invokes /actions-pinning. Do NOT use for permissions and injection hardening of workflow files (route to /oss-hygiene) or for CI run-time speed (route to /ci-optimize).
Perform security inspection and monitoring for Alibaba Cloud WAF (Web Application Firewall), covering CNAME-based domain access, hybrid cloud access, and cloud product access assets. Supports querying Web attacks, CC attacks, Bot attacks, API security events, traffic analysis (QPS/bandwidth), HTTP status code anomalies (4xx/5xx period-over-period), protection status, certificate expiry, and instance asset inventory. Use this Skill when users need WAF security inspection, protection status checks, attack event queries, traffic anomaly investigation, or to confirm whether WAF products are provisioned. Triggers: "WAF inspection", "WAF security check", "Web attack query", "traffic anomaly", "certificate check"
Use when managing Alibaba Cloud TongyiTranslate (AnyTrans) via OpenAPI/SDK, including the user needs translation service resource operations in Alibaba Cloud, including list/create/update actions, task status checks, and troubleshooting AnyTrans API workflows.
Use when managing Alibaba Cloud Virtual Private Cloud (VPC) via OpenAPI/SDK, including listing or creating VPCs and VSwitches, querying available zones, deleting VPC resources, managing route tables, and troubleshooting VPC network configurations.
Author error tracking alerts that fire when an issue is created, reopened, or starts spiking. Use when the user asks to set up error notifications, route exceptions to Slack/webhook/Linear, or evaluate which error events are worth alerting on. Covers trigger-event selection, integration choice, dedup against existing alerts, and shipping with the canonical message body shape.
[Alpha] Experimental Azure environment builder for infrastructure and deployment design. Use when building Azure infrastructure, generating Bicep with AVM modules, deploying apps to App Service/AKS/Container Apps, designing Hub-Spoke/AKS/AI Foundry architectures, or creating low-cost Azure operations demos.
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table management, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP server integration for AI-assisted querying. Modern TypeScript/Bun implementation replacing the Python `bq` CLI with instant startup (~10ms vs ~500ms), automatic cost awareness with confirmation prompts, and native streaming support (JSONL). Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files) from Cloud Storage.
CloudBase platform overview and routing guide. This skill should be used when users need high-level capability selection, platform concepts, console navigation, realtime (broadcast / presence / live database changes), or cross-platform best practices before choosing a more specific implementation skill.
Run LLMs and AI models on Cloudflare's global GPU network with Workers AI. Includes Llama, Flux image generation, BGE embeddings, and streaming support with AI Gateway for caching and logging. Use when: implementing LLM inference, generating images with Flux/Stable Diffusion, building RAG with embeddings, streaming AI responses, using AI Gateway for cost tracking, or troubleshooting AI_ERROR, rate limits, model not found, token limits, or neurons exceeded. Keywords: workers ai, cloudflare ai, ai bindings, llm workers, @cf/meta/llama, workers ai models, ai inference, cloudflare llm, ai streaming, text generation ai, ai embeddings, image generation ai, workers ai rag, ai gateway, llama workers, flux image generation, stable diffusion workers, vision models ai, ai chat completion, AI_ERROR, rate limit ai, model not found, token limit exceeded, neurons exceeded, ai quota exceeded, streaming failed, model unavailable, workers ai hono, ai gateway workers, vercel ai sdk workers, openai compatible workers, workers ai vectorize
Generate draw.io network diagrams — native .drawio files with CLI export (PNG/SVG/PDF), plus browser-based Mermaid/XML/CSV via MCP server. Use when creating network topology diagrams, generating architecture visuals, exporting diagrams to PNG or PDF, or building draw.io files from discovery data.
Send and diagnose transactional email through Twilio SendGrid from a terminal or agent: send messages, check deliverability (bounces and spam reports), and verify Signed Event Webhook signatures (ECDSA P-256) — with a bundled email-cli script that is read-only by default and gates every send behind a --dry-run/--yes confirmation. Use when an agent needs to send a transactional email, triage bounces or spam complaints, or confirm an inbound SendGrid webhook is authentic. Do not use for marketing or bulk email campaigns (that is SendGrid Marketing Campaigns), building email template systems, or other email providers (that is their own tooling).
Use this skill for persistent PostgreSQL storage in agent workflows — spin up databases on demand, test schema migrations safely on forks, and pass databases as assets between agents using Ghost. **Trigger when user asks to:** - Create a new PostgreSQL database quickly - Manage database lifecycle in agent workflows - Fork a database to test changes safely - Run analytics or heavy queries on a fork without impacting the production database - Share a database snapshot so others can spin up their own copy - Pass a database as an input or output between agents - Create a database for agents - Need many databases that are billed only when queried - Set up the Ghost MCP server or CLI **Note:** If the Ghost MCP server is already installed, use its tools directly — this skill is for environments without the MCP or as a reference for what's possible. **Keywords:** Ghost, ghost.build, database, create database, fork database, PostgreSQL, managed Postgres, MCP, agent database, connection string
Deploy, manage, and troubleshoot self-hosted GitHub Actions runners. Covers systemd service, Docker containers, Kubernetes (Actions Runner Controller), and the Scale Set Client. Use when setting up a CI runner, debugging registration failures, designing autoscaling, or hardening runner security. Do not use this skill for unrelated requests; route to the nearest named specialist.
Diagnose CDN domain DNS resolution issues using hcloud CLI and Python DNS probes. Query the CDN domain detail to obtain the expected CNAME, probe the actual DNS resolution of the domain via scripts/dns_resolve.py (dnspython) to collect resolved IP addresses, and verify the IP attribution against Huawei Cloud CDN via ShowIpInfo/v2 to determine whether the domain has been correctly resolved to Huawei Cloud CDN. Use this skill when the user wants to: (1) diagnose CDN DNS resolution failures, (2) check why a domain is not resolved to Huawei Cloud CDN, (3) verify DNS configuration for CDN domain access, (4) troubleshoot DNS anomaly or resolution timeout issues. Triggers include: "DNS解析诊断", "DNS异常", "域名解析失败", "CDN解析异常", "DNS配置", "DNS diagnosis", "resolution diagnosis". User utterance examples: "域名解析不到华为云CDN", "CNAME 配置了但没生效", "DNS 解析超时了", "解析出来的 IP 不是华为云的", "域名没解析到 CDN". Do NOT use for: HTTPS certificate errors, origin-pull failures, domain ownership verification, or any CDN/DNS configuration change (this skill is strictly read-only).
Huawei Cloud MRS cluster fault diagnosis skill. Diagnoses service faults, instance faults, and host faults through progressive root cause localization: quick log scan first, host troubleshooting when host issues are found, detailed investigation when no conclusion is reached. Diagnosis is driven by the built-in LakeWatch API client (lakewatch mode) or the MRS Manager API client (manager mode) and the per-layer knowledge base under fault_layer/ and scenarios/ (lakewatch) or fault_layer_manager/ and scenarios_manager/ (manager). The API mode is auto-detected by check_api_mode.py. No commands outside the knowledge base are fabricated. Applicable to MRS fault diagnosis and root cause localization scenarios where a service name or node name is provided. Trigger words: "故障诊断", "故障定位", "fault diagnosis", "fault diagnose", "MRS故障", "服务故障", "实例故障", "主机故障", "集群排查", "集群诊断", "启动失败", "停止异常", "KrbServer故障", "DBService故障", "fault troubleshooting"
Huawei Cloud algorithm auto-design (LLM4AD) skill. Performs the full lifecycle through KooCLI (command-line entry `hcloud`): algorithm project management, file upload, evolve task CRUD, status polling, result retrieval, and log inspection; also uses KooCLI's `hcloud IAM` subcommand to create the trust agency and grant algorithm design module bucket permissions. Trigger words: code evolution, evolve task, LLM4AD, algorithm optimization, upload algorithm file, create evolve task, start evolve task, fetch evolve result, evolve log, 代码演化, 演化任务, LLM4AD, code evolution, 算法优化, 上传算法文件, 创建演化任务, 启动演化任务, 拉取演化结果, 演化日志
Add PostHog metric capture to track counters, gauges, and histograms. Use after implementing features or reviewing PRs to ensure key operations are measured, or when asked to add metrics, counters, or track a value over time. Also handles mirroring existing Prometheus/StatsD/OpenTelemetry metrics into PostHog and initial OTLP exporter setup.
Analyse Kafka consumer group lag using the Lenses MCP server. Diagnoses lag causes (throughput bottlenecks, rebalancing, partition skew, stalled consumers) and suggests remediation. Use when user says "check consumer lag", "why are consumers slow", "lag report" or asks about consumer group health or offset progress. Do NOT use for resetting offsets or managing consumer groups.
Audit Kafka security configuration across the codebase and live cluster using the Lenses MCP server. Checks authentication (SASL), encryption (SSL/TLS), authorisation (ACLs), secrets management and environment tier mismatches. Use when user says "audit Kafka security", "check security config", "is my cluster secure" or asks about authentication, encryption or credentials. Do NOT use for configuring certificates, creating SASL users or setting up ACLs.
Estimate and compare what a workload costs on Lightning AI - fetch live per-hour GPU/CPU machine prices from the accelerator catalog for every cloud (Lightning Cloud, AWS, GCP, Lambda Labs, Nebius, Voltage Park), size hardware for a model, then quote a training run, fine-tune, serving deployment or data-prep job over hours to months, including spot rates, multi-node fan-out and Drive storage. Use when the user asks "how much would it cost to train/serve X", "what's the price of an H100/B200 on lightning", "how much for 32 8xH100 nodes for 2 weeks", "which cloud is cheapest for this", "what will my monthly bill be", or wants hardware sized for a model before pricing it.
Before fixing a slow/stale/timeout alert, measure the step yourself. Kill the theory with a stopwatch, not a code change. Measure-first ops triage for temporal alerts (stale, timeout, freshness, wedged, N hours behind) from gbrain doctor, autopilot, sync, and cron monitors — runs BEFORE any timeout raise, threshold change, or pipeline rewrite.
LLM and ML model deployment for inference. Use when serving models in production, building AI APIs, or optimizing inference. Covers vLLM (LLM serving), TensorRT-LLM (GPU optimization), Ollama (local), BentoML (ML deployment), Triton (multi-model), LangChain (orchestration), LlamaIndex (RAG), and streaming patterns.
Comprehensive guidance for building and deploying static websites with the Astro framework. This skill should be used when asked to "create astro site", "deploy astro to firebase", "set up content collections", "add mermaid diagrams to astro", "configure astro i18n", "build static blog", or "astro markdown setup". Covers SSG fundamentals, Content Collections, Markdown/MDX, partial hydration, islands architecture, and deployment to Netlify, Vercel, GitHub Pages, or GCP/Firebase.
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.