4 308 DevOps skills from 392 authors. They ship releases, run infrastructure and keep watch over what is deployed. Half of them fit into 2 096 tokens or less — that is what one costs your context window when the agent loads it. 507 ship runnable scripts rather than instructions alone. 28 of them cannot work without an MCP server, most often rube. We also found 633 copies of these same skills sitting in other people's repositories — counted once here, not 633 times.
4 308 unique 392 authors 2 042 updated this month 543 from vendors
Cloudflare Workers local development with Wrangler, Miniflare, hot reload, debugging. Use for project setup, wrangler.jsonc configuration, or encountering local dev, HMR, binding simulation errors.
Cloudflare Workflows for durable long-running execution. Use for multi-step workflows, retries, state persistence, or encountering NonRetryableError, execution failed errors. WorkflowEntrypoint, step.do, step.sleep, workflow retries, NonRetryableError, workflow state, wrangler workflows, workflow events, long-running tasks, step.sleepUntil, step.waitForEvent, workflow bindings
Cloudflare Zero Trust Access authentication for Workers. Use for JWT validation, service tokens, CORS, or encountering preflight blocking, cache race conditions, missing JWT headers.
GitHub repository automation (CI/CD, issue templates, Dependabot, CodeQL). Use for project setup, Actions workflows, security scanning, or encountering YAML syntax, workflow configuration, template structure errors. dependabot, codeql, security scanning, yaml syntax, github automation, repository setup, workflow templates, github actions matrix, secrets management, branch protection, codeowners, github projects, continuous integration, continuous deployment, workflow syntax error, action version pinning, runner version, github context, yaml indentation error
Hugo static site generator with Tailwind v4, headless CMS (Sveltia/Tina), Cloudflare deployment. Use for blogs, docs sites, or encountering theme installation, frontmatter, baseURL errors.
Manage Hetzner Cloud infrastructure with the `hcloud` CLI — servers, networks, firewalls, load balancers, volumes, DNS zones, SSH keys, primary/floating IPs, snapshots, certificates, placement groups, storage boxes. Use whenever the user mentions Hetzner, hcloud, VPS provisioning, or Hetzner location codes (fsn1, hel1, nbg1, ash, hil, sin) — even if they don't say "hcloud". CLI-only; does NOT cover Hetzner Robot (dedicated servers, separate product and API).
> Author useful, low-noise log alerts on services in a PostHog project. Use when the user asks to set up alerts for their logs, suggest alerts they should add, or evaluate whether a service is worth monitoring. Covers service triage, baseline characterisation, threshold drafting, back-testing via simulate, and shipping with a notification destination.
> Debug the signals pipeline locally end-to-end. Covers emitting test signals from fixtures, monitoring Temporal workflows via the REST API, reading sandbox agent logs from object storage, inspecting Docker sandbox containers, and diagnosing common failures (stale ClickHouse embeddings, agentsh network denials, inactivity timeouts). Use when a signal isn't reaching the inbox, a signal-report-summary workflow fails, or a sandbox task run times out.
Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.
> Focused Signals scout for PostHog projects using AI observability. Rotates through a set of lenses — cost, latency, errors, volume, eval performance, eval/enrichment config, clusters, and tool usage — watching each for trends and spikes sliced by the dimensions it discovers over time. Leans on the sandbox's bundled `exploring-llm-*` deep-dive skills for the actual queries. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other scouts.
> Focused Signals scout for PostHog projects moving data through pipelines. Watches the three delivery surfaces — CDP destinations and transformations (hog functions), batch exports, and hog flows (workflows/messaging) — for contradictions between configured rates stepping above a pipeline's own baseline, batch export runs failing or stalling (a growing data gap), and active flows failing for the people they trigger on. Emits findings only when they clear the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
> Focused Signals scout for PostHog projects collecting Content Security Policy (CSP) violation reports. Watches `$csp_violation` events for fresh blocked-URL clusters, per-directive bursts, page-scoped regressions after deploys, and suspicious third-party domains that may indicate a compromised script. Emits aggregated findings only when a cluster clears the confidence bar; otherwise writes durable memory and closes out empty. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
> Focused Signals scout for finding observability gaps in PostHog itself — significant event volumes the team isn't tracking, custom events with no insight or dashboard coverage, insights pointing at events that have stopped firing, dashboards missing related context, critical events with no alerts. Watches the event-stream-vs-saved- inventory delta as the team's product evolves and emits findings recommending new insights, dashboard additions, or alerts when gaps clear the confidence bar. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare (RETAIN, SafeDrug, Transformer, GNN). Part of the AlterLab Academic Skills suite.
Runs Python code in the cloud with Modal — serverless containers, on-demand GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that need GPU acceleration or dynamic scaling. Part of the AlterLab Academic Skills suite.
Dispatch long-running GPU/CPU jobs to remote compute with a provider-agnostic submit → poll → harvest pattern across SLURM/HPC (sbatch, squeue, sacct) and managed APIs (Modal, RunPod, GCP Batch / Vertex AI). Use when submitting a batch job to a cluster, polling job status, retrieving result artifacts from a scheduler or cloud GPU provider, or writing a portable job-submission wrapper; the foundation-model skills (alterlab-alphafold, alterlab-boltz, alterlab-rfdiffusion, and siblings) dispatch their GPU work through this pattern. For Modal-specific serverless container deployment and autoscaling prefer alterlab-modal instead. Part of the AlterLab Academic Skills suite.
Builds and deploys bioinformatics pipelines on the LatchBio platform using the Latch SDK — author workflows with @workflow/@task decorators, handle LatchFile/LatchDir I/O, register serverless workflows, configure CPU/GPU task resources, organize data in the Latch Registry, and wrap Nextflow/Snakemake pipelines. Use when developing or deploying a Latch SDK workflow, sizing task resources, working with the Registry, or porting a Nextflow/Snakemake bioinformatics pipeline onto LatchBio. Not for DNAnexus (dxpy/dx CLI) or generic Flyte. Part of the AlterLab Academic Skills suite.
Guidance for open science practices — preregistration (OSF Registries, AsPredicted, PROSPERO, ClinicalTrials.gov), open data and FAIR principles, repository choice (Zenodo, Dryad, Figshare), open access routes (Green/Gold/Diamond), Creative Commons licensing, reproducible computational workflows (Docker, Binder, Code Ocean), registered reports, open peer review, and TOP Guidelines. Use when preregistering a study, writing the FAIR data-sharing and repository section of a grant data management plan (NSF, NIH, ERC, UKRI), choosing a data repository, navigating open access, or building reproducible analyses. For human-subjects ethics, IRB applications, informed consent, or GDPR/HIPAA compliance, defer to alterlab-research-ethics. Part of the AlterLab Academic Skills suite.
Review Kafka Connect connector configurations for common misconfigurations using the Lenses MCP server. Checks error handling, DLQ setup, converters, transforms, task count and task health. Use when user says "review connectors", "check connector configs", "why is my connector failing" or asks about Kafka Connect configuration. Do NOT use for creating, deploying or controlling connectors.
Generate a TestContainers Java test class that spins up ShadowTraffic in-process to populate a Kafka topic with synthetic data during tests. Invokes the kafka-shadowtraffic skill to build the ShadowTraffic config, then adapts it for a containerized test network and scaffolds a JUnit 5 test class with Kafka, optional Schema Registry, and ShadowTraffic containers wired together. Use when the user says "set up a Java TestContainers test with ShadowTraffic", "write a Java test that generates Kafka data", "create a TestContainers test for my Kafka consumer", "I want to test my Kafka consumer with synthetic data in Java", or "scaffold a ShadowTraffic Java test". Do NOT use for non-Java projects, standalone Docker setups (use kafka-shadowtraffic instead), or creating Kafka topics.
Review dead letter queue implementations for completeness using the Lenses MCP server. Checks DLQ topic existence, configuration, monitoring, metadata preservation, retry logic, reprocessing paths and connector DLQ alignment. Use when user says "review dead letter queues", "check DLQ setup", "DLQ audit" or asks about error handling, message failures or reprocessing. Do NOT use for reprocessing DLQ messages or managing consumer offsets.
Generate a ShadowTraffic configuration to populate a Kafka topic with realistic synthetic data. Discovers the target topic, its key and value schemas, and the correct serializers from the live cluster via any attached Kafka MCP server, then writes a ready-to-run `shadowtraffic-config.json` and Docker command. Use when the user says "populate my Kafka topic with test data", "set up ShadowTraffic for topic X", "generate synthetic events into my topic", "I need fake data flowing into Kafka", "seed my topic with data", or "mock data for my Kafka topic". Do NOT use for creating topics, reviewing schemas, or building Kafka consumers.
Generate and validate Pangolin Newt blueprint configurations in YAML or Docker Labels format. Use when creating Pangolin resource configurations, proxy resources, client resources, authentication settings, or Docker Compose blueprints.
Create new GitLab stack projects from templates with proper directory structure, git configuration, validation hooks, and documentation. Use when initializing new Docker stack projects that follow GitLab stack patterns.
Manages Docker secrets for GitLab stack projects, ensuring secrets are never in .env or docker-compose.yml, properly stored in ./secrets directory, and securely integrated with Docker secrets. Use when users need to create secrets, migrate from environment variables, validate secret configuration, audit secret usage, or ensure secrets are never committed to git.
Comprehensive Docker and Docker Compose validation following best practices and security standards. Use this skill when users ask to validate Dockerfiles, review Docker configurations, check Docker Compose files, verify multi-stage builds, audit Docker security, or ensure compliance with Docker best practices. Validates syntax, security, multi-stage builds, and modern Docker Compose requirements.
Validates GitLab stack projects before deployment, ensuring proper architecture patterns, directory structure, secrets management, .env configuration, and Docker best practices. Use when users ask to validate a stack, check stack configuration, verify stack architecture, audit stack setup, or ensure stack deployment readiness.
> Use when creating, deploying, or managing Dataverse web resources for model-driven apps. Covers JavaScript form scripts (OnLoad, OnSave, OnChange events), HTML dashboard pages, CSS styling, image resources, navigation/side panes, ribbon/command bar customization, "javascript form", "form script", "html dashboard", "ribbon command", "onload event", "onchange event", "onsave event", "formContext", "Xrm.WebApi", "web resource deployment", "dashboard page", "form event handler", "side pane", "command bar", "ribbon", "modal dialog", "navigation", "Xrm.App", "Xrm.Navigation", "business process flow", "bpf", "stage change".
> Use when developing, registering, or deploying Dataverse plugins (C# server-side extensions). Covers the IPlugin interface, execution pipeline stages, entity images, common patterns (auto-numbering, cascading updates, validation), and registration/deployment. "cascading update", "pre-operation", "post-operation", "plugin registration", "IPlugin", "execution pipeline", "plugin trace", "InvalidPluginExecutionException", "PreValidation", "PostOperation".
> Use when building, deploying, or using PowerApps Component Framework (PCF) controls. Covers field controls and dataset controls, React virtual controls, control lifecycle, "custom control", "component framework", "dataset control", "virtual control", "pac pcf", "pcf init", "pcf push", "ControlManifest", "StandardControl", "ReactControl", "updateView", "getOutputs".
Scan all unresolved Sentry errors, group by root cause, fix in batch with TDD, and resolve in Sentry after deploy. Use when the user says 'fix sentry errors', 'scan sentry', 'resolve sentry issues', 'fix all errors', 'sentry cleanup', or wants to proactively fix production errors from Sentry monitoring.
Pre-deployment validation of Cisco IOS/IOS-XE configuration — catching missing saves, mismatched ACLs, overlapping subnets, duplicate IPs, missing route-maps, and dangerous commands before they cause outages.
Design an end-to-end MotherDuck data pipeline. Use for ETL/ELT workflows -- choosing raw, staging, and analytics boundaries, bulk ingestion paths, transformation sequencing, dlt/dbt integration, publication targets, or whether DuckLake is actually required.
Deliver repeatable MotherDuck architectures across multiple clients. Use when a consultancy, agency, or multi-client product team needs to standardize isolation, provisioning, regional deployment, sharing boundaries, and client-specific exceptions across client engagements.
Design an end-to-end MotherDuck data pipeline. Use for ETL/ELT workflows -- choosing raw, staging, and analytics boundaries, bulk ingestion paths, transformation sequencing, dlt/dbt integration, publication targets, or whether DuckLake is actually required.
Deliver repeatable MotherDuck architectures across multiple clients. Use when a consultancy, agency, or multi-client product team needs to standardize isolation, provisioning, regional deployment, sharing boundaries, and client-specific exceptions across client engagements.
Create a new implementation plan file for new features, refactoring existing code or upgrading packages, design, architecture or infrastructure.
Update an existing implementation plan file with new or update requirements to provide new features, refactoring existing code or upgrading packages, design, architecture or infrastructure.
Produce a Competitive Analysis Pack (alternatives map, landscape, battlecards, monitoring plan).
Produce a Platform & Infrastructure Pack (capabilities plan, scaling triggers, execution roadmap).
Plan product launch/release: rollout/rollback plan, comms, monitoring, post-launch review. See also: launch-marketing (marketing side).
AWS CLI 操作
Azure CLI 操作
Ansible 自动化运维
CI/CD 流水线配置
Docker Compose 编排
Terraform 基础设施即代码
Docker 容器操作与管理