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.
45,201 skills from 2,196 repositories · updated 20 minutes ago
2,316 skills
- kamaeiwasa-kosui/kamae-tsKamae (構え) — robust server-side TypeScript design. Functional domain modeling with discriminated unions, pure state transitions, Result types, schema-validated boundaries, and PII protection. TRIGGER when: writing TypeScript domain models, use cases, repositories, state transitions, error handling, boundary validation, or PII handling on the server side; designing types for business logic; implementing entity/value-object semantics in TS. SKIP: frontend React/Vue components, browser code, build tooling, code generation scripts, pure infrastructure-as-code; code unrelated to domain logic.DevOpsNo warnings★ 55↓ —
- preflightnitinjain999/platform-skillsProduction-readiness preflight check for a directory, repo, or single file. Auto-detects file types (Kubernetes manifests, Terraform, GitHub Actions workflows, Helm values/charts, Flux Kustomizations/HelmReleases, Dockerfiles, shell scripts) and applies type-specific checks across the whole scope. Returns a per-file summary table and aggregated verdict. Use before deploying, merging, or applying a folder of config. For PR diffs spanning multiple files use /platform-skills:pr-review instead. For deep Helm chart work use /platform-skills:helmchart instead.DevOpsReview first★ 70↓ —Also in 1 other repo
- config-hardeningproflead/codex-skills-libraryHarden configuration and defaults for safer deployment. Use when a mid-level developer needs to reduce misconfig risks.DevOpsNo warnings★ 163↓ —
- lamindbBioTender-max/awesome-bio-agent-skillsThis skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.DevOpsNo warnings★ 208↓ —
- aws-sst-developmentzxkane/aws-skillsSST v4 (Ion) expert for managing AWS resources as code with the Pulumi-backed framework. Use when writing or editing sst.config.ts, building infra/ modules (sst.aws.Function/Bucket/Dynamo/Cron/Service/Router, sst.Secret, sst.Linkable, raw aws.* Pulumi resources), wiring resource links, scoping IAM, or running sst deploy/dev/diff/remove. Essential when the user mentions SST, sst.config.ts, $config, $transform, $interpolate, sst.aws.*, sst.Secret, Pulumi/Ion, "sst deploy", a failed SST deploy (ConflictException on a resource-type change, "Identifier '__filename' has already been declared", MalformedPolicyDocument on an Output<T>), or wants to scaffold/troubleshoot AWS infrastructure with SST. Also use when a request to "deploy my AWS stack" or "add a Lambda/bucket/table" is made in a repo that already contains an sst.config.ts (using $config) or an sst dependency. Do NOT use when the task is primarily AWS CDK, Terraform, raw CloudFormation, or SAM with no SST present — those have their own tooling.DevOpsReview first★ 371↓ —
- aws-clichaterm/terminal-skillsAWS CLI 操作DevOpsNo warnings★ 59↓ —
- deploying-on-azureancoleman/ai-design-componentsDesign and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.DevOpsNo warnings★ 526↓ —
- configmap-secretchaterm/terminal-skillsKubernetes ConfigMap 与 SecretDevOpsNo warnings★ 59↓ —
- implementing-mlopsancoleman/ai-design-componentsStrategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.DevOpsNo warnings★ 526↓ —
- hunt-cloudEncod3d-Sec/TORCHCloud attack hunting for AWS / Azure / GCP - credential discovery, metadata SSRF, IAM privesc, service enumeration, persistence. Scope + billing aware. Wiki-first, FIND schema output.DevOpsReview first★ 335↓ —
- diagnoseavibebuilder/claude-primeInvestigate unexpected behavior and mysterious bugs. Use when the cause of a problem is unknown and the user needs to understand WHY something is happening — symptoms like: sudden unexplained changes in metrics or behavior, works locally but not in staging/production, inconsistent or intermittent failures, correct code producing wrong results, operations succeeding but having no effect, environment-specific failures, duplicate executions, stale data, or any "why did this change?" or "why is this happening?" situation. Covers infrastructure anomalies (cache hit rates dropping, latency spikes, queue behavior shifts) as well as code bugs. The key signal is confusion about root cause, not a request to implement a known fix. Do NOT use for feature requests, known fixes, planning, or documentation tasks.DevOpsNo warnings★ 120↓ —
- ansiblechaterm/terminal-skillsAnsible 自动化运维DevOpsNo warnings★ 59↓ —
- dspy-finetune-bootstrapOmidZamani/dspy-skillsUse for BootstrapFinetune, fine-tuning DSPy models, teacher-student distillation, weight optimization, and lower-cost deployment.DevOpsNo warnings★ 124↓ —
- dspy-production-deploymentOmidZamani/dspy-skillsUse for deploying DSPy with save/load, configure_cache, restrict_pickle, track_usage, async execution, streaming, and production runtime controls.DevOpsNo warnings★ 124↓ —
- dspy-debugging-observabilityOmidZamani/dspy-skillsUse for debugging DSPy programs, inspect_history, tracing LLM calls, custom callbacks, observability, monitoring, and cost tracking.DevOpsNo warnings★ 124↓ —
- implementing-gitopsancoleman/ai-design-componentsImplement GitOps continuous delivery for Kubernetes using ArgoCD or Flux. Use for automated deployments with Git as single source of truth, pull-based delivery, drift detection, multi-cluster management, and progressive rollouts.DevOpsNo warnings★ 526↓ —
- deep-divenelsonwerd/idea-to-ship-skillsRigorous multi-agent deep-dive analysis for complex investigative tasks — auditing codebases, evaluating strategies or systems, validating designs, doing open-ended research. Deploys 4–6 specialist agents in parallel across distinct lanes, then synthesis, then adversarial red-team review, then optional patching — producing structured markdown research files plus a plain-English executive briefing with honest 1–10 confidence ratings. ALWAYS invoke when the user says any of "deep dive", "thorough audit", "rigorous analysis", "comprehensive review", "audit this codebase", "analyze the strategy", "evaluate this design", "review this thoroughly", or "research deep dive". Also invoke proactively for any open-ended investigative task involving a codebase, strategy, system design, or research question that warrants 30+ minutes of structured analysis — even when the user doesn't use these exact phrases.DevOpsReview first★ 90↓ —
- ci-runner-auditUpdatedapache/magpieRead-only audit of GitHub Actions runner compatibility for one repository, a repository set, one Apache project, or the full Apache org. Finds obsolete GitHub-hosted runner labels and macOS runner/tool architecture mismatches. Produces TSV evidence; never edits workflows, opens PRs, or posts comments.DevOpsReview first★ 127↓ —
- operating-kubernetesancoleman/ai-design-componentsOperating production Kubernetes clusters effectively with resource management, advanced scheduling, networking, storage, security hardening, and autoscaling. Use when deploying workloads to Kubernetes, configuring cluster resources, implementing security policies, or troubleshooting operational issues.DevOpsNo warnings★ 526↓ —
- deploying-applicationsancoleman/ai-design-componentsDeployment patterns from Kubernetes to serverless and edge functions. Use when deploying applications, setting up CI/CD, or managing infrastructure. Covers Kubernetes (Helm, ArgoCD), serverless (Vercel, Lambda), edge (Cloudflare Workers, Deno), IaC (Pulumi, OpenTofu, SST), and GitOps patterns.DevOpsNo warnings★ 526↓ —
- writing-github-actionsancoleman/ai-design-componentsWrite 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.DevOpsNo warnings★ 526↓ —
- implementing-observabilityancoleman/ai-design-componentsMonitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.DevOpsNo warnings★ 526↓ —
- using-timeseries-databasesancoleman/ai-design-componentsTime-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.DevOpsNo warnings★ 526↓ —
- writing-infrastructure-codeancoleman/ai-design-componentsManaging cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.DevOpsNo warnings★ 526↓ —