ai-skills

Cloud Architecture — Azure, Google Cloud Platform, multi-cloud strategy, cloud landing zones, Azure Landing Zones, GCP organization hierarchy, Well-Architected Framework, cloud migration, FinOps, cost optimization, cloud networking, VPC, VNet, peering, VPN, CDN, WAF, cloud security, Entra ID, Cloud IAM, Workload Identity, compliance (GDPR, HIPAA, SOC 2, ISO 27001), managed services, AKS, GKE, serverless, Cloud Run, Azure Functions, data services, Cosmos DB, Spanner, BigQuery, Terraform, Bicep, disaster recovery, high availability

Content Design — page content strategy, conversion copywriting, visual content direction, landing page optimization, hero sections, value propositions, CTAs, social proof, testimonials, brand voice, content hierarchy, F-pattern, Z-pattern scanning, emotional design, storytelling, AI content generation, image selection, infographics, product promotion, attention-grabbing content, content personalization

Technical Content Writing — documentation strategy, Diátaxis framework, API documentation, UI microcopy, technical guides, docs-as-code, information architecture, release notes, troubleshooting guides, style guides, AI product documentation, SEO for docs

Data Engineering — ETL/ELT pipelines, Apache Spark, Apache Airflow, dbt, data warehousing, data lakes, BigQuery, Snowflake, Kafka, data modeling, data quality, schema evolution, batch and stream processing, data governance, data observability

Database Engineering — PostgreSQL, MySQL, SQL Server, MongoDB, Redis, Cassandra, DynamoDB, database design, schema modeling, SQL optimization, indexing strategies, partitioning, replication, backup and recovery, migrations, stored procedures, database security, connection pooling, NoSQL data modeling, time-series databases, graph databases, database monitoring, capacity planning

Use this skill when bootstrapping a target repository to be ai-skills-aware — on the first run of any ai-skills workflow in a fresh repo, when adopting the ai-skills plugin in an existing repo, or after upgrading to a plugin version that adds new memory paths or templates, including when the user does not say "init" but asks to "set up" or "onboard" the repo — to detect codebase type, create CLAUDE.md + AGENTS.md scaffolding, initialize the .ai-skills-memory/ directory tree from L1 templates, and configure .gitignore. Idempotent — safe to re-run. Accepts `--codebase-type <type>` and `--overwrite`. Not for re-initializing only memory — use `/memory-init` instead.

Use this skill when a local container won't start, a service is unreachable from the host, a local docker-compose stack is misbehaving, or as the Docker-layer diagnosis step of a local bugfix flow — including when the user describes the symptom without naming Docker — to run a Docker-specific local diagnostic that collects container status, logs, networking, and resource usage and diagnoses issues, applying the SRE or DevOps role for investigation. For multi-scope environment analysis (Docker + Kubernetes + CI runner + drift snapshot + optional auto-fix) use `/env-analyze` instead.

Use this skill when investigating a production incident, when an alert fires (latency spike, error rate, pod crashloop), when a customer-reported issue needs prod telemetry, or as the diagnosis step of an incident-response or production-bugfix flow — including when the user describes a prod symptom without asking to "analyze" — to analyze the production environment by collecting Kubernetes pod status, managed database health, logs, metrics, and networking and diagnosing issues, supporting GCP, Azure, and AWS via the `cloud-platforms` skill and applying the SRE or DevOps role.

Use this skill when the user asks to analyze, investigate, assess, evaluate, compare, or research a codebase, architecture, system, product, market, or research topic in a structured way — including requests that need explicit scope, framework-driven reasoning, and separated facts/inference/recommendation but do not use the word "analyze" — to run a deep analysis workflow producing traceable scope, evidence, and conclusions. Not for quick one-off lookups or implementation work — this is an investigation-and-conclusions workflow.

Use this skill when a service, module, or system needs documentation, audit, or tech-debt assessment — onboarding a new team to an undocumented service, establishing a pre-redesign "what do we actually have?" baseline, or a tech-debt audit before roadmap planning, including when the user does not say "architecture" — to analyze the existing architecture and produce ARCHITECTURE.md, current-state C4 diagrams, a component inventory, gap analysis (current vs target), and a technical-debt register. Routes to system-architect (default — system/module/service analysis), cloud-architect (`--cloud` for cloud-resource inventory + cost + compliance review), or devops-architect (`--cicd` for CI/CD platform audit + DORA baseline + governance review).

22 hooks across 14 events

Terse, change-focused output style for /develop PR descriptions. Forces summary-first, bullet-listed changes, minimal narrative. Use when generating PRs from /develop or /create-pr workflows where reviewers need to scan quickly.

Structured Markdown output style with consistent heading hierarchy for /feature-design artefacts (PRD, ARCHITECTURE, UX-FLOW, DATA-MODEL, RISKS, IMPLEMENTATION-PLAN, REVIEW-LOG). Use when emitting design pack files to <repo>/docs/features/<feature-id>/.

Cross-platform docker-compose health monitor (Python). Watches service state and surfaces changes as Claude notifications (one JSON line per change to stdout). Opt-in via userConfig.env_watch_enabled (default false); polling interval via userConfig.env_watch_interval (default 15s, min 5s). Exits silently when disabled, when no docker-compose file is in cwd, or when the docker CLI is unavailable. Honors SIGINT (all platforms) and SIGTERM (POSIX). Replaces the legacy bash-only env-watch.sh which is kept as a deprecated shim.