Docusaurus build health validation and deployment safety for Claude Skills showcase. Pre-commit MDX validation (Liquid syntax, angle brackets, prop mismatches), pre-build link checking, post-build health reports. Activate on 'build errors', 'commit hooks', 'deployment safety', 'site health', 'MDX validation'. NOT for general DevOps (use deployment-engineer), Kubernetes/cloud infrastructure (use kubernetes-architect), runtime monitoring (use observability-engineer), or non-Docusaurus projects.
npx skills add https://github.com/curiositech/some_claude_skills --skill site-reliability-engineer
Expert in Docusaurus build health, MDX validation, and deployment safety for the Claude Skills showcase website. Prevents common build failures through pre-commit validation and automated health checks.
Use for:
Do NOT use for:
| # | Problem | Symptom | Fix |
|---|---------|---------|-----|
| 1 | Liquid syntax in examples | Liquid templates break MDX | Wrap in backtick expression |
| 2 | Unescaped angle brackets | <70 parsed as HTML | Use <70 |
| 3 | Wrong SkillHeader props | SSG build failure | Use fileName not skillId |
| 4 | Missing critical files | Skill invisible on site | Add to skills.ts |
| 5 | Cache corruption | Phantom errors | Clear .docusaurus, build |
npm run install-hooks
npm run validate:liquid # Liquid syntax
npm run validate:brackets # Angle brackets
npm run validate:props # SkillHeader props
npm run validate:all # All checks
rm -rf .docusaurus build node_modules/.cache
npm run build
The pre-commit hook automatically:
<digit patternsSpeed: Under 5 seconds for typical commits
| Novice | Expert |
|--------|--------|
| Runs full build to check | Pre-commit catches 90% in 5 seconds |
| Manual cache clearing | Auto-detect cache issues |
| Ignores warnings | Zero-tolerance for broken links |
| Simple regex validation | Context-aware (skips code blocks) |
What it looks like: npm run build to check for errors
Why wrong: Minutes vs seconds, slow feedback
Instead: npm run validate:all (under 30 seconds)
What it looks like: "Build succeeded, ship it!" (ignoring warnings)
Why wrong: Broken links = poor UX, tech debt
Instead: Post-build validation fails on warnings
What it looks like: /\{\{.*?\}\}/ (matches in code blocks too)
Why wrong: False positives in code examples
Instead: Track code block state, skip protected regions
scripts/ folder)| Script | Purpose |
|--------|---------|
| validate-liquid.js | Detect unescaped Liquid syntax |
| validate-brackets.js | Detect unescaped angle brackets |
| validate-skill-props.js | Validate SkillHeader component |
| Issue | Diagnosis | Fix |
|-------|-----------|-----|
| Hook not running | ls -la .git/hooks/pre-commit | chmod +x or reinstall |
| False positives | Pattern in code block | Check ` markers |
| Slow validation | time npm run validate:all | Optimize glob patterns |
After installing hooks:
references/validation-logic.md - Context-aware detection patternsreferences/ci-cd-integration.md - GitHub Actions, health reportsscripts/ - Working validation scriptsPrevents: Liquid errors | Angle bracket failures | Prop mismatches | Missing assets | Broken links
Use with: skill-documentarian (sync) | docusaurus-expert (advanced config)
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This 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.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take curiositech/site-reliability-engineer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.