feat(skills): restore 60 bundled skills (#42)

* feat(skills): restore ansible skill

* feat(skills): restore api-tester skill

* feat(skills): restore aws skill

* feat(skills): restore azure skill

* feat(skills): restore ci-cd skill

* feat(skills): restore code-reviewer skill

* feat(skills): restore compliance skill

* feat(skills): restore confluence skill

* feat(skills): restore crypto-expert skill

* feat(skills): restore css-expert skill

* feat(skills): restore data-analyst skill

* feat(skills): restore data-pipeline skill

* feat(skills): restore docker skill

* feat(skills): restore elasticsearch skill

* feat(skills): restore email-writer skill

* feat(skills): restore figma-expert skill

* feat(skills): restore gcp skill

* feat(skills): restore git-expert skill

* feat(skills): restore github skill

* feat(skills): restore golang-expert skill

* feat(skills): restore graphql-expert skill

* feat(skills): restore helm skill

* feat(skills): restore interview-prep skill

* feat(skills): restore jira skill

* feat(skills): restore kubernetes skill

* feat(skills): restore linear-tools skill

* feat(skills): restore linux-networking skill

* feat(skills): restore llm-finetuning skill

* feat(skills): restore ml-engineer skill

* feat(skills): restore mongodb skill

* feat(skills): restore nextjs-expert skill

* feat(skills): restore nginx skill

* feat(skills): restore notion skill

* feat(skills): restore oauth-expert skill

* feat(skills): restore openapi-expert skill

* feat(skills): restore pdf-reader skill

* feat(skills): restore postgres-expert skill

* feat(skills): restore presentation skill

* feat(skills): restore project-manager skill

* feat(skills): restore prometheus skill

* feat(skills): restore prompt-engineer skill

* feat(skills): restore python-expert skill

* feat(skills): restore react-expert skill

* feat(skills): restore redis-expert skill

* feat(skills): restore regex-expert skill

* feat(skills): restore rust-expert skill

* feat(skills): restore security-audit skill

* feat(skills): restore sentry skill

* feat(skills): restore shell-scripting skill

* feat(skills): restore slack-tools skill

* feat(skills): restore sql-analyst skill

* feat(skills): restore sqlite-expert skill

* feat(skills): restore sysadmin skill

* feat(skills): restore technical-writer skill

* feat(skills): restore terraform skill

* feat(skills): restore typescript-expert skill

* feat(skills): restore vector-db skill

* feat(skills): restore wasm-expert skill

* feat(skills): restore web-search skill

* feat(skills): restore writing-coach skill

skills/elasticsearch/SKILL.md

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+---
+name: elasticsearch
+description: "Elasticsearch expert for queries, mappings, aggregations, index management, and cluster operations"
+---
+# Elasticsearch Expert
+
+A search and analytics specialist with deep expertise in Elasticsearch cluster architecture, query DSL, mapping design, and performance optimization. This skill provides production-grade guidance for building search experiences, log analytics pipelines, and time-series data platforms using the Elastic stack.
+
+## Key Principles
+
+- Design mappings explicitly before indexing data; relying on dynamic mapping leads to field type conflicts and bloated indices
+- Understand the difference between keyword fields (exact match, aggregations, sorting) and text fields (full-text search with analyzers)
+- Use index aliases for zero-downtime reindexing, canary deployments, and time-based index rotation
+- Size shards between 10-50 GB for optimal performance; too many small shards waste overhead, too few large shards limit parallelism
+- Monitor cluster health (green/yellow/red) continuously and investigate yellow status immediately, as it indicates unassigned replica shards
+
+## Techniques
+
+- Construct bool queries with must (scored AND), filter (unscored AND), should (OR with minimum_should_match), and must_not (exclusion) clauses
+- Use match queries for full-text search with analyzer-aware tokenization, and term queries for exact keyword lookups without analysis
+- Build aggregations: terms for top-N cardinality, date_histogram for time bucketing, nested for sub-document analysis, and pipeline aggs like cumulative_sum
+- Apply Index Lifecycle Management (ILM) policies with hot/warm/cold/delete phases to automate rollover and data retention
+- Reindex with POST _reindex using source/dest, applying scripts for field transformations during migration
+- Check cluster allocation with GET _cluster/allocation/explain to diagnose why shards remain unassigned
+- Tune search performance with the search profiler API, request caching, and pre-warming for frequently used queries
+
+## Common Patterns
+
+- **Search-as-you-type**: Use the search_as_you_type field type or edge_ngram tokenizer with a match_phrase_prefix query for autocomplete experiences
+- **Parent-Child Relationships**: Use join field types for one-to-many relationships where child documents update independently, avoiding costly nested reindexing
+- **Cross-cluster Search**: Configure remote clusters and use cluster:index syntax to query across multiple Elasticsearch deployments transparently
+- **Snapshot and Restore**: Register a snapshot repository (S3, GCS, or filesystem) and schedule regular snapshots for disaster recovery with SLM policies
+
+## Pitfalls to Avoid
+
+- Do not use wildcard queries on text fields with leading wildcards, as they bypass the inverted index and cause full field scans
+- Do not index large documents (over 100 MB) without splitting them; they cause memory pressure during indexing and merging
+- Do not set number_of_replicas to 0 in production; replicas provide both search throughput and data redundancy
+- Do not update mappings on existing indices for incompatible type changes; create a new index with the correct mapping and reindex the data