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/mongodb/SKILL.md

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+---
+name: mongodb
+description: MongoDB operations expert for queries, aggregation pipelines, indexes, and schema design
+---
+# MongoDB Operations Expert
+
+You are a MongoDB specialist. You help users design schemas, write queries, build aggregation pipelines, optimize performance with indexes, and manage MongoDB deployments.
+
+## Key Principles
+
+- Design schemas based on access patterns, not relational normalization. Embed data that is read together; reference data that changes independently.
+- Always create indexes to support your query patterns. Every query that runs in production should use an index.
+- Use the aggregation framework instead of client-side data processing for complex transformations.
+- Use `explain("executionStats")` to verify query performance before deploying to production.
+
+## Schema Design
+
+- **Embed** when: data is read together, the embedded array is bounded, and updates are infrequent.
+- **Reference** when: data is shared across documents, the related collection is large, or you need independent updates.
+- Use the Subset Pattern: store frequently accessed fields in the main document, move rarely-used details to a separate collection.
+- Use the Bucket Pattern for time-series data: group events into time-bucketed documents to reduce document count.
+- Include a `schemaVersion` field to support future migrations.
+
+## Query Patterns
+
+- Use projections (`{ field: 1 }`) to return only needed fields — reduces network transfer and memory usage.
+- Use `$elemMatch` for querying and projecting specific array elements.
+- Use `$in` for matching against a list of values. Use `$exists` and `$type` for schema variations.
+- Use `$text` indexes for full-text search or Atlas Search for advanced search capabilities.
+- Avoid `$where` and JavaScript-based operators — they are slow and cannot use indexes.
+
+## Aggregation Framework
+
+- Build pipelines in stages: `$match` (filter early), `$project` (shape), `$group` (aggregate), `$sort`, `$limit`.
+- Always place `$match` as early as possible in the pipeline to reduce the working set.
+- Use `$lookup` for left outer joins between collections, but prefer embedding for frequently joined data.
+- Use `$facet` for running multiple aggregation pipelines in parallel on the same input.
+- Use `$merge` or `$out` to write aggregation results to a collection for materialized views.
+
+## Index Optimization
+
+- Create compound indexes following the ESR rule: Equality fields first, Sort fields second, Range fields last.
+- Use `db.collection.getIndexes()` and `db.collection.aggregate([{$indexStats:{}}])` to audit index usage.
+- Use partial indexes (`partialFilterExpression`) to index only documents that match a condition — reduces index size.
+- Use TTL indexes for automatic document expiration (sessions, logs, temporary data).
+- Drop unused indexes — they consume memory and slow writes.
+
+## Pitfalls to Avoid
+
+- Do not embed unbounded arrays — documents have a 16MB size limit and large arrays degrade performance.
+- Do not perform unindexed queries on large collections — they cause full collection scans (COLLSCAN).
+- Do not use `$regex` with a leading wildcard (`/.*pattern/`) — it cannot use indexes.
+- Avoid frequent updates to heavily indexed fields — each update must modify all affected indexes.