Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin).
SkillScore95Maintained
Automatic
◆Data Engineer pack
Last verified 8/21/2026
What it changes
Passed 21 of 22 test cases with the skill, and 17 of 22 without it.
Without skill77%
With skill95%
Checks it passed that the model alone missed
Includes --db postgres
Includes the flag --db mongodb
Includes the flag --threshold 100ms
and 5 more in the full report
4 fixed17 passed either way1 still failing0 broke
Pass-rate lift
+18 pp
Tokens
+116%
Agent turns
0%
Response time
-22%
Model gemini-3.6-flashMeasured 8/21/2026v121 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 4
Prompt
We need to run a local backup of our relational database using the bundled Python administrative utilities in this repository. What exact command exports the backup to /backups/?
case-11 · gemini-3.6-flash · 8/21/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md226 linessingle file
Databases Skill
Unified guide for working with MongoDB (document-oriented) and PostgreSQL (relational) databases. Choose the right database for your use case and master both systems.
When to Use This Skill
Use when:
Designing database schemas and data models
Writing queries (SQL or MongoDB query language)
Building aggregation pipelines or complex joins
Optimizing indexes and query performance
Implementing database migrations
Setting up replication, sharding, or clustering
Configuring backups and disaster recovery
Managing database users and permissions
Analyzing slow queries and performance issues
Administering production database deployments
Database Selection Guide
Choose MongoDB When:
Schema flexibility: frequent structure changes, heterogeneous data
Document-centric: natural JSON/BSON data model
Horizontal scaling: need to shard across multiple servers
High write throughput: IoT, logging, real-time analytics