How do I install the Django DB Side Computation skill?

Run npx skillmds add thedixitjain/django-db-side-computation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Django DB Side Computation safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Django DB Side Computation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Django DB Side Computation free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Django DB Side Computation?

thedixitjain (@thedixitjain) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Django DB Side Computation by thedixitjain

Name: Django DB Side Computation
Rating: 4.2 (5 reviews)
Author: thedixitjain

Django DB Side Computation

Move Django performance-sensitive Python loops into database-side computation with annotations, aggregates, filtered aggregates, F expressions, functions, Subquery, OuterRef, Exists, Window expressions, db_default, and GeneratedField. Use when Django code counts, sums, filters, flags, ranks, or derives values in Python after fetching rows.

thedixitjain b70c1db 2 files · 5.0 KB Updated Sep 8, 2026 2 repo stars

File contents

Django DB-Side Computation

Use this skill when Django fetches rows into Python to compute values the database can compute more efficiently or more consistently.

Workflow

Identify the Python computation.
- Counts, sums, min/max, booleans, latest related rows, rankings, denormalized totals, or per-row derived fields.
- Confirm whether the computed value must be exact, current, and transactionally consistent.
Pick the SQL expression level.
- filter(), exclude(), and F() for simple comparisons and updates.
- annotate() and aggregates for per-object counts and totals.
- Filtered aggregates for conditional counts and sums.
- Exists() for yes/no related-row checks.
- Subquery() with OuterRef() for latest or scalar related values.
- Window() for ranks and running calculations.
- GeneratedField for deterministic same-row computed columns when the database supports it.
Validate query shape.
- Inspect generated SQL or QuerySet.explain().
- Clear unintended ordering before grouping when needed.
- Confirm indexes support joins, filters, and ordering.
Decide whether the result should be stored.
- Use annotations for request-time values.
- Use generated fields for same-row deterministic values.
- Use materialized views or denormalized tables when cross-row aggregate queries are too expensive and can be stale.

See aggregation-patterns.md for Django expression examples.

Safety Notes

Database computation is not always faster; complex queries can overload the database or block OLTP work.
Generated fields have database-specific restrictions and PostgreSQL supports only persisted generated columns.
Subqueries and annotations can duplicate work if composed carelessly. Read the plan.

Verification

Compare correctness against the old Python calculation on representative data, then measure SQL time and total response time.

Source: hashgraph-online/awesome-codex-plugins → plugins/LVTD-LLC/skills/skills/django-db-side-computation/SKILL.md

---name: django-db-side-computationdescription: Move Django performance-sensitive Python loops into database-side computation with annotations, aggregates, filtered aggregates, F expressions, functions, Subquery, OuterRef, Exists, Window expressions, db_default, and GeneratedField. Use when Django code counts, sums, filters, flags, ranks, or derives values in Python after fetching rows.---# Django DB-Side ComputationUse this skill when Django fetches rows into Python to compute values the database can compute more efficiently or more consistently.## Workflow1. Identify the Python computation. - Counts, sums, min/max, booleans, latest related rows, rankings, denormalized totals, or per-row derived fields. - Confirm whether the computed value must be exact, current, and transactionally consistent.2. Pick the SQL expression level. - `filter()`, `exclude()`, and `F()` for simple comparisons and updates. - `annotate()` and aggregates for per-object counts and totals. - Filtered aggregates for conditional counts and sums. - `Exists()` for yes/no related-row checks. - `Subquery()` with `OuterRef()` for latest or scalar related values. - `Window()` for ranks and running calculations. - `GeneratedField` for deterministic same-row computed columns when the database supports it.3. Validate query shape. - Inspect generated SQL or `QuerySet.explain()`. - Clear unintended ordering before grouping when needed. - Confirm indexes support joins, filters, and ordering.4. Decide whether the result should be stored. - Use annotations for request-time values. - Use generated fields for same-row deterministic values. - Use materialized views or denormalized tables when cross-row aggregate queries are too expensive and can be stale.See [aggregation-patterns.md](references/aggregation-patterns.md) for Django expression examples.## Safety Notes- Database computation is not always faster; complex queries can overload the database or block OLTP work.- Generated fields have database-specific restrictions and PostgreSQL supports only persisted generated columns.- Subqueries and annotations can duplicate work if composed carelessly. Read the plan.## VerificationCompare correctness against the old Python calculation on representative data, then measure SQL time and total response time.---**Source:** [`hashgraph-online/awesome-codex-plugins`](https://github.com/hashgraph-online/awesome-codex-plugins) → `plugins/LVTD-LLC/skills/skills/django-db-side-computation/SKILL.md`

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