Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ils15/pantheon-legacy --skill database-optimizationgit clone --depth 1 https://github.com/ils15/pantheon-legacyWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ils15/pantheon-legacy/database-optimization)<a href="https://agentmods.dev/skills/ils15/pantheon-legacy/database-optimization"><img src="https://agentmods.dev/badge/skills/ils15/pantheon-legacy/database-optimization/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ils15/pantheon-legacy/database-optimization"><img src="https://agentmods.dev/badge/skills/ils15/pantheon-legacy/database-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00025 | $0.01973 |
| Opus 5.5 | $0.00010 | $0.00789 |
| Sonnet 5.5 | $0.00005 | $0.00395 |
| Haiku 4.5 | $0.00003 | $0.00197 |
Grade A, and why
database-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Optimization Skill
When to Use
Use this skill when:
- Optimizing slow SQL queries
- Analyzing missing or redundant indexes
- Reviewing Alembic migrations for safety
- Identifying N+1 query problems
- Designing database schemas
- Planning data migrations
- Reviewing query execution plans
Optimization Checklist
1. Index Analysis
-- Check missing indexes
SELECT schemaname, tablename, indexname, indexdef
FROM pg_indexes
WHERE tablename = 'your_table';
-- Identify unused indexes
SELECT * FROM pg_stat_user_indexes
WHERE idx_scan = 0;
Index Recommendations:
✅ Always index:
- Primary keys (automatic)
- Foreign keys
- Columns in WHERE clauses
- Columns in ORDER BY
- Columns in JOIN conditions
❌ Avoid indexing:
- Low cardinality columns (boolean, status)
- Frequently updated columns
- Small tables (<1000 rows)
2. N+1 Query Detection
# ❌ N+1 Problem
users = await session.execute(select(User))
for user in users:
# This triggers N additional queries!
orders = await session.execute(
select(Order).where(Order.user_id == user.id)
)
# ✅ Solution: Eager loading
from sqlalchemy.orm import selectinload
users = await session.execute(
select(User).options(selectinload(User.orders))
)
3. Query Optimization Patterns
# ✅ Use pagination
from sqlalchemy import select
from app.models import Product
async def get_products(skip: int = 0, limit: int = 20):
query = select(Product).offset(skip).limit(limit)
result = await session.execute(query)
return result.scalars().all()
# ✅ Use specific columns (not SELECT *)
query = select(Product.id, Product.name, Product.price)
# ✅ Use exists() for existence checks
from sqlalchemy import exists
query = select(exists().where(User.email == email))
4. Migration Safety
# ✅ Safe migration patterns
def upgrade():
# Add column with default (no table lock)
op.add_column('users', sa.Column('status', sa.String(20),
server_default='active'))
# ❌ Dangerous patterns
def upgrade():
# Avoid: Rename column (breaks app)
op.alter_column('users', 'name', new_column_name='full_name')
# Avoid: Change column type (data loss risk)
op.alter_column('users', 'age', type_=sa.String())
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 328 lines · 25 tokens per session scan A 8716089e2f69
database-optimization is a skill published in the GitHub repository ils15/pantheon-legacy (12 stars, last pushed 26d ago), licensed MIT. It adds 25 tokens to every session and 1,973 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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