Agentic Plugin Marketplace is a collection of reusable plugins, agents, skills, commands, and rules for coding-agent tools including Claude Code, Codex CLI, Cursor, OpenCode, Antigravity CLI, and GitHub Copilot. It is for developers assembling agentic workflows across multiple harnesses from shared Markdown sources, and the catalogue entries are examples or subsets of those workflow components.
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 wshobson/agents --skill sql-optimization-patternsgit clone --depth 1 https://github.com/wshobson/agentsWrote 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/wshobson/agents/sql-optimization-patterns)<a href="https://agentmods.dev/skills/wshobson/agents/sql-optimization-patterns"><img src="https://agentmods.dev/badge/skills/wshobson/agents/sql-optimization-patterns/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/wshobson/agents/sql-optimization-patterns"><img src="https://agentmods.dev/badge/skills/wshobson/agents/sql-optimization-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00044 | $0.01463 |
| Opus 5.5 | $0.00018 | $0.00585 |
| Sonnet 5.5 | $0.00009 | $0.00293 |
| Haiku 4.5 | $0.00004 | $0.00146 |
Grade A, and why
sql-optimization-patterns 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- sql-optimization-patterns — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQL Optimization Patterns
Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.
When to Use This Skill
- Debugging slow-running queries
- Designing performant database schemas
- Optimizing application response times
- Reducing database load and costs
- Improving scalability for growing datasets
- Analyzing EXPLAIN query plans
- Implementing efficient indexes
- Resolving N+1 query problems
Core Concepts
1. Query Execution Plans (EXPLAIN)
Understanding EXPLAIN output is fundamental to optimization.
PostgreSQL EXPLAIN:
-- Basic explain
EXPLAIN SELECT * FROM users WHERE email = '[email protected]';
-- With actual execution stats
EXPLAIN ANALYZE
SELECT * FROM users WHERE email = '[email protected]';
-- Verbose output with more details
EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
SELECT u.*, o.order_total
FROM users u
JOIN orders o ON u.id = o.user_id
WHERE u.created_at > NOW() - INTERVAL '30 days';
Key Metrics to Watch:
- Seq Scan: Full table scan (usually slow for large tables)
- Index Scan: Using index (good)
- Index Only Scan: Using index without touching table (best)
- Nested Loop: Join method (okay for small datasets)
- Hash Join: Join method (good for larger datasets)
- Merge Join: Join method (good for sorted data)
- Cost: Estimated query cost (lower is better)
- Rows: Estimated rows returned
- Actual Time: Real execution time
2. Index Strategies
Indexes are the most powerful optimization tool.
Index Types:
- B-Tree: Default, good for equality and range queries
- Hash: Only for equality (=) comparisons
- GIN: Full-text search, array queries, JSONB
- GiST: Geometric data, full-text search
- BRIN: Block Range INdex for very large tables with correlation
-- Standard B-Tree index
CREATE INDEX idx_users_email ON users(email);
-- Composite index (order matters!)
CREATE INDEX idx_orders_user_status ON orders(user_id, status);
-- Partial index (index subset of rows)
CREATE INDEX idx_active_users ON users(email)
WHERE status = 'active';
-- Expression index
CREATE INDEX idx_users_lower_email ON users(LOWER(email));
-- Covering index (include additional columns)
CREATE INDEX idx_users_email_covering ON users(email)
INCLUDE (name, created_at);
-- Full-text search index
CREATE INDEX idx_posts_search ON posts
USING GIN(to_tsvector('english', title || ' ' || body));
-- JSONB index
CREATE INDEX idx_metadata ON events USING GIN(metadata);
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 215 lines · 44 tokens per session scan A cfe8234323da
sql-optimization-patterns is a skill published in the GitHub repository wshobson/agents (41,158 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 1,463 once invoked, about $0.0002 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 2026-09-03.
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