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 netresearch/composer-agent-skill-plugin --skill database-analyzer-skillgit clone --depth 1 https://github.com/netresearch/composer-agent-skill-pluginWrote 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/netresearch/composer-agent-skill-plugin/database-analyzer-skill)<a href="https://agentmods.dev/skills/netresearch/composer-agent-skill-plugin/database-analyzer-skill"><img src="https://agentmods.dev/badge/skills/netresearch/composer-agent-skill-plugin/database-analyzer-skill/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/netresearch/composer-agent-skill-plugin/database-analyzer-skill"><img src="https://agentmods.dev/badge/skills/netresearch/composer-agent-skill-plugin/database-analyzer-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00031 | $0.01119 |
| Opus 5.5 | $0.00012 | $0.00448 |
| Sonnet 5.5 | $0.00006 | $0.00224 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
database-analyzer 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Analyzer Skill
This skill helps you analyze database schemas, identify optimization opportunities, and understand table relationships.
Instructions
- Identify the target: Determine which table or schema to analyze
- Gather context: Understand the current usage patterns and performance concerns
- Analyze structure: Examine table definitions, indexes, and relationships
- Identify issues: Look for missing indexes, improper data types, or inefficient structures
- Suggest improvements: Provide specific, actionable recommendations
Examples
Example 1: Basic Table Analysis
User request: "Analyze the users table for optimization opportunities"
Approach:
- Check table structure and data types
- Verify indexes on frequently queried columns
- Look for redundant or missing indexes
- Suggest appropriate data types for columns
Analysis Steps:
-- 1. Get table structure
DESCRIBE users;
-- 2. Check existing indexes
SHOW INDEX FROM users;
-- 3. Analyze table statistics
ANALYZE TABLE users;
Common Issues to Check:
- Missing indexes on foreign keys
- Text columns that should be ENUM or SET
- Missing or excessive indexes
- Improper data types (e.g., VARCHAR when INT would suffice)
Example 2: Performance Investigation
User request: "Why are queries on the orders table slow?"
Approach:
- Identify frequently executed queries
- Check for missing indexes on WHERE/JOIN columns
- Analyze table size and growth patterns
- Suggest partitioning if appropriate
Investigation Steps:
-- 1. Check table size
SELECT
table_name,
round(((data_length + index_length) / 1024 / 1024), 2) AS 'Size (MB)'
FROM information_schema.TABLES
WHERE table_name = 'orders';
-- 2. Identify slow queries
SHOW PROCESSLIST;
-- 3. Check query execution plan
EXPLAIN SELECT * FROM orders WHERE customer_id = 123;
Optimization Recommendations:
- Add composite indexes for common query patterns
- Consider partitioning by date for large historical tables
- Archive old data to separate tables
- Optimize data types to reduce row size
What ships with it
2 files 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 · 176 lines · 31 tokens per session scan A 4e43dee2e5d5
database-analyzer is a skill published in the GitHub repository netresearch/composer-agent-skill-plugin (11 stars, last pushed 26d ago), licensed MIT. It adds 31 tokens to every session and 1,119 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.
Other skills, from other repositories
laravel-eloquent
Eloquent and query-layer engineering rules for Laravel — eliminating N+1, choosing a pagination strategy, short atomic transactions, casts and scopes on the model, where raw SQL is allowed, and how migrations declare the schema those queries depend on. Use when writing or reviewing Eloquent models, migrations, query…
alloydb-basics
Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.
cloud-databases-onboarding
Guides users through discovering their database requirements, recommends a Google Cloud database based on a recommendation matrix, and assists in database creation. Use when a user asks 'What database service should I use?', 'Help me pick a database', or when a user wants to create a new database on Google Cloud.…
obsidian-bases
Create and edit Obsidian Bases (.base files) with views, filters, formulas, and summaries. Use when working with .base files, creating database-like views of notes, or when the user mentions Bases, table views, card views, filters, or formulas in Obsidian.
spanner-basics
Assists in provisioning instances and databases, designing performant schemas, and querying data in Cloud Spanner on Google Cloud. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.