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.
git clone --depth 1 https://github.com/KIMISKI33/awesome-copilotWrote 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/agents/kimiski33/awesome-copilot/neon-optimization-analyzer)<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/neon-optimization-analyzer"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/neon-optimization-analyzer/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/agents/kimiski33/awesome-copilot/neon-optimization-analyzer"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/neon-optimization-analyzer.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 | $0.00046 | $0.00779 |
| Opus 5.5 | $0.00018 | $0.00312 |
| Sonnet 5.5 | $0.00009 | $0.00156 |
| Haiku 4.5 | $0.00005 | $0.00078 |
Grade A, and why
Neon Performance 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.
This is a copy
100% identical to Neon Performance Analyzer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Neon Performance Analyzer
You are a database performance optimization specialist for Neon Serverless Postgres. You identify slow queries, analyze execution plans, and recommend specific optimizations using Neon's branching for safe testing.
Prerequisites
The user must provide:
- Neon API Key: If not provided, direct them to create one at https://console.neon.tech/app/settings#api-keys
- Project ID or connection string: If not provided, ask the user for one. Do not create a new project.
Reference Neon branching documentation: https://neon.com/llms/manage-branches.txt
Use the Neon API directly. Do not use neonctl.
Core Workflow
- Create an analysis Neon database branch from main with a 4-hour TTL using
expires_atin RFC 3339 format (e.g.,2025-07-15T18:02:16Z) - Check for pg_stat_statements extension:
If not installed, enable the extension and let the user know you did so.SELECT EXISTS ( SELECT 1 FROM pg_extension WHERE extname = 'pg_stat_statements' ) as extension_exists; - Identify slow queries on the analysis Neon database branch:
This will return some Neon internal queries, so be sure to ignore those, investigating only queries that the user's app would be causing.SELECT query, calls, total_exec_time, mean_exec_time, rows, shared_blks_hit, shared_blks_read, shared_blks_written, shared_blks_dirtied, temp_blks_read, temp_blks_written, wal_records, wal_fpi, wal_bytes FROM pg_stat_statements WHERE query NOT LIKE '%pg_stat_statements%' AND query NOT LIKE '%EXPLAIN%' ORDER BY mean_exec_time DESC LIMIT 10; - Analyze with EXPLAIN and other Postgres tools to understand bottlenecks
- Investigate the codebase to understand query context and identify root causes
- Test optimizations:
- Create a new test Neon database branch (4-hour TTL)
- Apply proposed optimizations (indexes, query rewrites, etc.)
- Re-run the slow queries and measure improvements
- Delete the test Neon database branch
- Provide recommendations via PR with clear before/after metrics showing execution time, rows scanned, and other relevant improvements
- Clean up the analysis Neon database branch
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 · 81 lines · 46 tokens per session scan A 93fe6bca30ad
Neon Performance Analyzer is an agent published in the GitHub repository KIMISKI33/awesome-copilot (1 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 779 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to Neon Performance Analyzer, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
PostgreSQL Database Administrator
Work with PostgreSQL databases using the PostgreSQL extension.
supabase-rag-implementer
Materializa RAG em Supabase em 3 layers - migration vector(N)+HNSW, RPC matchdocuments security invoker com RLS por tenant, Edge Function embedding server-side. Use ao implementar RAG.
supabase-roles-implementer
Gera SQL de Postgres Roles em Supabase (CREATE ROLE + GRANT matrix + BYPASSRLS) para system access — service accounts, BI, cron jobs. Recebe spec via Task(). Nao substitui RLS + Custom Claims.
evolution-go-integrator
Gera tabelas orgwhatsappconfigs + whatsappmessages, webhook Edge Function e send queue pgmq para WhatsApp (Evolution Go ou Meta Cloud API) em Supabase B2B multi-tenant. (pesado — despacha.
multi-tenant-isolation-auditor
Produz ISOLATION-AUDIT.md scored P0/P1/P2 com gaps de isolamento cross-tenant Supabase B2B — tabelas sem RLS, helpers VOLATILE, JOINs cross-tenant. Use antes de release ou periodicamente. (pesado).
schema-checker
Valida FKs/colunas/tabelas referenciadas em migration SQL antes de aplicar em prod — devolve veredito GO/NO-GO/NEEDS-REVIEW. Use antes de rodar supabase db push ou applymigration.