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 natea/fitfinder --skill agentdb-advancedgit clone --depth 1 https://github.com/natea/fitfinderWrote 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/natea/fitfinder/agentdb-advanced)<a href="https://agentmods.dev/skills/natea/fitfinder/agentdb-advanced"><img src="https://agentmods.dev/badge/skills/natea/fitfinder/agentdb-advanced/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/natea/fitfinder/agentdb-advanced"><img src="https://agentmods.dev/badge/skills/natea/fitfinder/agentdb-advanced.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 | $0.00049 | $0.03390 |
| Opus 5 | $0.00024 | $0.01695 |
| Sonnet 5 | $0.00010 | $0.00678 |
| Haiku 4.5 | $0.00005 | $0.00339 |
Grade B, and why
AgentDB Advanced Features scanned grade B with 1 finding 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 5d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo ufw allow 4433/udp This is a copy
100% identical to AgentDB Advanced Features — 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 — 551 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentDB Advanced Features
What This Skill Does
Covers advanced AgentDB capabilities for distributed systems, multi-database coordination, custom distance metrics, hybrid search (vector + metadata), QUIC synchronization, and production deployment patterns. Enables building sophisticated AI systems with sub-millisecond cross-node communication and advanced search capabilities.
Performance: <1ms QUIC sync, hybrid search with filters, custom distance metrics.
Prerequisites
- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow)
- Understanding of distributed systems (for QUIC sync)
- Vector search fundamentals
QUIC Synchronization
What is QUIC Sync?
QUIC (Quick UDP Internet Connections) enables sub-millisecond latency synchronization between AgentDB instances across network boundaries with automatic retry, multiplexing, and encryption.
Benefits:
- <1ms latency between nodes
- Multiplexed streams (multiple operations simultaneously)
- Built-in encryption (TLS 1.3)
- Automatic retry and recovery
- Event-based broadcasting
Enable QUIC Sync
import { createAgentDBAdapter } from 'agentic-flow/reasoningbank';
// Initialize with QUIC synchronization
const adapter = await createAgentDBAdapter({
dbPath: '.agentdb/distributed.db',
enableQUICSync: true,
syncPort: 4433,
syncPeers: [
'192.168.1.10:4433',
'192.168.1.11:4433',
'192.168.1.12:4433',
],
});
// Patterns automatically sync across all peers
await adapter.insertPattern({
// ... pattern data
});
// Available on all peers within ~1ms
QUIC Configuration
const adapter = await createAgentDBAdapter({
enableQUICSync: true,
syncPort: 4433, // QUIC server port
syncPeers: ['host1:4433'], // Peer addresses
syncInterval: 1000, // Sync interval (ms)
syncBatchSize: 100, // Patterns per batch
maxRetries: 3, // Retry failed syncs
compression: true, // Enable compression
});
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.
- 5d ago First seen · 551 lines · 49 tokens per session scan B e5524686b1e9
AgentDB Advanced Features is a skill published in the GitHub repository natea/fitfinder (4 stars, last pushed 10mo ago), licensed MIT. It adds 49 tokens to every session and 3,390 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). It is 100% identical to AgentDB Advanced Features, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
9router-embeddings
Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
azure-search-documents-dotnet
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET"…
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.