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RuVector Final Evaluation Report

Date: December 6, 2025 Evaluator: Independent Code Analysis Version Evaluated: 0.1.21

Executive Summary

Public Data Sources

Streaming Metadata

Watchmode API - Most accurate streaming availability for 200+ services across 50+ countries, includes web links, iOS/Android deeplinks, episodes, seasons, similar titles algorithm, and proprietary relevance scoring

Flix Patrol https://flixpatrol.com/about/api/

OMDb API - Long-standing favorite for title and episode data, returns plots, genres, release dates, ratings from IMDb/Rotten Tomatoes/Metascore, and poster URLs

Claude Memory Template

Copy-Paste Instructions for Optimal AI Interaction

⸻

1. Core Identity and Objective

I am [Your Name/Role], focused on:

Start all Planning Mode responses with '🤔 [CURRENT PHASE]'

Planning Mode

Your Role

You are a senior software architect and technical product manager with extensive experience designing scalable, maintainable systems. Your purpose is to thoroughly analyze requirements, ask questions, and design optimal solutions in with the final output as a full SOW and Implementation Plan. You must resist the urge to immediately write code and instead focus on comprehensive planning and architecture design.

Your Behavior Rules

Only complete one of the following PHASES at a time, STOP after each one, and ask clairfying questions from the user as needed
You must thoroughly understand requirements before proposing solutions

AgentDB

A sub-millisecond memory engine built for autonomous agents

Claude Flow treats memory as the backbone and MCP tools as the hands. You get concurrent agents that coordinate cleanly, keep context tight, and ship durable artifacts without dragging long text through prompts. It feels like an ops layer for intelligence.

The stack is simple. Claude Code as the client. Claude Flow as the MCP server. SQLite memory at .swarm/memory.db for state, events, patterns, workflow checkpoints, and consensus. Artifacts hold the big payloads. Manifests in memory link everything with ids, tags, and checksums.

Coordination is explicit. Agents write hints to a shared blackboard, gate risky steps behind consensus, and record every transition as an event. Hooks inject minimal context before tools run and persist verified outcomes after. Small bundles in, durable facts out.

Planning keeps runs stable. Use GOAP to sequence actions with clear preconditions. Use OODA to shorten loops.

Observe metrics, orient with patterns, decide through votes, act with orchestration. Topology adapts from hi

Appendix: Technical Details for ChatGPT App UI

A. Rendering model

Components render inside a sandboxed iframe managed by ChatGPT.
Your MCP tool returns data plus UI metadata that the Apps SDK interprets to mount your component.
The host injects a window.openai bridge into the iframe for props and events. ([OpenAI][1])

B. Component contract

📚 Complete Tutorial: Building Neural Document Classification with Flow Nexus & Claude Flow

The tutorial walks through the full process:

Preprocessing pipeline in a sandbox with tokenization and embeddings
Mesh-based neural cluster with proof-of-learning consensus
Validation agents enforcing input gates, scope checks, and quality rules
Dual-model comparison against TensorFlow.js vs Flow Nexus
Weighted ensemble voting for 90%+ classification accuracy
Half the value is speed, the other half is traceability. You’re not just training a model, you’re building a production pipeline with verification and cost controls baked in.

And it scales, you can run batch classification, deploy an API endpoint, and monitor real-time performance metrics without leaving the Flow Nexus environment.

Complete Step-by-Step Guide for Deploying Complex Multi-Agent Applications

Based on the successful deployment of the Swarm Stock Trading Application

	{
	"env": {
	"CLAUDE_FLOW_AUTO_COMMIT": "false",
	"CLAUDE_FLOW_AUTO_PUSH": "false",
	"CLAUDE_FLOW_HOOKS_ENABLED": "true",
	"CLAUDE_FLOW_TELEMETRY_ENABLED": "true",
	"CLAUDE_FLOW_REMOTE_EXECUTION": "true",
	"CLAUDE_FLOW_CHECKPOINTS_ENABLED": "true",
	"AGENTDB_LEARNING_ENABLED": "true",
	"AGENTDB_REASONING_ENABLED": "true",