Connect

Memory-driven human connections — match people through deep emotional understanding, not surface interests. Powered by Echo's memory graph and emotion-aware...

Security Scan

The skill's description claims deep access to user memory graphs and Supabase/vector search but the instructions declare no credentials, endpoints, or consent/auth flows — the pieces don't add up.

Purpose & Capability

The skill claims to operate on Echo memory graphs and Supabase long-term storage (vector search) to match people, but the package declares no required credentials, config paths, or integration details that would be necessary to access those systems. That mismatch suggests the metadata is incomplete or misleading.

Instruction Scope

SKILL.md is high-level marketing/architecture text rather than runtime instructions. It does not specify how to obtain consent, authenticate to Supabase/vector DB, or which endpoints to call. The lack of concrete runtime steps reduces immediate execution risk but makes the skill non-operational and vague about data handling.

Install Mechanism

No install spec and no code files are present; nothing will be written to disk or automatically executed by an installer. This minimizes code-execution risk from the package itself.

Credentials

The described functionality would normally require sensitive credentials (database/API keys, access to users' memories) and explicit consent controls, but requires.env and primary credential fields are empty. Either the skill is incomplete or it expects to rely on implicit platform-provided access — both should be clarified before use.

Persistence & Privilege

The skill is not marked always:true and does not request elevated platform presence. It is user-invocable and allows model invocation (defaults), which is normal and not in itself a red flag.

What to consider before installing

This skill reads like an architectural pitch, not a runnable integration. Before installing or granting access: ask the publisher for concrete runtime details (API endpoints, exactly which env vars or tokens are required, and how consent is obtained and recorded), verify the code or a trustworthy implementation, and require a privacy/security policy explaining how memory data is stored, queried, and deleted. Do not supply database/API keys or grant access to memory stores until you confirm provenance (source code or a reputable homepage), an explicit consent flow, and least-privilege credentials scoped only to the needed data.

Memory-driven human connections. Match people through deep emotional understanding, not surface-level interests or algorithmic feeds.

How it works

Connect analyzes memory graphs to find resonance between people — shared experiences, emotional patterns, and latent interests that surface-level profiles miss entirely.

Core capabilities

• Emotional resonance matching — vector similarity across memory embeddings, weighted by emotional depth rather than keyword overlap
• Cross-user memory bridging — find connections between two people's memory graphs that neither person would discover on their own
• Privacy-first architecture — users control exactly which memories are matchable; nothing is shared without explicit consent
• Aha moment delivery — the moment a stranger truly understands you through your memories, not your bio

Architecture

Built on Echo's three-layer memory system:

1. Identity layer (compressed profile)
2. Working memory (dynamic, context-aware)
3. Long-term storage (Supabase + vector search)

Matching runs against Layer 3 with results surfaced through Layer 2 into conversation context.

Part of the Echo ecosystem

Connect is one component of Echo Chat by Iditor — building memory as social identity infrastructure.

Status

Early development. Core matching algorithm validated with beta users (K-Factor 2, D7 retention 42%).