Elite Human Memory — Hermes Optimized

This version is tuned for Hermes. It keeps the human-like, selective, and contextual philosophy while taking advantage of Hermes’ tool ecosystem and vector capabilities.

Memory Layers

Working Memory

Current conversation only. Transient and not persisted.

Episodic Memory

Daily raw memory files stored at:

memory/YYYY-MM-DD.md

Semantic Memory

Curated long-term memory stored in:

MEMORY.md

Vector Index (Hermes-enhanced)

Embeddings generated from MEMORY.md and recent episodic files, stored in:

memory/vectors/

This enables semantic search alongside traditional metadata filtering.

Conflict Ledger

Detected contradictions are logged in:

memory/conflicts/

Context Schema (Metadata)

Every memory entry should include:

• When: Timestamp + recency weight
• Where: Channel/context (Telegram, CLI, web, etc.)
• Why: Trigger or reason it was recorded
• State: active | stale | superseded | resolved
• Scope: global | project | person | temporary
• Validity:
• confidence: high / medium / low
• last_verified: date
• expires: optional date
• Related: Links to other memories, people, or projects
• Source: Path + line number (for traceability)

Auto-Promotion Heuristics

The agent should evaluate daily memory entries for promotion using the following weighted signals:

Strong signals (high weight):

• Explicitly referenced by the user in later conversations
• Repeated across 3+ days or sessions
• Tied to a core project, goal, or person
• User corrects or reinforces the memory

Supporting signals (medium weight):

• High confidence rating
• Clear future utility
• Related to an active decision or preference

Promotion Rules:

• If 2+ strong signals → Propose promotion
• If 1 strong + 2 supporting signals → Propose promotion
• If only supporting signals → Log for weekly review only

User Control:

• Default behavior: Always propose before promoting
• Optional mode: auto_promote = true (for trusted, low-risk memories)

Conflict Detection & Resolution

When two memories contain contradictory information, the agent should:

1. Detect the conflict during write or weekly maintenance.
2. Log it in memory/conflicts/ with:
• Both conflicting statements
• Context and sources
• Severity (high / medium / low)
3. Resolve using one of these methods:
• Ask the user for clarification
• Propose a resolution with reasoning
• Auto-resolve low-severity conflicts with a note (e.g. “Temporarily preferred X over Y”)

All resolutions must update the State field of the affected memories and record the decision in the conflict log.

Retrieval Strategy

When the user asks about history, decisions, preferences, or past context, the agent should follow this order:

1. Semantic Search (Primary)

Query the vector index over MEMORY.md and recent daily files for relevant memories.

1. Metadata Filtering (Secondary)

Apply filters on scope, state, confidence, date, and related entities.

1. Hermes Memory Tool Integration

Also query the built-in Hermes memory tool to surface any simple key-value facts stored there.

1. Response Guidelines
• Use natural confidence language
• Mention if a memory may be stale or conflicting
• Include Source: references when helpful

Hermes Integration

This skill is designed to work alongside the existing Hermes memory tool:

• Use this system for rich, contextual, human-style memory
• Use the Hermes memory tool for simple, high-frequency key-value facts
• Both systems can reference each other when relevant

Behavioral Triggers

Auto-read memory when:

• User asks about past decisions, preferences, people, projects, or dates
• The current context feels incomplete or contradictory

Auto-write memory when:

• User gives explicit “remember this” instructions
• Clear decisions or repeated preferences appear
• New long-running context is established

Auto-maintenance:

• Weekly review (can be manually triggered or scheduled)

Storage Layout

memory/
├── YYYY-MM-DD.md              # Episodic / daily memory
├── conflicts/
│   └── YYYY-MM-DD.md          # Conflict logs and resolutions
├── MEMORY.md                  # Curated long-term semantic memory
└── vectors/                   # Embeddings for semantic search

Notes

• This skill is optimized for Hermes and makes full use of its tool ecosystem.
• Vector search significantly improves retrieval quality over pure keyword/metadata search.
• Auto-promotion and conflict detection reduce manual maintenance burden while keeping the user in control.

Version: 2.0.0 (Hermes Optimized)

Status: Ready for local use and marketplace publishing.