Skill: cliftonwknox/elite-human-memory (version 1.0.1)

--- name: elite-human-memory description: Portable Clawhub-compatible human-like memory system with layered storage (Working/Episodic/Semantic + optional Vector index), rich metadata schema, auto-promotion heuristics, conflict detection & resolution, and semantic retrieval. Platform-agnostic design suitable for publishing and multi-agent use. --- # Elite Human Memory — Portable Edition This version prioritizes broad compatibility and portability. It preserves the human-like, selective, and contextual philosophy while avoiding tight coupling to any single agent framework. Ideal for Clawhub, custom agents, or cross-platform deployments. ## Memory Layers **Working Memory** Current conversation only. Transient and not persisted beyond the session. **Episodic Memory** Daily raw memory files stored at: `memory/daily/YYYY-MM-DD.md` (or user-configurable path) **Semantic Memory** Curated long-term memory stored in: `memory/MEMORY.md` **Vector Index** (Optional / Portable) Embeddings generated from `MEMORY.md` and recent episodic files. Can be implemented with any vector database (Chroma, FAISS, LanceDB, or even a simple embedding cache). Stored in: `memory/vectors/` or external vector store. This enables semantic search alongside traditional metadata filtering. If no vector capability is available, fall back to keyword + metadata search. **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, Discord, 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 - **Why**: Trigger or reason it was recorded - **State**: `active` | `stale` | `superseded` | `resolved` - **Scope**: `global` | `project` | `person` | `temporary` - **Validity**: - `confidence`: high / medium / low - `last_verified`: date -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 or reference (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 (or embedding similarity) over `MEMORY.md` and recent daily files for relevant memories. 2. **Metadata Filtering** (Secondary) Apply filters on scope, state, confidence, date, and related entities. 3. **Keyword / Full-text Fallback** Traditional search when vector capabilities are unavailable. 4. **Response Guidelines** - Use natural confidence language - Mention if a memory may be stale or conflicting - Include `Source:` references when helpful ## Platform Integration Notes This skill is designed to be **framework-agnostic**: - Use file system operations for storage and retrieval - Integrate with any available embedding model or vector store for semantic search - Can coexist with simpler key-value memory systems (map rich contextual memory here, simple facts elsewhere) - Works in Hermes, Clawhub-published agents, LangChain, AutoGen, or custom loops ## 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 via cron/agent loop) ## Storage Layout (Example) ``` memory/ ├── daily/ │ └── YYYY-MM-DD.md # Episodic / daily memory ├── conflicts/ │ └── YYYY-MM-DD.md # Conflict logs and resolutions ├── MEMORY.md # Curated long-term semantic memory └── vectors/ # Optional embeddings (or use external DB) ``` Paths are examples — make them configurable for the target environment. ## Notes - This skill is optimized for portability and easy publishing on Clawhub. - Vector search (when available) significantly improves retrieval quality. - Auto-promotion and conflict detection reduce manual maintenance burden while keeping the user in control. - No hard dependencies on proprietary tools or specific runtimes. See `references/two-track-approach.md` for the parallel Hermes-optimized vs portable versioning strategy used in this skill family. --- **Version:** 1.0.0 (Portable / Clawhub Compatible) **Status:** Ready for publishing and cross-platform use


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