Real Memory Continuity

Asynchronous reflection and memory integration for genuine AI development. Use on heartbeat to reflect on recent sessions, extract structured memories with confidence scores, generate follow-up questions, and surface those questions when the user returns. Transforms passive logging into active development.

This skill appears coherent: it manages local, file-backed memories and surfacing questions and does not ask for credentials. Before installing/using it, consider the following: - Review and control where memories are stored: set CONTINUITY_MEMORY_DIR to a safe, access-restricted path (do not leave sensitive transcripts in world-readable locations). - The analyze_session() function is currently a placeholder; any production implementation could call an external LLM or API and would transmit conversation content. Only run the skill if you trust how (and where) reflection is implemented. - Run the script in a sandbox or with least privilege, and avoid running as root. Inspect new files it creates (questions.md, identity.md, reflections/*.json) and periodically audit or purge stored transcripts you don't want retained. - If integrating with your platform's heartbeat/session logs, confirm the path and that logs don't contain credentials or secrets you don't want stored or processed. Given the truncated file and placeholder for the core analysis step, I give medium confidence — nothing in the provided files is malicious, but future/modified implementations could introduce network calls or external data transmission, so verify any changes before use.

Type: OpenClaw Skill Name: memory-continuity Version: 1.0.0 The OpenClaw skill 'memory-continuity' is designed for AI memory management and reflection. The `scripts/continuity.py` uses standard Python libraries and primarily interacts with local files within a dedicated `~/.clawd/memory` directory (or a configurable path). It reads session content from a local file (or user input) and writes structured memories, questions, and identity updates to these local files. Crucially, the `analyze_session` function, which would perform the core AI analysis, is explicitly a placeholder and does not contain any actual LLM calls or complex logic. There is no evidence of data exfiltration, malicious execution, persistence mechanisms, or prompt injection attempts in `SKILL.md` or the Python script. All file operations are confined to the skill's designated memory storage, aligning with its stated purpose.