Memory-Trace

Problem Solved

Turning scattered character settings, scripts, and reference materials into a stable role package is often blocked by inconsistent personality descriptions, tone boundaries, memory fragments, and audio/visual assets. memory-trace breaks “replicating a character” into material processing, persona modeling, memory extraction, and SoulPod generation, so the output can feed directly into the Memory-Inhabit role-chat workflow.

How It Works

• Material processing: extracts character information from supplied assets and supports PDF text inputs via pdfplumber.
• Persona modeling: builds relationship positioning, personality tendencies, speaking style, and capability boundaries, such as “older brother/lover” or “mentor/teasing peer.”
• Memory extraction: records key plot points, dialogue tendencies, and symbolic imagery in memories/raw_memories.json for later retrieval.
• SoulPod generation: emits profile.json, system_prompts.txt, config.json, prompt/universal_prompt.txt, prompt/story_baseline.txt, and optional assets/images/ and assets/audio/.
• Deployment handoff: the package can be placed under inhabit/personas/<role>/ for role-based conversation.

Scope and Caveats

• It focuses on producing role knowledge packages, not on building a general long-term memory system; usable character materials or parseable documents are required.
• For legacy roles, trace/output/<role>/ and inhabit/personas/<role>/ are usually the authoritative sources; regenerate trace/origin/<role>/ only when re-creating from raw materials.
• Voice recommendations are inferred and written to config.json, then should be validated against the actual TTS output.