emoclaw

name	emoclaw
description	Give your AI emotions that grow from its own memories. Emoclaw builds a unique emotional state that shifts with every conversation, decays between sessions, and evolves over time through self-calibration. Train it on your agent's identity files and watch it develop its own emotional fingerprint.
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Emotion Skill

Emotions for AI agents — built from memories, shaped by relationships, always changing.

Emoclaw trains a lightweight model on your agent's identity and conversation history, producing a persistent emotional state that evolves with every interaction. Emotions decay naturally between sessions, shift based on who's talking and what's being said, and gradually recalibrate as your agent grows. The result is injected into the system prompt as an [EMOTIONAL STATE] block, giving your AI a felt sense of its own inner life.

How it works

1. Bootstrap — extract.py reads your agent's identity/memory files. label.py scores each passage via the Claude API (opt-in). train builds a small neural net from those scores. One-time setup.
2. Encode — Each incoming message is turned into a 384-dim vector by a frozen MiniLM sentence encoder. No fine-tuning, no network call — runs from a local cache.
3. Feel — The encoding + context (who's talking, what channel, previous emotion) flows through a GRU and MLP head, outputting an N-dimensional emotion vector (0-1 per dimension). The GRU hidden state persists across sessions — this is the "emotional residue" that carries forward mood.
4. Decay — Between sessions, each dimension drifts back toward its baseline at a configurable half-life (fast for arousal, slow for safety/groundedness). Time apart = cooling off.