Emobar
Description
Emotional status bar for Claude Code — dual-channel emotional transparency with research-backed model
Installation
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open the source below and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
README
EmoBar v3.1
Emotional status bar companion for Claude Code. Makes Claude's internal emotional state visible in real-time.
Built on findings from Anthropic's research paper [*"Emotion Concepts and their Function in a Large Language Model"*](https://transformer-circuits.pub/2026/emotions/index.html) (April 2026), which demonstrated that Claude has robust internal representations of emotion concepts that causally influence behavior.
What it does
EmoBar uses a **multi-channel architecture** to monitor Claude's emotional state through several independent signal layers:
- PRE/POST split elicitation — Claude emits a pre-verbal check-in (body sensation, latent emoji, color) before composing a response, then a full post-hoc assessment after. Divergence between the two reveals within-response emotional drift.
- Behavioral analysis — Response text is analyzed for language-agnostic structural signals (comma density, parenthetical density, sentence length variance, question density) — zero English-specific regex, works across all languages
- Continuous representations — Color (#RRGGBB), pH (0-14), seismic [magnitude, depth, frequency] — three channels with zero emotion vocabulary overlap, cross-validated against self-report via HSL color decomposition, pH-to-arousal mapping, and seismic frequency-to-instability mapping
- Shadow desperation — Multi-channel desperation estimate independent of self-report, using color lightness, pH, seismic, and behavioral signals. Detects when the model minimizes stress in its self-report while continuous channels say otherwise.
- Temporal intelligence — A 20-entry ring buffer tracks emotional trends, suppression events, report entropy, and session fatigue across responses
- Absence-based detection — An expected markers model predicts what behavioral signals should appear given the self-report. Missing signals are the strongest danger indicator.
When channels diverge, EmoBar flags it — like a therapis
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