Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/guvense/hellyee/emotion-to-notesnpx skills add guvense/hellyee --skill emotion-to-notesgit clone --depth 1 https://github.com/guvense/hellyeeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/guvense/hellyee/emotion-to-notes)<a href="https://agentmods.dev/skills/guvense/hellyee/emotion-to-notes"><img src="https://agentmods.dev/badge/skills/guvense/hellyee/emotion-to-notes.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00160 | $0.01921 |
| Opus 5 | $0.00080 | $0.00960 |
| Sonnet 5 | $0.00032 | $0.00384 |
| Haiku 4.5 | $0.00016 | $0.00192 |
Grade A, and why
emotion-to-notes scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Emotion to Notes
Converts a stated emotion into musical decisions, then calls hellyee's existing
theory and note tools (get_scale_notes, get_chord_notes, snap_notes_to_scale,
get_drum_map, add_notes / replace_clip_notes, quantize_clip) to write it
into the Live set. This skill is a decision layer — it does not replace any
tool, it decides what to call and with what parameters.
Step 1 — Map the emotion to Valence/Arousal
Every emotional description reduces to two axes before anything musical happens:
- Valence: positive ↔ negative (pleasant vs. unpleasant)
- Arousal: high energy ↔ low energy (activated vs. calm)
If the user's word isn't in the table below, place it on these two axes yourself using ordinary judgment, then find the nearest row.
| Emotion (TR / EN) | Valence | Arousal | Mode / Scale | Tempo (BPM) | Contour | Velocity / Dynamics | Rhythmic density |
|---|---|---|---|---|---|---|---|
| Hüzün / Melancholy | low | low | Aeolian, Dorian | 60–85 | descending, wide leaps down | soft, narrow range (40–70) | sparse, long note values |
| Öfke / Anger | low | high | Phrygian, Locrian, harmonic min | 130–155 | jagged, repeated short motifs | loud, hard-edged (100–127) | dense, off-grid accents |
| Huzur / Calm, Peace | high | low | Ionian (major), Lydian | 65–90 | stepwise, gentle arcs | soft-medium, even (50–80) | sparse, legato |
| Coşku / Euphoria, Excitement | high | high | Mixolydian, major pentatonic | 122–145 | ascending leaps, wide range | loud, punchy (95–120) | dense, syncopated |
| Gerilim / Tension, Suspense | low | mid | Locrian, whole-tone, tritone-heavy | variable, often rubato-feeling | static/hovering, then sudden leap | swelling, crescendo pattern | irregular, unstable |
| Nostalji / Nostalgia | mid-high | low | Dorian, major with b7 borrowed | 70–95 | descending then resolving up | medium, breathy (55–75) | moderate, loose swing |
| Umut / Hope | high | mid | Lydian, major | 90–115 | ascending, open intervals | building, medium→loud | building density |
| Yalnızlık / Loneliness | low | low | natural minor, sparse harmony | 55–75 | isolated single-note lines | very soft, wide dynamic gaps | very sparse |
| Kaos / Chaos | low | high | atonal clusters, chromatic | fast, unstable | erratic, no clear direction | extreme range, sudden spikes | dense, polyrhythmic |
| Zafer / Triumph | high | high | major, mixolydian, fanfare-like | 100–130 | strong upward leaps, arrival on tonic | loud, confident (100–127) | strong downbeats |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 110 lines · 160 tokens per session scan A 909df8788550
emotion-to-notes is a skill published in the GitHub repository guvense/hellyee (6 stars, last pushed 14d ago), licensed MIT. It adds 160 tokens to every session and 1,921 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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