Borrowing it
Nothing to install: this file belongs to guvense/hellyee. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/guvense/hellyee/main/.claude/skills/mastering-targets/SKILL.mdgit 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/mastering-targets)<a href="https://agentmods.dev/skills/guvense/hellyee/mastering-targets"><img src="https://agentmods.dev/badge/skills/guvense/hellyee/mastering-targets/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/guvense/hellyee/mastering-targets"><img src="https://agentmods.dev/badge/skills/guvense/hellyee/mastering-targets.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00123 | $0.01243 |
| Opus 5.5 | $0.00049 | $0.00497 |
| Sonnet 5.5 | $0.00025 | $0.00249 |
| Haiku 4.5 | $0.00012 | $0.00124 |
Grade A, and why
mastering-targets 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 2mo 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mastering Targets
The honest measurement contract
Streaming platforms measure LUFS (integrated loudness) and true peak. hellyee cannot read either — Live's API exposes only the master meter (0–1 scale, 0.85 ≈ 0 dB, ~10 Hz, sustained level not true peak). So:
- Work in meter targets (below), calibrated from real sessions. They are approximations, and say so when reporting.
- If the user has a loudness meter plugin (Youlean, ADPTR) on their master, its readout is invisible to the API — ask the user to read the LUFS number aloud and adjust from that. That combination (their eyes + your control) is the accurate path.
- True-peak overshoot between meter samples is invisible: keep the limiter ceiling conservative (values below) rather than trusting the meter.
Chain and gain staging
Order on the master: EQ Eight → Glue Compressor → Limiter (limiter always last). If the user's set already has a master chain, work with it — never stack a second limiter.
- Trim all track faders proportionally until the loudest section's master peak reads ~0.75 pre-limiter (preserves the mix balance).
- EQ: high-pass ≤25 Hz; fix real problems only (a −1 dB mud cut near 500 Hz, a gentle +1–2 dB high shelf for air). Mastering EQ moves are small.
- Glue: ratio 2:1, slow attack (transients pass), threshold for ~1–3 dB of glue, auto release.
- Limiter: raise Gain stepwise, measuring the loudest 8 bars each step, until the target below is reached.
Targets by destination
| Destination | Industry target | hellyee meter target (drop, ~8 bars) | Limiter ceiling |
|---|---|---|---|
| Spotify | −14 LUFS-I, −1 dBTP | peak ≈ 0.83–0.85, mean ≈ 0.80–0.83 | −1.0 dB |
| Apple Music | −16 LUFS-I, −1 dBTP | peak ≈ 0.81–0.83, mean ≈ 0.78–0.81 | −1.0 dB |
| YouTube | −14 LUFS-I | as Spotify | −1.0 dB |
| SoundCloud / no normalization | loudness wins (−8…−11) | peak ≈ 0.87–0.89 | −0.3 dB |
| Club / DJ WAV | −6…−9 LUFS-I | peak ≈ 0.88–0.91, mean ≈ 0.86+ | −0.3 dB |
| Ambient / dynamic material | −16…−18 | peak ≈ 0.75–0.78 | −1.0 dB |
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.
- 2mo ago First seen · 87 lines · 123 tokens per session scan A 3c1b5ac431a0
mastering-targets is a skill published in the GitHub repository guvense/hellyee (9 stars, last pushed 2mo ago), licensed MIT. It adds 123 tokens to every session and 1,243 once invoked, about $0.0005 per session on Opus 5.5. 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.
mastering-targets compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| mastering-targetsguvense/hellyee | Skill | 9 | 2mo ago | MIT | A | 123 | Claude Code |
| dv-overviewmicrosoft/Dataverse-skills | Skill | 254 | yesterday | MIT | A | 98 | Claude Code |
| solr-extendinggriddynamics/rosetta | Skill | 370 | yesterday | Apache-2.0 | A | 23 | Claude Code, Codex |
| dv-connectmicrosoft/Dataverse-skills | Skill | 254 | yesterday | MIT | B | 66 | Claude Code |
| servicenow-cmdbKnuckles-Team/servicenow-api | Skill | 20 | yesterday | MIT | A | 130 | Claude Code, Codex |
| servicenow-table-apiKnuckles-Team/servicenow-api | Skill | 20 | yesterday | MIT | A | 180 | Claude Code, Codex |
| vector-backend-operationsKnuckles-Team/vector-mcp | Skill | 16 | 2d ago | MIT | A | 113 | Claude Code, Codex |