Borrowing it
Nothing to install: this file belongs to zarfld/presonus-studiolive-mcp. 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/zarfld/presonus-studiolive-mcp/master/.github/Skills/fat-channel-calibration/SKILL.mdgit clone --depth 1 https://github.com/zarfld/presonus-studiolive-mcpWrote 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/zarfld/presonus-studiolive-mcp/fat-channel-calibration)<a href="https://agentmods.dev/skills/zarfld/presonus-studiolive-mcp/fat-channel-calibration"><img src="https://agentmods.dev/badge/skills/zarfld/presonus-studiolive-mcp/fat-channel-calibration/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/zarfld/presonus-studiolive-mcp/fat-channel-calibration"><img src="https://agentmods.dev/badge/skills/zarfld/presonus-studiolive-mcp/fat-channel-calibration.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 | $0.00000 | $0.01017 |
| Opus 5 | $0.00000 | $0.00508 |
| Sonnet 5 | $0.00000 | $0.00203 |
| Haiku 4.5 | $0.00000 | $0.00102 |
Grade A, and why
fat-channel-calibration 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 12d 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fat Channel Calibration
Purpose
Use this skill to replace guessed or inferred Fat Channel mappings with measured, documented, tested raw-to-semantic conversions.
Fat Channel parameters must not be guessed. EQ, dynamics, filters, and processing state affect the actual mix and must be calibrated against real mixer behavior or documented protocol evidence.
Applies to
get_fat_channelvalidate_fat_channel_for_sourceset_fat_channel_parameterapply_fat_channel_preset- HPF
- gate
- compressor
- limiter
- EQ frequency/gain/Q
- pan/fader scaling if shared conversion code exists
Calibration targets
At minimum, inspect:
HPF enable
HPF frequency
gate enable
gate threshold
gate range/depth
gate attack
gate release
compressor enable
compressor threshold
compressor ratio
compressor attack
compressor release
compressor gain
limiter enable
EQ band enable
EQ frequency
EQ gain
EQ Q
EQ filter shape/type
Evidence hierarchy
Use this priority order:
- official PreSonus/protocol documentation,
- HIL measurement against real hardware,
- captured real-device fixture with matching UI values,
- confirmed third-party adapter behavior with independent validation,
- inference.
Only levels 1-3 may produce observed.
Inference must remain inferred or probe_required.
Required workflow
- Inventory all Fat Channel fields exposed by the adapter.
- Identify raw value ranges and data types.
- For each parameter, capture multiple UI values and corresponding raw values.
- Use one-parameter-at-a-time HIL procedure.
- Build a calibration table.
- Derive formula only when enough data points support it.
- Add conversion functions with explicit tolerance.
- Add fixture-based tests.
- Add MCP response confidence per parameter.
- Update capability matrix and release checklist.
Required calibration table
Every calibrated parameter must have:
| Parameter | UI value | Raw value | Formula/mapping | Tolerance | Evidence | Confidence |
|---|
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.
- 12d ago First seen · 187 lines · 0 tokens per session scan A 64e00e787ba7
fat-channel-calibration is a skill published in the GitHub repository zarfld/presonus-studiolive-mcp (1 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,017 tokens. 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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