audio-calibration-engineer

audio-calibration-engineer is a skill for Codex from daredoole/audio-calibration-mcp. It costs 60 tokens per session (1,276 once invoked), scanned A, original, MIT.

A guided audio-calibration workflow for measuring and adjusting general speakers, powered speakers, car systems, and laptops. It uses REW, a tool for measuring room and audio response, plus optional JamesDSP processing.

In plain words
What is it for?
Use it to run REW measurements, assess sound by listening, create conservative equalizer settings, produce reports, and optionally configure JamesDSP. It does not cover A1 Evo or Denon-specific calibration transfers.
Why use it?
It keeps measurements, listening observations, backups, and approvals in the workflow instead of relying on guesses. It also separates what was observed from what was inferred.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit Use it to run REW measurements, assess sound by listening, create conservative equalizer settings, produce reports, and optionally configure JamesDSP. It does not cover A1 Evo or Denon-specific calibration transfers.

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Install with agentmods
npx agentmods add skills/daredoole/audio-calibration-mcp/audio-calibration-engineer
Install

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.

Any agent
npx skills add daredoole/audio-calibration-mcp --skill audio-calibration-engineer
Clone the repo
git clone --depth 1 https://github.com/daredoole/audio-calibration-mcp

Made for: Codex.

Wrote 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.

agentmods badge for audio-calibration-engineer

README.md
[![agentmods](https://agentmods.dev/badge/skills/daredoole/audio-calibration-mcp/audio-calibration-engineer/github.svg)](https://agentmods.dev/skills/daredoole/audio-calibration-mcp/audio-calibration-engineer)
Your own site
<a href="https://agentmods.dev/skills/daredoole/audio-calibration-mcp/audio-calibration-engineer"><img src="https://agentmods.dev/badge/skills/daredoole/audio-calibration-mcp/audio-calibration-engineer/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.

agentmods 80×15 button for audio-calibration-engineer

Your own site · 80×15
<a href="https://agentmods.dev/skills/daredoole/audio-calibration-mcp/audio-calibration-engineer"><img src="https://agentmods.dev/badge/skills/daredoole/audio-calibration-mcp/audio-calibration-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,276 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00060 $0.01276
Opus 5 $0.00030 $0.00638
Sonnet 5 $0.00012 $0.00255
Haiku 4.5 $0.00006 $0.00128

Measured 10d ago against content hash 2c14b8fb2b0e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

audio-calibration-engineer 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 10d 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.

skills/audio-calibration-engineer/SKILL.md · 40 lines

How it starts

The opening of the file, as written. The whole thing — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Audio Calibration Engineer

Use measured evidence and separate observations from interpretation. Never invent speaker specifications, microphone calibration, SPL accuracy, routing state, or expected improvement.

Workflow

  1. Start with audio_doctor. If the REW API is offline, call rew_install_discover; when no candidate is found, ask for an absolute executable path and pass it to rew_launch_plan. Start REW only through the matching confirmed rew_launch_execute, then run rew_capability_negotiate. Use audio_guided_session_plan for an end-to-end guided workflow; use individual tools in Expert mode. After each accepted guided stage, use audio_session_advance_plan and its confirmed executor so the session retains evidence, backups, and an explicit next-tool list. Inventory the host, REW, microphone calibration, output path, profiles, and existing measurements.
  2. Identify the device class. Read general-speakers.md, car-audio.md, or laptop.md as applicable.
  3. Preserve the current route, REW configuration, measurement file, and DSP preset before changes.
  4. Build a hash-bound plan. Immediately before audible output, obtain explicit confirmation that the microphone is placed, the area is clear, and the selected output is safe.
  5. Begin at the class-specific conservative level and frequency range. Require clipping and SPL abort guards. Stop on unexpected routing, silence, clipping, overload, or device distress.
  6. Save raw measurements before analysis. For reference work, use rew_repeated_session_plan so left, right, and combined outputs retain 4–6 separate traces and complete control/preset fingerprints. Run rew_measurement_quality; missing SNR is a rejection by default. Use its returned evidenceArtifact when advancing the guided quality-gate stage. Reject clipping, route/DSP drift, incomplete traces, poor repeatability, or inadequate SNR before interpreting sound.
  7. Use rew_dual_resolution_analysis, rew_direct_late_analysis, and rew_human_listening_assessment for distinct engineering, perceptual, direct/late, tonal, channel-match, extension, crossover, decay, distortion/compression, timing, and confidence dimensions. The human-listening assessment is asynchronous: poll audio_job_status, and cancel with explicit confirmation through audio_job_cancel. Never synthesize dimensions into an unsupported universal sound-quality score.
  8. For crossover changes, analyze magnitude and phase and verify with a measured combined trace. For output limits, use a protected level ladder and rew_compression_analysis.
  9. Prefer placement, polarity, delay, crossover, and stable cut-first EQ. Use audio_eq_design_plan for one role or audio_linked_stereo_eq_plan for independently validated left/right evidence. Gate correction through audio_speaker_protection_assessment. Do not boost narrow/spatial nulls or claim a visually flat trace is optimal.
  10. Apply only an exact confirmed DSP plan with backup and rollback, then re-measure at matched level. audio_post_eq_verification is asynchronous: poll audio_job_status, and cancel through audio_job_cancel. It calculates the before/after level difference from the traces; never substitute a claimed match. Predicted filter response alone never accepts a change. Export filters through a hash-bound export plan. Equalizer APO and CamillaDSP file adapters require explicit configured paths; other systems remain export-only until an adapter is proven.
  11. Use audio_listening_test_plan for level-matched randomized A/B or ABX validation. For JamesDSP, prefer jamesdsp_ab_plan, jamesdsp_ab_present_execute, and jamesdsp_ab_restore_execute so preset and host-volume changes are transactional. Use audio_report_plan comparison groups for averaged, uncertainty-banded before/after SVG evidence.

Read the full file on GitHub · 40 lines

Changes

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.

  1. 10d ago First seen · 40 lines · 60 tokens per session scan A 2c14b8fb2b0e

Subscribe to this mod's changes

audio-calibration-engineer is a skill published in the GitHub repository daredoole/audio-calibration-mcp (5 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 1,276 once invoked, about $0.0003 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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