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 skills add AkshitIreddy/agent-skills --skill audio-verificationgit clone --depth 1 https://github.com/AkshitIreddy/agent-skillsWrote 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/akshitireddy/agent-skills/audio-verification)<a href="https://agentmods.dev/skills/akshitireddy/agent-skills/audio-verification"><img src="https://agentmods.dev/badge/skills/akshitireddy/agent-skills/audio-verification/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/akshitireddy/agent-skills/audio-verification"><img src="https://agentmods.dev/badge/skills/akshitireddy/agent-skills/audio-verification.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 | $0.00020 | $0.01283 |
| Opus 5.5 | $0.00008 | $0.00513 |
| Sonnet 5.5 | $0.00004 | $0.00257 |
| Haiku 4.5 | $0.00002 | $0.00128 |
Grade A, and why
audio-verification 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 1mo 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audio verification
An audio graph that compiles can still be silent, over-range, noisy, or abruptly cut off. Render it to samples when that will answer the reported problem, then exercise the actual app path for scheduling, lifecycle, and device behavior.
Evidence boundary
Measurements can establish sample-level facts. They do not establish that a cue is pleasant, that a person heard it, or that the browser and output device played it correctly. Report those limits explicitly.
For a changed cue:
- Render the real graph offline when practical. Prefer importing the production module; if you reproduce the graph in a probe, disclose that it may drift.
- Measure the fields relevant to the symptom. Compare before and after under identical settings when a meaningful baseline exists.
- Exercise the cue in the running app when the issue involves user gestures, overlapping voices, scheduling, context state, or device changes.
- If the evidence cannot settle the complaint, report the result as a bounded measurement or non-reproduction and identify the needed listening check.
Running the included probe
scripts/audio-probe.js is a classic script with no
imports. Evaluate it in a browser page using an available browser automation,
preview, test runner, or developer-console facility. It defines
globalThis.AudioProbe.
const metrics = await AudioProbe.measure((ctx, destination) => {
const oscillator = ctx.createOscillator();
const gain = ctx.createGain();
gain.gain.setValueAtTime(0, 0);
gain.gain.linearRampToValueAtTime(0.3, 0.02);
gain.gain.exponentialRampToValueAtTime(0.0004, 0.4);
oscillator.connect(gain).connect(destination);
oscillator.start(0);
oscillator.stop(0.5);
}, { seconds: 0.6 });
const comparison = await AudioProbe.compare(
{ before: buildOld, after: buildNew },
{ seconds: 0.6 },
);
OfflineAudioContext requires a browser-compatible Web Audio implementation.
Use what the project or host already provides. Installing a browser engine or a
Web Audio shim solely for a small check is optional and should be proportionate
to the task.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed · -14 lines · -97 tokens per session bdb6ab732792
- 2mo ago First seen · 118 lines · 117 tokens per session scan A 42c31797856c
audio-verification is a skill published in the GitHub repository AkshitIreddy/agent-skills (1 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 1,283 once invoked, about $0.0001 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.
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