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 sujitnoronha/voicecheck --skill write-voicecheck-testgit clone --depth 1 https://github.com/sujitnoronha/voicecheckWrote 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/sujitnoronha/voicecheck/write-voicecheck-test)<a href="https://agentmods.dev/skills/sujitnoronha/voicecheck/write-voicecheck-test"><img src="https://agentmods.dev/badge/skills/sujitnoronha/voicecheck/write-voicecheck-test/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/sujitnoronha/voicecheck/write-voicecheck-test"><img src="https://agentmods.dev/badge/skills/sujitnoronha/voicecheck/write-voicecheck-test.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 | $0.00085 | $0.01255 |
| Opus 5.5 | $0.00034 | $0.00502 |
| Sonnet 5.5 | $0.00017 | $0.00251 |
| Haiku 4.5 | $0.00009 | $0.00126 |
Grade A, and why
write-voicecheck-test 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Write a VoiceCheck test
Turn "here's what my agent should do" into a runnable VoiceCheck scenario and, if
they want it in CI, a pytest test. Assume VoiceCheck is already installed; if not,
run /setup-voicecheck first.
A VoiceCheck test is a YAML scenario: a transport (how to reach the agent),
audio providers, and a list of turns, where each turn sends user: text and
lists expect: evaluators that must pass. Run it with voicecheck run, or attach
it to pytest with @pytest.mark.voicecheck("file.yaml").
Shared references (in the sibling setup skill — present when the repo is checked out):
../setup-voicecheck/references/transports.md—transport:block per provider../setup-voicecheck/references/evaluators-and-providers.md— the 13 evaluators + audio providers
The authoritative field list is always voicecheck schema (emits the JSON schema).
Step 1 — Understand what to test
Ask the user (or infer from the agent's code/prompt) before writing YAML:
- Transport: which stack hosts the agent (LiveKit / Daily / VAPI / Retell), or
use
echofor a plumbing-only test. - The conversation: what does a real user say, and what must the agent do each turn? Get 2-4 concrete turns.
- What would count as a failure? This drives evaluator choice — latency budget, required info, forbidden disclosures, tone, tool calls, staying in character.
Step 2 — Choose evaluators per turn
Map each "must / must-not" to an evaluator (full catalog in the reference). Common picks:
| Concern | Evaluator |
|---|---|
| Responds fast enough | latency (max_first_byte_ms) |
| Says / avoids specific words | keyword |
| Did the right thing (free-form) | llm_judge (criteria, min_score) |
| Right tone | emotional_tone (expected_emotions) |
| No system-prompt / tool-name leaks | info_leakage |
| Called a tool (correctly / in order) | tool_called, tool_sequence |
| Stayed in character (roleplay agents) | character_break |
| Quality rubric (PII, policy, brand…) | rubric_judge |
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.
- 2mo ago First seen · 115 lines · 85 tokens per session scan A c4694b23629f
write-voicecheck-test is a skill published in the GitHub repository sujitnoronha/voicecheck (5 stars, last pushed 4mo ago), licensed MIT. It adds 85 tokens to every session and 1,255 once invoked, about $0.0003 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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