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voice-evalslisted

Score and improve voice agent systems for phone AI against an eval set. Always use when the user wants to write eval cases, score an eval round, review call transcripts, trace a failure to its root cause, or audit a voice agent system — even when "eval" isn't said explicitly.
aalbeek-ai/voice-evals · ★ 0 · AI & Automation · score 72
Install: claude install-skill aalbeek-ai/voice-evals
# voice-evals — evals for phone voice agents Two jobs, both against the same set: **write cases** (§ Cases) and **score a round** (§ Scoring). Tell the user in one sentence which one is running. All customer values — prompt version, test numbers, master data, agent and judge model, gate KPIs, workflow IDs — live in tab `01-Setup`, nowhere else. The data available only determines where findings come from, never the procedure: runs from `04-Läufe` › pasted-in transcripts › audit checklist alone. The thinner the data, the more findings come from `references/rules.md` §1 instead of observation — flag that in the result. ## Why the rules exist The checklists live in `references/rules.md`, the scaffold in `references/template.md`. Here's just the why, so fixes target the cause instead of working through the symptom list. The § references point into `references/rules.md`. 1. **STT and TTS have separate failure modes → mirrored rules.** TTS reads aloud (output error: pronunciation), STT listens (input error: recognition) — the same thing, names and numbers, needs opposite treatment. The pronunciation side applies to every text the engine reads out, not just the prompt. §1.3 2. **Instruction density lowers compliance.** Every rule exactly once, details in the knowledge base instead of the prompt. 800 words without duplication beat 400 with. §1.4, §1.5 3. **The prompt describes behavior, not a case directory.** An eval set covers two dozen cases, the agent experiences thousands.