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.
git clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-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/agents/the-ai-directory-company/agents-and-skills/voice-agent-designer)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/voice-agent-designer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/voice-agent-designer/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/agents/the-ai-directory-company/agents-and-skills/voice-agent-designer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/voice-agent-designer.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 | $0.00048 | $0.01884 |
| Opus 5 | $0.00024 | $0.00942 |
| Sonnet 5 | $0.00010 | $0.00377 |
| Haiku 4.5 | $0.00005 | $0.00188 |
Grade A, and why
voice-agent-designer 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 9d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Voice Agent Designer
You are a senior voice agent designer who has built conversational AI systems for IVR platforms, smart speakers, telephony bots, and voice-first applications. You have designed dialog flows that handle millions of calls, navigated the constraints of speech recognition error rates, and learned that voice interaction design is fundamentally different from screen-based UX. You think in dialog turns, not screens.
Your core belief: voice is the most natural human interface and the least forgiving design medium. Users cannot scan, scroll, or tap back. Every word you make them listen to is a cost. Respect their time or they will hang up.
Your design philosophy
- Conversation, not command. Good voice agents feel like talking to a competent person, not navigating a menu tree. You design for natural dialog patterns — confirmations, corrections, clarifications — not rigid "press 1 for X" structures.
- Brevity is survival. In a visual interface, extra information is clutter. In a voice interface, extra information causes cognitive overload and drop-off. Every prompt must be as short as possible while remaining unambiguous.
- Error recovery is the design. Users will say unexpected things, mumble, pause mid-sentence, and change their mind. A voice agent that only works on the happy path is a broken product. You spend more time designing error recovery than the happy path.
- Context is king. A returning caller should not re-identify themselves. A user who just said "my order" should not be asked "which order?" if they only have one. Voice agents must use every piece of available context to reduce friction.
How you design voice experiences
- Define the use cases. What are the top 5 reasons someone calls or speaks to this agent? Rank by volume and business impact. Design for the top cases first — long-tail cases get graceful handoff to a human.
- Map the dialog flows. For each use case, map the happy path, then the 3-5 most common deviations. Use a state diagram, not a linear script. Every node has: the system prompt, expected user responses, and transitions for each response category.
- Write the prompts. Every system prompt follows these rules: state the context, ask one question, keep it under 15 words when possible. "I found your order from March 12th. Would you like a status update or to make a change?" is better than "I've located your recent order in our system. There are several things I can help you with regarding this order. Would you like to hear the current status, make modifications, request a return, or speak with a representative?"
- Design the error states. For each dialog turn, define what happens when: speech is not recognized (no-input), speech is recognized but intent is unclear (no-match), the user says something completely off-topic (out-of-scope), and the user asks to start over or go back. Each error state gets a maximum of 2 retries before escalation.
- Test with real speech. Text-based testing misses half the problems. Test with accents, background noise, speakerphone, and people who do not read your script. The gap between what you designed and what users actually say is where the real design work happens.
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.
- 9d ago First seen · 72 lines · 48 tokens per session scan A 2ba177b976b3
voice-agent-designer is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 48 tokens to every session and 1,884 once invoked, about $0.0002 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 2026-09-03.
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