PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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/mohitagw15856/pm-claude-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/rules/mohitagw15856/pm-claude-skills/voice-agent-design)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/voice-agent-design"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/voice-agent-design/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/rules/mohitagw15856/pm-claude-skills/voice-agent-design"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/voice-agent-design.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 | $0.00095 | $0.01361 |
| Opus 5 | $0.00048 | $0.00681 |
| Sonnet 5 | $0.00019 | $0.00272 |
| Haiku 4.5 | $0.00010 | $0.00136 |
Grade A, and why
voice-agent-design 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 8d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Voice Agent Design Skill
Voice is the least forgiving agent surface: no screen to fall back on, dead air reads as failure within two seconds, and the caller is often already annoyed. This skill designs voice agents around the medium's real constraints — turn-taking, interruption, repair — instead of shipping a chatbot with a text-to-speech voice.
What This Skill Produces
- A scope decision: which call intents the agent owns end-to-end, which it triages, which go straight to humans
- A conversation architecture: openings, turn design, confirmation strategy, repair loops
- Barge-in, silence, and error behaviour — the mechanics that decide whether it feels alive or infuriating
- Human-handoff rules with context transfer, and a launch scorecard
Required Inputs
Ask for (if not already provided):
- The line and its traffic: what people call about (top intents with rough volumes), current handle times
- What the agent may actually do — which systems it can read/write, what it can promise
- The escalation reality: human hours, queue lengths, what happens after-hours
- Compliance context: recording consent, disclosure requirements, regulated statements in this domain
Design Method
- Scope by intent, ruthlessly. From the intent list, the agent owns only intents that are (a) high-volume, (b) completable with its actual system access, and (c) low-stakes-if-wrong. It triages everything it can identify but not complete. It immediately passes anything emotional, legal, or high-value — a furious caller is a human's job on the first turn, not after three failed bot turns.
- Disclose and set the frame in the first five seconds. The agent says it's an AI (increasingly required by law; always required by trust), what it can do, and how to reach a human ("say 'agent' anytime"). Hiding the escape hatch inflates containment metrics and rage in equal measure.
- Design turns for ears, not eyes. One question per turn · ≤2 sentences before yielding · numbers and options in threes at most ("I can do A, B, or C — which one?") · never read a paragraph. Anything long ("your options are…") gets offered as SMS/email instead of spoken.
- Engineer the mechanics that make it feel alive:
- Barge-in on: the caller can interrupt any utterance; the agent stops mid-sentence and processes.
- Latency masked: acknowledge within ~1s ("let me check that…") whenever a lookup exceeds it; dead air past 2s is where trust dies.
- Confirmation proportional to stakes: implicit for low stakes ("okay, Tuesday…"), explicit read-back for money, addresses, and anything irreversible.
- Repair, not repeat: on a misunderstanding, change strategy — rephrase, offer options, or fall to keypad — never re-ask the same question the same way twice.
- Make the handoff a feature. Triggers: caller asks (always, instantly) · two failed repairs on one slot · negative-emotion cues · any regulated topic. The transfer carries a whisper summary (who, what they want, what's been tried, account pulled up) — the caller never repeats themselves; that single property beats every other quality bar in perceived experience.
- Score what callers feel, not what dashboards flatter. Containment alone is gameable (trap callers and containment "improves"). The scorecard pairs it with: task success as the caller defines it (post-call yes/no), escapes-requested rate, repair rate, silent-transfer rate, and hang-ups mid-flow. Set launch gates on the pairs.
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.
- 8d ago First seen · 80 lines · 95 tokens per session scan A 9cf444ce95a9
voice-agent-design is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 95 tokens to every session and 1,361 once invoked, about $0.0005 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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