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 Zoominfo/zoominfo-mcp-plugin --skill call-coachinggit clone --depth 1 https://github.com/Zoominfo/zoominfo-mcp-pluginWrote 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/zoominfo/zoominfo-mcp-plugin/call-coaching)<a href="https://agentmods.dev/skills/zoominfo/zoominfo-mcp-plugin/call-coaching"><img src="https://agentmods.dev/badge/skills/zoominfo/zoominfo-mcp-plugin/call-coaching/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/zoominfo/zoominfo-mcp-plugin/call-coaching"><img src="https://agentmods.dev/badge/skills/zoominfo/zoominfo-mcp-plugin/call-coaching.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 | $0.00114 | $0.00754 |
| Opus 5 | $0.00057 | $0.00377 |
| Sonnet 5 | $0.00023 | $0.00151 |
| Haiku 4.5 | $0.00011 | $0.00075 |
Grade A, and why
call-coaching 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 10d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Call Coaching
Give a rep specific, actionable feedback on a single call: what worked, what to improve, and what to focus on next time.
Prerequisites
browse_engagements (to find the call) is free but requires an active calendar/meeting integration; conversation_intelligence (to analyze it) requires at least one connected meeting source and consumes AI credits. If the call has no transcript to analyze, say so rather than coaching from assumption.
Input
Provided via $ARGUMENTS:
- Which call (optional) — an account and/or date. If omitted, the skill identifies the most likely recent call and confirms before analyzing.
- Focus (optional) — e.g. "discovery", "the pricing objection", "did I talk too much". Targets the coaching.
Workflow
- Identify the call.
browse_engagementsfilters by date and by company/contact ID, not by call name, so resolve any named account or contact first viasearch_companies/search_contacts, then callbrowse_engagements(engagementType: MEETINGS,sort: -chronological) scoped to that ID and date window and pick the matching meeting from the results. If nothing was named, callbrowse_engagementsfor the user's recent meetings, propose the most likely candidate, and confirm it before spending credits. If more than one meeting plausibly matches, ask. Keep the chosen engagement ID. - Analyze how it went. Run
conversation_intelligencescoped to that engagement ID. Ask how the rep handled discovery (did they uncover pain, budget, timeline, decision process), how objections were handled, what the customer's reactions and sentiment were, and where the conversation stalled or advanced. Keep the query scoped to this one call. - Coach against good practice. Assess the call against solid discovery and objection-handling fundamentals — open questions over pitching, listening over talking, surfacing next steps, addressing concerns directly. Ground every point in a specific moment from the call; cite what was said. Be candid and useful, not generic. If the transcript is too thin to judge something, say so rather than inventing a critique.
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.
- 10d ago First seen · 42 lines · 114 tokens per session scan A 569174bb2abc
call-coaching is a skill published in the GitHub repository Zoominfo/zoominfo-mcp-plugin (7 stars, last pushed 6d ago), licensed MIT. It adds 114 tokens to every session and 754 once invoked, about $0.0006 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 2026-08-30.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
miniapp
Build a tiny interactive HTML playground only when someone asks to see, play with, or step through a mechanism.
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.
master-yinguang
A reference-based assistant for questions about Yinguang and Pure Land Buddhism, a Buddhist tradition focused on faith, ethical living, and practice connected with rebirth in the Pure Land. It can answer in Yinguang’s historical teaching style.