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 qbr-prepgit 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/qbr-prep)<a href="https://agentmods.dev/skills/zoominfo/zoominfo-mcp-plugin/qbr-prep"><img src="https://agentmods.dev/badge/skills/zoominfo/zoominfo-mcp-plugin/qbr-prep/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/qbr-prep"><img src="https://agentmods.dev/badge/skills/zoominfo/zoominfo-mcp-plugin/qbr-prep.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 | $0.00147 | $0.00889 |
| Opus 5 | $0.00073 | $0.00445 |
| Sonnet 5 | $0.00029 | $0.00178 |
| Haiku 4.5 | $0.00015 | $0.00089 |
Grade A, and why
qbr-prep 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QBR Prep Pack
Everything to walk into a quarterly business review: what value landed, what the customer has been raising, where the risks are, and what to cover.
Prerequisites
account_research and conversation_intelligence consume AI credits (CI is a minimum ~9 per call), and this skill runs account_research plus CI once per key engagement, so it can be credit-heavy. browse_engagements (to find the calls) is free but requires an active calendar/meeting integration; conversation_intelligence requires at least one connected meeting or email source. If no conversation data exists, build the pack from account_research and flag that value moments and themes could not be drawn from conversations, pointing the user to their ZoomInfo admin.
Input
Provided via $ARGUMENTS:
- Account (required) — ZoomInfo company ID (preferred), or a name/domain to resolve via
search_companies. - Review scope (optional) — the period being reviewed and the QBR's goal (renewal runway, expansion, adoption push). Shapes what to foreground; ask if it materially changes the pack and was not provided.
Workflow
- Resolve the account. If no account was supplied, ask the user which one before proceeding. Use the ZoomInfo ID directly, or resolve a name/domain via
search_companies. - Snapshot the account. Run
account_researchfor the deal/relationship picture, stakeholders, and recent firmographic/news context. This frames the review. - Read recent calls. Call
browse_engagements(account-scoped,engagementType: MEETINGS,sort: -chronological) and pick the few most relevant recent calls (typically the last 2-4 — each gets its own CI call, which costs credits). Runconversation_intelligencescoped to each engagement ID (one call per engagement) for value moments the customer voiced, usage and adoption themes they raised, risks and concerns, and open items. Keep each query to its single engagement; CI sees only the last few engagements and cannot topic-search or count. - Assemble the pack. Synthesize into a review-ready structure. Ground value moments and risks in specific conversations or CRM context with sources; do not assert outcomes the evidence does not support. Note that usage/adoption themes here come from what was discussed, not from product telemetry (ZoomInfo does not have product-usage data).
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 · 45 lines · 147 tokens per session scan A 102e5f32e9d8
qbr-prep is a skill published in the GitHub repository Zoominfo/zoominfo-mcp-plugin (7 stars, last pushed 5d ago), licensed MIT. It adds 147 tokens to every session and 889 once invoked, about $0.0007 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…