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 mutual-action-plangit 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/mutual-action-plan)<a href="https://agentmods.dev/skills/zoominfo/zoominfo-mcp-plugin/mutual-action-plan"><img src="https://agentmods.dev/badge/skills/zoominfo/zoominfo-mcp-plugin/mutual-action-plan/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/mutual-action-plan"><img src="https://agentmods.dev/badge/skills/zoominfo/zoominfo-mcp-plugin/mutual-action-plan.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 | $0.00151 | $0.00914 |
| Opus 5 | $0.00076 | $0.00457 |
| Sonnet 5 | $0.00030 | $0.00183 |
| Haiku 4.5 | $0.00015 | $0.00091 |
Grade A, and why
mutual-action-plan 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 11d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mutual Action Plan
Turn the commitments and timelines from recent calls into a single shared plan: who does what, by when, on both sides, toward an agreed goal.
Prerequisites
browse_engagements (to find the calls) is free but requires an active calendar/meeting integration; conversation_intelligence (to read them) requires at least one connected meeting or email source and consumes AI credits (minimum ~9 per call), and this skill calls it once per key engagement, so the spend scales with how many calls you deep-read. account_research (optional deal context) consumes AI credits. If no conversation data exists, say so rather than inventing a plan.
Input
Provided via $ARGUMENTS:
- Account (required) — ZoomInfo company ID (preferred), or a name/domain to resolve via
search_companies. - Goal & target date (ask if it matters and was not given) — the close date, go-live, or objective the plan drives toward. Do not assume it; if the plan needs a date that was never discussed, ask the user.
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(browse_engagementsfilters by company ID + date, not by name). - Find the recent calls. Call
browse_engagements(account-scoped,engagementType: MEETINGS,sort: -chronological). Pick the few most recent substantive calls that carry plan-relevant content (typically the last 2-4); each gets its own CI call, which costs credits, so do not fan out across the whole history. Keep their engagement IDs. - Deep-read each call. Run
conversation_intelligencescoped to each engagement ID (one CI call per engagement) for open items, agreed next steps, who owns each (us vs them), and any dates, deadlines, or sequencing discussed. Keep each query to its single engagement; CI sees only the last few engagements and cannot topic-search or count. - Confirm the goal. If a target close/go-live date or objective was discussed, anchor the plan on it. If the plan needs one and it was never stated, ask the user rather than inventing a date.
- Assemble the MAP. Merge the items into one plan, deduping across calls. Give each a clear owner and a target date where one was discussed. Order by date. Mark anything discussed without an owner or date as needing confirmation. Include only steps the conversations support; do not pad with a generic playbook.
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.
- 11d ago First seen · 46 lines · 151 tokens per session scan A 5f91862de050
mutual-action-plan is a skill published in the GitHub repository Zoominfo/zoominfo-mcp-plugin (7 stars, last pushed 7d ago), licensed MIT. It adds 151 tokens to every session and 914 once invoked, about $0.0008 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.
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