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 gs-bbluhm/gainsight-mcp-plugins --skill staircase-meeting-prepgit clone --depth 1 https://github.com/gs-bbluhm/gainsight-mcp-pluginsWrote 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/gs-bbluhm/gainsight-mcp-plugins/staircase-meeting-prep)<a href="https://agentmods.dev/skills/gs-bbluhm/gainsight-mcp-plugins/staircase-meeting-prep"><img src="https://agentmods.dev/badge/skills/gs-bbluhm/gainsight-mcp-plugins/staircase-meeting-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/gs-bbluhm/gainsight-mcp-plugins/staircase-meeting-prep"><img src="https://agentmods.dev/badge/skills/gs-bbluhm/gainsight-mcp-plugins/staircase-meeting-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 | $0.00098 | $0.01535 |
| Opus 5.5 | $0.00039 | $0.00614 |
| Sonnet 5.5 | $0.00020 | $0.00307 |
| Haiku 4.5 | $0.00010 | $0.00153 |
Grade A, and why
staircase-meeting-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 25d 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 — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Staircase Meeting Prep
Walk into a customer meeting with a real point of view. One account, the last ~90 days of communication, turned into a prep sheet: where you left off, what each side owes the other, what to be ready for, and three talking points to drive the conversation.
Foundation references
Read these before composing the query if you have not in this session:
../staircase-mcp-expert/: query mechanics, non-determinism handling.../../_shared/staircase-output-best-practices.md: the house style.
When to use
Trigger phrases:
- "prep me for [account]" / "prep me for my meeting with [account]"
- "I have a meeting with [account]"
- "meeting prep for [account]"
- "what should I know before my [account] call"
- "brief me before [account]"
Optimized for: Cowork (card + sections) and Code (markdown + optional file).
Step 1: Resolve the account
Call staircase_account_lookup(name=<account>) first.
- High-confidence single match: proceed.
- Multiple low-confidence matches: STOP and ask which account. Do not guess.
- No match: say so and ask for an alternate name.
Step 1.5: Pull the structured scaffold (cheap, deterministic)
Before the narrative query, call staircase_account_info(account_id) once. It returns, deterministically, in one call: health / sentiment / engagement scores, risk level, expansion readiness + summary, renewal date, revenue (and per-product ARR where synced), tier, journey phase, last engagement / last touch, owner and CSM names, and the stored analyst summaries (AI Summary, Churn Risk, Renewal). Use it to:
- anchor the "where we left off" and health framing on real numbers, not a re-derived guess;
- decide emphasis (a risk-4 account walks in defensively; an expansion-ready one, offensively);
- fill the commercial context (renewal date, ARR) the prep sheet needs, flagged as CRM-authoritative for the exact figure.
This is the factual spine. analyze_account (next) supplies the dated timeline, the both-sides commitments, stakeholder stances, and evidence that account_info does not.
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.
- 25d ago First seen · 133 lines · 98 tokens per session scan A c48db70f0f9b
staircase-meeting-prep is a skill published in the GitHub repository gs-bbluhm/gainsight-mcp-plugins (4 stars, last pushed 20d ago), licensed MIT. It adds 98 tokens to every session and 1,535 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-26.
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