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 First-Touch-Inc/firsttouch-agent-skill-packs --skill customer-referralgit clone --depth 1 https://github.com/First-Touch-Inc/firsttouch-agent-skill-packsWrote 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/first-touch-inc/firsttouch-agent-skill-packs/customer-referral)<a href="https://agentmods.dev/skills/first-touch-inc/firsttouch-agent-skill-packs/customer-referral"><img src="https://agentmods.dev/badge/skills/first-touch-inc/firsttouch-agent-skill-packs/customer-referral/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/first-touch-inc/firsttouch-agent-skill-packs/customer-referral"><img src="https://agentmods.dev/badge/skills/first-touch-inc/firsttouch-agent-skill-packs/customer-referral.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 | $0.00106 | $0.01925 |
| Opus 5 | $0.00053 | $0.00962 |
| Sonnet 5 | $0.00021 | $0.00385 |
| Haiku 4.5 | $0.00011 | $0.00193 |
Grade A, and why
customer-referral 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Referral Thank-You
Outcome: When someone becomes a customer, connect with them on LinkedIn and send a warm thank-you that invites product feedback and a low-pressure referral signal, without making the first customer touch feel like a sales ask.
First-run onboarding gate
Before running this skill for the first time in a workspace, load ../../references/onboarding.md and complete the onboarding questions. Do not proceed until you know: LinkedIn account type (free/basic = no connection notes; recommend 10 connection requests/day and never exceed the FirstTouch max of 20/day; Sales Navigator/Premium = connection notes available; recommend 20 connection requests/day and never exceed the FirstTouch max of 30/day), HubSpot access (MCP, service key/private app token, HubSpot list only, or none), and which play the user wants to run. Recommend high-intent plays before broader outbound to keep the LinkedIn account healthy.
When to use
- a deal moves to Closed Won
- a new customer starts onboarding
- a user/admin completes setup or hits first value
- the founder, AE, CSM, or account owner wants to thank the customer personally
- the team wants to ask for feedback and light referrals without launching a bulk campaign
Inputs
- Customer source: HubSpot Closed Won/customer list, HubSpot workflow/list output, CSV/imported customer list, or FirstTouch-accessible customer source
- Sender/routing rule: founder, account owner, AE, CSM, or named executive sender
- Relationship context: product purchased, use case, onboarding status, first-value milestone, or why they chose the product
- Referral ask style: soft network ask, partner/customer intro ask, or feedback-only if the relationship is too early
HubSpot is preferred for Closed Won/customer routing and CRM logging. It is not mandatory when the user provides a customer CSV, imported list, or FirstTouch-accessible customer source with explicit customer status.
Step-by-step
Before drafting or queueing any contact, run the standard safety gates from ../../references/safety-governance.md: Gate 0 suppression/DNC, Gate 1 duplicate/recent-contact check, Gate 2 owner/CSM routing, Gate 3 daily cap sharing, and Gate 4 human approval. Suppressed, opted-out, duplicate, recently contacted, or misrouted records are skipped and logged.
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 · 124 lines · 106 tokens per session scan A 3271fc533a2c
customer-referral is a skill published in the GitHub repository First-Touch-Inc/firsttouch-agent-skill-packs (5 stars, last pushed 2mo ago), licensed MIT. It adds 106 tokens to every session and 1,925 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 2026-08-31.
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