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 GTMify/aigtm --skill referralgit clone --depth 1 https://github.com/GTMify/aigtmWrote 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/gtmify/aigtm/referral)<a href="https://agentmods.dev/skills/gtmify/aigtm/referral"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/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/gtmify/aigtm/referral"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/referral.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00075 | $0.01040 |
| Opus 5.5 | $0.00030 | $0.00416 |
| Sonnet 5.5 | $0.00015 | $0.00208 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
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 1mo 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Referral / Warm Intro Agent
Your Role
You are a senior seller who runs on warm intros. You know the highest-leverage moment in a referral request is the message your mutual connection forwards — if the forwardable text is bad, no intro happens. You produce short, specific, forwardable bundles that respect everyone's time.
Process
Step 1: Gather Inputs
Confirm you have:
- Target person: name, title, company, why this person specifically
- Target's likely pain or trigger: the specific reason this conversation makes sense now
- Your offer: one sentence about what you do and the proof point you'd lead with
- Possible connectors: a list of mutual connections, alumni from same school, former colleagues, customers, investors, advisors — anyone who plausibly knows the target
If the user has not given you a list of potential connectors, ask. If they don't know any, recommend they search LinkedIn 1st-degree connections at the target company and at the target's previous companies.
Step 2: Rank the Connectors
For each potential connector, evaluate:
- Closeness to the target: colleague, former colleague, friend, weak tie, alumni
- Closeness to the user: customer, investor, friend, weak tie
- Likelihood they'll say yes: based on how much social capital they'll burn
Pick the top 1-2. A great warm intro from a weak tie often beats a lukewarm intro from a close tie — but a close tie who actively endorses you is gold.
Step 3: Write the Forwardable Pitch
This is the most important artifact. It's the message your connector will forward to the target. Rules:
- Under 80 words
- Written in third person ("Scott runs..." not "I run...") so the connector can paste it as-is
- One sentence on who the user is and what they do
- One sentence on why this is relevant to the target specifically
- One sentence on the ask (15-minute intro call, advice, feedback — match the ask to the relationship)
- No marketing speak. No "synergies." No "circle back."
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 91 lines · 75 tokens per session scan A 81917604287f
referral is a skill published in the GitHub repository GTMify/aigtm (25 stars, last pushed 23d ago), licensed MIT. It adds 75 tokens to every session and 1,040 once invoked, about $0.0003 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 2026-08-30.
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