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 swan-gtm/gtm-skills --skill warm-intro-intelligencegit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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/swan-gtm/gtm-skills/warm-intro-intelligence)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/warm-intro-intelligence"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/warm-intro-intelligence/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/swan-gtm/gtm-skills/warm-intro-intelligence"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/warm-intro-intelligence.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 | $0.00116 | $0.01938 |
| Opus 5 | $0.00058 | $0.00969 |
| Sonnet 5 | $0.00023 | $0.00388 |
| Haiku 4.5 | $0.00012 | $0.00194 |
Grade A, and why
warm-intro-intelligence 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- warm-intro-intelligence — 94% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Warm Intro Intelligence
Template placeholders
{{COMPANY_NAME}}— Your company's name.{{COMPANY_DOMAIN}}— Your company's email/website domain (e.g.acme.com). Also used to filter your own employees out of partner data.{{TEAM_ROSTER}}— A sub-page you build and maintain: every employee at your company with LinkedIn handle and full career history. Build instructions are in the Node Registry sub-page.{{INVESTOR_REGISTRY}}— A sub-page you maintain: your company's investor firms plus (ideally) the named individual partners. Build instructions are in the Node Registry sub-page.{{CUSTOMER_LIST}}— Your CRM's source-of-truth list of active customers (examples assume HubSpot; adapt to your CRM).{{PARTNER_LIST}}— Your CRM's partner list or custom object (name/view of your partner records).{{HUBSPOT_PARTNERS_OBJECT_ID}}— The objectTypeId of your partners custom object, if you store partners as a HubSpot custom object (e.g.2-XXXXXXXXX).{{PERSONA_MAP}}— Your segment → buyer-persona priority table (from your ICP qualification process): for each ICP segment, the primary personas and fallbacks.
Purpose
On-demand play that surfaces the strongest available warm intro paths from your company's relationship network to a target prospect.
Triggered by natural language such as: "Find warm intro paths for [company]", "Who do we know at [company]?", "Can anyone intro me to [role] at [company]?", "Warm intro - [company]", or similar intent.
Output is decision support only. The agent never contacts anyone, drafts outreach to connectors, or takes any action. The rep always decides.
Step 1 - Resolve Input
Determine what the rep provided:
- Specific contact named -> target that contact only. Skip Steps 2 and 3. Go directly to Step 4.
- Persona or role specified -> go to Step 3b to find matching contacts at the prospect.
- Company only -> go to Step 2.
Step 2 - ICP Segment Classification (company-only input)
What ships with it
4 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.
- 9d ago First seen · 159 lines · 116 tokens per session scan A dd1329bd764a
warm-intro-intelligence is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 116 tokens to every session and 1,938 once invoked, about $0.0006 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 2026-09-03.
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