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 handle-linkedin-connection-request-signalgit 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/handle-linkedin-connection-request-signal)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/handle-linkedin-connection-request-signal"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/handle-linkedin-connection-request-signal/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/handle-linkedin-connection-request-signal"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/handle-linkedin-connection-request-signal.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 | $0.00160 | $0.03279 |
| Opus 5 | $0.00080 | $0.01639 |
| Sonnet 5 | $0.00032 | $0.00656 |
| Haiku 4.5 | $0.00016 | $0.00328 |
Grade A, and why
handle-linkedin-connection-request-signal 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.
How it starts
The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Template placeholders
Replace every {{...}} before enabling. See the setup checklist reference for the full setup list.
{{PROFILE_OWNER}}— Team member whose inbound connection requests are processed{{CRM}}— Your CRM (e.g. HubSpot, Attio){{INTERNAL_DOMAINS}}— Your company domain(s); requests from these stop silently{{VISIBILITY_CHANNEL}}— Slack channel that gets one visibility post per processed request{{CRM_HYGIENE_SKILL}}— Sub-skill reference: how to create/update companies & contacts in your CRM{{LEAD_SCORING_SKILL}}— Sub-skill reference: your lead scoring & qualification methodology (its normal MQL alerting stays on){{REACTIVATION_SKILL}}— Sub-skill reference: how you handle closed-lost account reactivation (optional — skip the reactivation branch's owner notification if you don't have one){{SELF_SERVE_THRESHOLD}}— Company profile below which accounts route self-serve rather than MQL (default: fewer than 50 employees AND ≤$10M raised){{REACTIVATION_WINDOW}}— Minimum time since a closed-lost deal before a request counts as a reactivation signal (default: ~90 days)
Overview
This skill runs when someone sends {{PROFILE_OWNER}} a LinkedIn connection request. The requester reached out to YOU — this is a deliberate, active first-party intent signal, meaningfully stronger than a passive profile view. The job is: resolve + enrich the requester and their company, gate it, attach to CRM per standard hygiene, and score the signal.
Golden rule — the CRM is never polluted. Only a requester who is identified + company-resolved + not-internal + not-a-current-customer + ICP-matched ever reaches a CRM write. Everyone else is silently discarded or logged to account memory only.
The webhook trigger only validates + routes. All behavior lives here.
Payload shape
The routing trigger passes a single, already-deduplicated signal with the
person's name, LinkedIn member id / URL, headline, and the invitation note (or
null), plus a detection timestamp. Be robust to both a structured person
object and a stringified context blob carrying the same fields.
What ships with it
1 file 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 · 269 lines · 160 tokens per session scan A 2ee73495ae0c
handle-linkedin-connection-request-signal is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 160 tokens to every session and 3,279 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 2026-09-03.
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