Agent skill

Handle Linkedin Connection Request Signal

by swan-gtm in swan-gtm/gtm-skills

A skill your agent uses when someone sends a team member an inbound LinkedIn connection request.

MITAuto-check passedSales & Support

Install Handle Linkedin Connection Request Signal

skills CLI
$ npx skills add swan-gtm/gtm-skills --skill handle-linkedin-connection-request-signal -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install swan-gtm/gtm-skills handle-linkedin-connection-request-signal --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/swan-gtm/gtm-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ariel-cohen/handle-linkedin-connection-request-signal .claude/skills/handle-linkedin-connection-request-signal && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
handle-linkedin-connection-request-signal
GitHub stars
172
Token cost
~3.4k tokens
SKILL.md length
1,767 words
Files
2 (incl. references)
Skills in repo
32
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when someone sends a team member an inbound LinkedIn connection request.

  • Works in 8 steps: Dedup (before enrichment) → Resolve requester identity & company… → Internal check → …
  • Someone sends a team member an inbound LinkedIn connection request
  • SKILL.md covers Template placeholders, Overview, Step 1 — Dedup (before… and Step 2 — Resolve requester…, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Handle Linkedin Connection Request Signal is an agent skill from swan-gtm/gtm-skills. Use this skill when someone sends a team member an inbound LinkedIn connection request. An inbound request is a deliberate, ACTIVE first-party intent signal — meaningfully stronger than a passive profile view — and it deserves different scoring rules. The skill resolves and enriches the requester, gates them through dedup / internal / relationship / ICP checks, attaches the signal to the CRM, and scores it with active-signal overrides: a decision maker at a serious company is MQL-eligible on this lone signal, a…

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/setup-customization-checklist.md`).

It sits in Sales & Support, covering Referral and retention marketing. It works with LinkedIn. The repository describes itself as: Open, production-grade GTM skills for AI agents. The licence is MIT.

When your agent uses it

  • Someone sends a team member an inbound LinkedIn connection request
  • Tasks that involve Referral and retention marketing

Example prompts

  • “/handle-linkedin-connection-request-signal”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Dedup (before enrichment)
  2. Resolve requester identity & company (mandatory)
  3. Internal check
  4. CRM relationship check
  5. ICP check
  6. Log signal & create/update Company + Contact in the CRM
  7. Score the signal (ACTIVE-signal rules)
  8. Visibility post

What it can do on your machine

Read from SKILL.md and the folder at commit 67abd04. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Handle Linkedin Connection Request Signal loads about 3.4k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 204 tokens; SKILL.md has 1,767 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~204
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from swan-gtm/gtm-skills at commit 67abd04, republished under its MIT licence (© swan-gtm). 1,767 words, ~3,432 tokens.

Download SKILL.mdSave it as .claude/skills/handle-linkedin-connection-request-signal/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
handle-linkedin-connection-request-signal
description
Use this skill when someone sends a team member an inbound LinkedIn connection request. An inbound request is a deliberate, ACTIVE first-party intent signal — meaningfully stronger than a passive profile view — and it deserves different scoring rules. The skill resolves and enriches the requester, gates them through dedup / internal / relationship / ICP checks, attaches the signal to the CRM, and scores it with active-signal overrides: a decision maker at a serious company is MQL-eligible on this lone signal, a weak persona caps at the awareness tier, and a request from a closed-lost account is treated as a reactivation candidate rather than ignored. Every ICP-relevant outcome posts to a visibility channel; replies and accepts stay human. It never sends outreach.
title
Handle LinkedIn connection request signal
category
Signals
tags
Sales

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.

  • The invitation note, when present, is strong verbatim intent evidence. Carry it through verbatim to scoring, account memory, and any alert. Never invent one if absent.
  • The headline is a LinkedIn headline, not a company name — never treat it as a company or parse a company out of it.
Scope — score + alert only

This skill does scoring + routing + CRM/account-memory updates ONLY. These are people who reached out to YOU — any reply or accept is handled manually by {{PROFILE_OWNER}} outside the agent. Do not send outreach of any kind, do not build or enroll sequences, do not queue or accept connection requests or DMs, and do not create review/approval tasks. Letting {{LEAD_SCORING_SKILL}} post its normal MQL alert for a genuine qualifying account is expected and is NOT outreach. (⚠️ Policy decision — see the checklist. Wire outreach on top only as a conscious opt-in once you trust the signal quality.)

Step 1 — Dedup (before enrichment)

A connection request is a single discrete action, so the only real duplicate source is webhook redelivery from the same person. After you have the person's LinkedIn member id (and, once resolved, the account), check the resolved account's memory for an existing connection-request entry with the same member id. If found → redelivery of an already-processed request → stop silently. No new CRM writes, no memory writes, no channel post.

If the person cannot be identified at all (no name and no LinkedIn URL / member id) → stop silently (anonymous). No enrichment, no writes, no post.

Step 2 — Resolve requester identity & company (mandatory)

Identifying the person is not the same as resolving the company. You need both before proceeding. Never infer the company from the headline.

  1. Run contact enrichment on the person's LinkedIn URL (or member id) to resolve current employer name, company domain, confirmed title, and location.
  2. Headline-vs-enrichment conflict (mandatory live check). Enrichment is frequently stale and lags job changes. If the headline names a specific, different current company or status than enrichment returns — "Stealth", "Building X", "Founder/Co-Founder at [new co]", "ex-[Company]", or a named employer that is not the enriched one — do NOT trust enrichment. Verify against the person's live LinkedIn profile via a profile scrape and use the current position shown there. A request from someone who has left the enriched company is not intent for that company.
  3. If enrichment does not return a confident company name + domain, run a LinkedIn profile scrape on the profile URL, then general web research as a last resort.

Exit condition — UNRESOLVED: If after the full waterfall you still cannot confidently determine a company name + domain, set outcome = UNRESOLVED, write nothing to CRM / account memory, do NOT post, and stop. A guessed company is worse than UNRESOLVED.

Exit this step with: full name, confirmed title, company name + domain, location.

Step 3 — Internal check

If the resolved domain is one of {{INTERNAL_DOMAINS}} → stop silently. No CRM, no memory, no post.

Step 4 — CRM relationship check

Look up the company in the CRM and branch:

  • Active Partner → stop silently. Partner accounts are managed by their owner — no CRM writes, no scoring, no post.
  • Current customer (Closed Won) → outcome = EXISTING CUSTOMER. Log the request (with verbatim note) to the account's memory so the owner sees the touch — memory-only write. Do NOT create/update CRM records, do NOT score. Jump to Step 8.
  • Active (open) deal → outcome = EXISTING RELATIONSHIP. Log the request (with verbatim note) to account memory so the deal owner sees it. Do NOT create outreach. Jump to Step 8.
  • Closed-Lost → reactivation candidate (do NOT ignore). A deliberate connection request from a decision maker months after a lost deal is a genuine reactivation signal. Do NOT silent-stop. Instead:
    1. Log the request (with verbatim note) to account memory.
    2. Continue to Step 5 (ICP) and Step 7 (score) treating it as a live signal.
    3. In Step 7, apply the closed-lost reactivation judgment: the top-tier reactivation floor applies only when there was a genuine prior buying evaluation (a real deal / demo / trial), the requester is a strong buyer persona, and enough time has elapsed ({{REACTIVATION_WINDOW}} since the close). A weak persona, a very recent close, or a loss driven by low-dollar pricing friction does NOT lift it — it stays at the awareness tier: log + visibility post, no MQL.
    4. For a qualifying reactivation, also load {{REACTIVATION_SKILL}} for the deal-owner notification and "what changed" framing — notify the deal owner and post, but do NOT run any reactivation outreach or sequence. The MQL channel must get exactly one post per reactivation: the lead scoring skill's normal alert owns it; the reactivation skill contributes only the deal-owner notification.
  • No existing relationship (net-new) → continue to Step 5.
Show full SKILL.md (718 more words)Show less

Step 5 — ICP check

Compare the company against your ICP segments.

  • Not ICP → outcome = NOT ICP. No CRM writes, no account-memory write. Still post one {{VISIBILITY_CHANNEL}} message. Jump to Step 8.
  • ICP match → continue to Step 6.

Step 6 — Log signal & create/update Company + Contact in the CRM

⛔ Hard gate — do nothing in this step unless ALL are true: (a) requester identified (not anonymous); (b) company confidently resolved to a real domain in Step 2; (c) domain not internal (Step 3 passed); (d) not a current customer (Step 4); (e) passed the ICP check (Step 5). If any fails, skip this step entirely. (Closed-Lost reactivation candidates that are ICP DO proceed here.)

Follow {{CRM_HYGIENE_SKILL}} to create/update the company and a contact for this person (name, title, LinkedIn URL, company, location), associated together. Add a CRM note on the contact:

LinkedIn connection request → {{PROFILE_OWNER}}
• Sent {{PROFILE_OWNER}} a connection request on [detected_at]
• Note included: [verbatim invitation note, or "none"]
• Prior connection requests from this company: [n]

Log the same to the account's memory and refresh its state summary, keyed by the person's LinkedIn member id (this is what Step 1's dedup reads).

Step 7 — Score the signal (ACTIVE-signal rules)

Run {{LEAD_SCORING_SKILL}} on this account. Pass forward the full signal context: signal type, who received it, the verbatim invitation note, the person's name / title / LinkedIn URL / company, prior connection-request count, and that enrichment + CRM attach are done.

A connection request is an ACTIVE signal — these overrides apply and take precedence over generic passive-signal caps:

  • Classify persona first:
    • Decision maker / strong buyer persona: CRO, VP/Head of Sales, VP/Head of Marketing, CMO, CEO, Co-founder, RevOps Lead, GTM Engineer, Growth Lead, Head of Demand Gen, Agency Owner, or equivalent GTM leadership with buying authority (tune to your ICP's buying committee).
    • Weak / non-buyer persona: IC, Support, Finance, HR, Ops, Analyst, Strategy/Partnerships/BD, or any junior title without buying authority.
  • Decision maker → top-tier / MQL-eligible on this lone signal. Do NOT cap at the awareness tier the way a lone passive profile view is capped. A note referencing your product, their pain, or a meeting request strengthens this.
  • "Serious company" still governs the tier. The self-serve gate runs first: a decision maker at a company below {{SELF_SERVE_THRESHOLD}} correctly lands in the self-serve tier, not MQL — that is expected, not a bug. A genuine MQL means a decision maker AND a company that clears that gate.
  • Weak / non-buyer persona → caps at the awareness tier. Accumulates in memory; lifts when other signals stack.
  • Never the maximum tier on a lone LinkedIn signal.
  • The verbatim invitation note feeds CRM + scoring exactly as written.

Instruct the scoring skill explicitly: scoring, tags, stage, CRM sync, memory, and its normal MQL alert are all in scope; outreach is not. For a qualifying account, let its Lead Scoring Alert post to the real MQL channel as usual — do NOT suppress or redirect it.

Step 8 — Visibility post

Post to {{VISIBILITY_CHANNEL}} for these outcomes: SCORED (any tier), NOT ICP, EXISTING CUSTOMER, EXISTING RELATIONSHIP. This is a visibility log and is in addition to any MQL-channel post the scoring skill sent in Step 7 (do not deduplicate them away).

Post nothing (fully silent) for: ANONYMOUS / UNRESOLVED requesters, INTERNAL-domain requesters, ACTIVE PARTNERS, and duplicate redeliveries.

Format:

🤝 [Full Name] — [title]
Company: [company name] ([domain]) | [location]
Note: [verbatim invitation note, or "no note"]
Prior requests: [n] (this company)
LinkedIn: [linkedin_url]

Outcome: [one of]
  SCORED — [tier]  (decision maker at a serious company = MQL)
  EXISTING CUSTOMER — logged, no scoring
  EXISTING RELATIONSHIP — active deal / closed-lost reactivation, logged, no outreach
  NOT ICP — no CRM, no scoring
What we did: [one line]

Hard rules (non-negotiable)

  • Do NOT send any outreach (email or LinkedIn).
  • Do NOT create, edit, or enroll anyone in any sequence.
  • Do NOT queue, send, or accept any connection request or LinkedIn message.
  • Do NOT DM the person or draft a reply, and do NOT create review/approval tasks.
  • Never infer a company from a headline; never guess when the waterfall comes back empty — UNRESOLVED beats a wrong company.
  • If any step is blocked, report the blocker in the outcome/memory — never substitute an outreach action.

What good looks like

A great run turns a raw connection request into exactly one of the named outcomes with zero CRM pollution: the company was resolved through the full waterfall (with the headline-conflict check actually catching job-changers), gates ran in order, the invitation note traveled verbatim into the CRM note, account memory, and the visibility post, and a decision maker at a serious company produced both the MQL alert and the visibility post — one of each. Closed-lost requesters got the reactivation treatment instead of a silent drop.

Mediocre looks like: companies guessed from headlines, weak personas floating above the awareness tier on a single request, reactivation candidates ignored because the account was closed-lost, double MQL posts, or any reply/outreach sent by the agent.

© swan-gtm, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/ariel-cohen/handle-linkedin-connection-request-signal of swan-gtm/gtm-skills.

  • SKILL.md
  • references/setup-customization-checklist.md

Open the folder on GitHubat commit 67abd04

Compare with similar skills

Handle Linkedin Connection Request Signal next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Handle Linkedin Connection Request Signal compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Handle Linkedin Connection Request Signal this skillswan-gtm/gtm-skills172—~3.4kAutomated safety check: PassMIT
Referral IntroTheCraigHewitt/skills159—~6.7kAutomated safety check: PassMIT
Customer Win Back Sequencergooseworks-ai/goose-skills1.2k1 repos~2.8kAutomated safety check: PassMIT
Lead Generation Research GuideRightNow-AI/openfang18k—~1.8kAutomated safety check: PassApache-2.0
B2B Lead Generationminhnv0807/ai-business-skills609—~1.2kAutomated safety check: PassMIT
Outreach Specialistognjengt/founder-skills447—~3.2kAutomated safety check: PassMIT

Similar skills

  • Referral Intro

    TheCraigHewitt/skills

    When the user wants to ask for a warm introduction, build a referral program, or systematize their referral channel.

    159 GitHub stars~6.7k tokensUpdated 4 mo ago
    Sales & SupportAuto-check passed
  • Customer Win Back Sequencer

    gooseworks-ai/goose-skills

    For churned accounts, research what has changed since they left — new funding, team growth, competitor dissatisfaction, product updates that address their pain — then assess re-engagement potential…

    1.2k GitHub starsUsed in 1 repo~2.8k tokens
    Marketing & SEOAuto-check passed
  • Lead Generation Research Guide

    RightNow-AI/openfang

    Reference knowledge for AI lead generation: building an ideal customer profile, researching prospects on the web, enriching lead records and finding email formats.

    18k GitHub stars~1.8k tokensUpdated 3 mo ago
    Sales & SupportAuto-check passed
  • B2B Lead Generation

    minhnv0807/ai-business-skills

    Plans B2B pipeline work from ICP definition and prospecting through lead scoring, outbound sequences, sales assets and MQL to SQL handoff, aiming at qualified pipeline.

    609 GitHub stars~1.2k tokensUpdated 29 days ago
    Sales & SupportAuto-check passed
  • Outreach Specialist

    ognjengt/founder-skills

    Crafts high-converting outreach messages and email sequences for cold outreach, LinkedIn DMs, and follow-ups.

    447 GitHub stars~3.2k tokensUpdated 4 mo ago
    Sales & SupportAuto-check passed
  • Churn Analysis

    shawnpang/startup-founder-skills

    When the user needs to identify at-risk accounts, understand why customers are leaving, reduce churn rate, build health scores, design save plays, or create win-back campaigns.

    343 GitHub stars~2.3k tokensUpdated 6 mo ago
    Sales & SupportAuto-check passed

More from swan-gtm/gtm-skills

All 32 skills in this repo
  • Revops Revenue Planning

    swan-gtm/gtm-skills

    Annual and quarterly revenue plan construction, top-down vs bottoms-up reconciliation, plan versioning, stretch goal handling, and FP&A-RevOps collaboration for B2B revenue teams.

    172 GitHub stars~7.2k tokensUpdated 3 days ago
    Auto-check passed
  • AI Personalization Prompts

    swan-gtm/gtm-skills

    A skill your agent uses when setting up AI-powered personalization, building Clay or lemlist workflows, or automating prospect research — 6 AI personalization prompts (lemlist style) plus 2 email…

    172 GitHub stars~653 tokensUpdated 3 days ago
    Auto-check passed
  • Audience Icp Filter

    swan-gtm/gtm-skills

    A skill your agent uses when a list of people already exists and someone needs to know who on it is worth contacting — event or webinar attendees, registrants, a prospecting export, a CRM segment, a…

    172 GitHub stars~1.9k tokensUpdated 3 days ago
    Auto-check passed
  • Brand Mention Monitor

    swan-gtm/gtm-skills

    A skill your agent uses when you need to know what people are saying about a brand across the web and social — "monitor brand mentions", "what are people saying about [brand] this week", "run a…

    172 GitHub stars~2.1k tokensUpdated 3 days ago
    Auto-check passed
  • Bridge Before Cold

    swan-gtm/gtm-skills

    Use this skill before staging a prospect and before drafting any first touch, when a segment has gone silent, and when deciding whether an account is genuinely cold.

    172 GitHub stars~1.4k tokensUpdated 3 days ago
    Auto-check passed
  • Champion Move Detection

    swan-gtm/gtm-skills

    Use this skill on a monthly cadence to detect champions and heavy users of your paying customers who changed jobs, verify the move against live LinkedIn data, score the new company, and surface…

    172 GitHub stars~5k tokensUpdated 3 days ago
    Auto-check passed

Works with

Questions about Handle Linkedin Connection Request Signal

What does Handle Linkedin Connection Request Signal do?

A skill your agent uses when someone sends a team member an inbound LinkedIn connection request. Handle Linkedin Connection Request Signal is an agent skill from swan-gtm/gtm-skills. Use this skill when someone sends a team member an inbound LinkedIn connection request.

When should I use Handle Linkedin Connection Request Signal?

Handle Linkedin Connection Request Signal fits situations like: someone sends a team member an inbound LinkedIn connection request; tasks that involve Referral and retention marketing.

How do I install Handle Linkedin Connection Request Signal in Claude Code?

Run `npx skills add swan-gtm/gtm-skills --skill handle-linkedin-connection-request-signal -a claude-code`. Or copy the skill folder (skills/ariel-cohen/handle-linkedin-connection-request-signal in swan-gtm/gtm-skills) into .claude/skills/handle-linkedin-connection-request-signal in your project. Claude Code loads it when a task matches its description.

How do I install Handle Linkedin Connection Request Signal in Codex?

Run `npx skills add swan-gtm/gtm-skills --skill handle-linkedin-connection-request-signal -a codex`. Or copy the skill folder (skills/ariel-cohen/handle-linkedin-connection-request-signal in swan-gtm/gtm-skills) into .agents/skills/handle-linkedin-connection-request-signal in your project. Codex loads it when a task matches its description.

Can I use Handle Linkedin Connection Request Signal in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add swan-gtm/gtm-skills --skill handle-linkedin-connection-request-signal -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/handle-linkedin-connection-request-signal, .gemini/skills/handle-linkedin-connection-request-signal, .github/skills/handle-linkedin-connection-request-signal and .opencode/skills/handle-linkedin-connection-request-signal in your project.

What does Handle Linkedin Connection Request Signal need to run?

SKILL.md names no scripts, command-line tools or credentials: Handle Linkedin Connection Request Signal is instructions for the agent only.

Does Handle Linkedin Connection Request Signal access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Handle Linkedin Connection Request Signal safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Handle Linkedin Connection Request Signal use?

Handle Linkedin Connection Request Signal is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Handle Linkedin Connection Request Signal use?

About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 808 tokens, read only when the agent opens those files.

What are the alternatives to Handle Linkedin Connection Request Signal?

Skills that share tags, products or a category with Handle Linkedin Connection Request Signal: Referral Intro (TheCraigHewitt/skills, 159 stars), Customer Win Back Sequencer (gooseworks-ai/goose-skills, 1.2k stars), Lead Generation Research Guide (RightNow-AI/openfang, 18k stars) and B2B Lead Generation (minhnv0807/ai-business-skills, 609 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Handle Linkedin Connection Request Signal?

swan-gtm (a GitHub organization) maintains it in swan-gtm/gtm-skills, which has 172 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on October 8, 2026.

Source: swan-gtm/gtm-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.