gt-linkedin-outbounder

gt-linkedin-outbounder is a skill for Claude Code, Codex from Growth-Today/claude-skills. It costs 236 tokens per session (4,402 once invoked), scanned A, original, MIT.

A guide for contacting potential customers through LinkedIn, the professional networking website. It covers connection requests, direct messages, message sequences, personalization, and account setup.

In plain words
What is it for?
Use it to write LinkedIn outreach, plan follow-ups, personalize messages, and organize campaigns across personal or multiple accounts.
Why use it?
It helps avoid treating LinkedIn like ordinary email by accounting for its public social setting, account limits, and platform rules.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to write LinkedIn outreach, plan follow-ups, personalize messages, and organize campaigns across personal or multiple accounts.

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Install with agentmods
npx agentmods add skills/growth-today/claude-skills/gt-linkedin-outbounder
Install

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.

Any agent
npx skills add Growth-Today/claude-skills --skill gt-linkedin-outbounder
Clone the repo
git clone --depth 1 https://github.com/Growth-Today/claude-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for gt-linkedin-outbounder

README.md
[![agentmods](https://agentmods.dev/badge/skills/growth-today/claude-skills/gt-linkedin-outbounder/github.svg)](https://agentmods.dev/skills/growth-today/claude-skills/gt-linkedin-outbounder)
Your own site
<a href="https://agentmods.dev/skills/growth-today/claude-skills/gt-linkedin-outbounder"><img src="https://agentmods.dev/badge/skills/growth-today/claude-skills/gt-linkedin-outbounder/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.

agentmods 80×15 button for gt-linkedin-outbounder

Your own site · 80×15
<a href="https://agentmods.dev/skills/growth-today/claude-skills/gt-linkedin-outbounder"><img src="https://agentmods.dev/badge/skills/growth-today/claude-skills/gt-linkedin-outbounder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 236 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,402 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00236 $0.04402
Opus 5.5 $0.00094 $0.01761
Sonnet 5.5 $0.00047 $0.00880
Haiku 4.5 $0.00024 $0.00440

Measured 17d ago against content hash fd06360ef707, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

gt-linkedin-outbounder 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 17d 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.

gt-linkedin-outbounder/SKILL.md · 219 lines

How it starts

The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Setup (Run Once Per Session)

Before loading any sub-skill or resource, locate this skill's install directory:

  1. Use Glob to search for **/gt-linkedin-outbounder/SKILL.md
  2. The directory containing this SKILL.md is SKILL_BASE
  3. Sub-skills are at: {SKILL_BASE}/.claude/skills/{sub-skill}/gt-SKILL.md
  4. Resources are at: {SKILL_BASE}/resources/{group}/...

Always resolve SKILL_BASE dynamically, never assume a hardcoded install location.

LinkedIn Outbound (Main Skill)

You are an experienced LinkedIn outbound strategist who has run campaigns across rented engines (multi-account) and personal/founder-led profiles. You routinely hit 30-45% connection acceptance and 20-35% reply rates after acceptance. Your job here is to send each request to the right sub-skill and to handle the things that cut across all of them: account safety, infrastructure, and the rules of the channel.

What LinkedIn Is (and Isn't)

LinkedIn is a semi-warm social channel, not an inbox. The platform's social context fundamentally changes how outreach works:

  • A connection request is the equivalent of "knocking on the door" - much more visible than email
  • A connection acceptance is a small social commitment from the prospect - they are slightly warm, but did NOT opt in to a pitch
  • DMs are conversational - the rhythm is closer to texting a colleague than emailing a stranger
  • Every action is logged and visible; one wrong move (mass spam, irrelevant pitch) damages the sending profile permanently
  • Account restrictions are a constant operational reality - infrastructure is as critical as copy

Mental Models

The Four-Layer LinkedIn-First Engine

Every well-built LinkedIn outbound motion has four layers, in order. Skip a layer and the whole engine underperforms - usually invisibly, until the metrics decay.

  1. Targeting & enrichment - qualified, signal-layered prospect lists. If the data isn't rich enough to write a personalized first line, the targeting isn't ready yet.
  2. Warming - building familiarity before the connection request. Profile views, post engagement, thoughtful comments. By the time the request arrives, the sender's name shouldn't be brand new to the prospect.
  3. Outreach & follow-up - the sequence itself, run with conditional logic so the next step depends on what the prospect did (accepted vs. viewed vs. replied vs. silent). See {SKILL_BASE}/resources/sequences/dm-sequence.md.
  4. Conversion - turning replies into booked meetings, with email as the support channel for prospects who engaged on LinkedIn but didn't convert there.

Read the full file on GitHub · 219 lines

Changes

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.

  1. 17d ago Changed · +1 lines fd06360ef707
  2. 22d ago First seen · 218 lines · 236 tokens per session scan A 96f0c027ef37

Subscribe to this mod's changes

gt-linkedin-outbounder is a skill published in the GitHub repository Growth-Today/claude-skills (6 stars, last pushed 2d ago), licensed MIT. It adds 236 tokens to every session and 4,402 once invoked, about $0.0009 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 2026-09-24.

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