gt-linkedin-outbound

gt-linkedin-outbound is a skill for Claude Code, Codex from Growth-Today/claude-skills. It costs 235 tokens per session (4,299 once invoked), scanned A, original, MIT.

A set of instructions for planning and writing LinkedIn outreach for business-to-business campaigns. It covers connection requests, direct messages, message sequences, personalization, account safety and multi-account campaign setup.

In plain words
What is it for?
Writing cold connection requests and messages, personalizing outreach, planning follow-ups, and handling rented or personal LinkedIn accounts.
Why use it?
It helps turn a broad outreach goal into channel-specific messages and a structured sequence, while accounting for LinkedIn's limits and social context.

Skill for Claude CodeCodex

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

Good fit Writing cold connection requests and messages, personalizing outreach, planning follow-ups, and handling rented or personal LinkedIn accounts.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/growth-today/claude-skills/gt-linkedin-outbound"><img src="https://agentmods.dev/badge/skills/growth-today/claude-skills/gt-linkedin-outbound.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 235 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,299 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 $0.00235 $0.04299
Opus 5 $0.00118 $0.02150
Sonnet 5 $0.00047 $0.00860
Haiku 4.5 $0.00023 $0.00430

Measured 12d ago against content hash 4562b2d224a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

gt-linkedin-outbound 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 12d 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-outbound/SKILL.md · 218 lines

How it starts

The opening of the file, as written. The whole thing — 218 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-outbound/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 · 218 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. 12d ago First seen · 218 lines · 235 tokens per session scan A 4562b2d224a5

Subscribe to this mod's changes

gt-linkedin-outbound is a skill published in the GitHub repository Growth-Today/claude-skills (3 stars, last pushed today), licensed MIT. It adds 235 tokens to every session and 4,299 once invoked, about $0.0012 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 2026-08-31.

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