linkedin-comment-warmer

linkedin-comment-warmer is a skill for Claude Code from styfinity/linkedin-engine. It costs 42 tokens per session (469 once invoked), scanned A, original, MIT.

A LinkedIn outreach helper that drafts thoughtful comments on a potential customer's recent posts before you send a connection request.

In plain words
What is it for?
Use it to create two or three specific comments based on pasted LinkedIn posts, matched to your voice and offer, with suggested spacing between comments.
Why use it?
It helps you become familiar to someone before contacting them, without resorting to generic praise or an immediate sales pitch.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the linkedin-engine plugin — 29 skills, 1 hook, 1 MCP server shipped together

Good fit Use it to create two or three specific comments based on pasted LinkedIn posts, matched to your voice and offer, with suggested spacing between comments.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/styfinity/linkedin-engine/linkedin-comment-warmer
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 styfinity/linkedin-engine --skill linkedin-comment-warmer
Clone the repo
git clone --depth 1 https://github.com/styfinity/linkedin-engine

Made for: Claude Code.

Or install linkedin-engine, the plugin that ships this one along with the rest of its 29 skills, 1 hook, 1 MCP server.

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 linkedin-comment-warmer

README.md
[![agentmods](https://agentmods.dev/badge/skills/styfinity/linkedin-engine/linkedin-comment-warmer/github.svg)](https://agentmods.dev/skills/styfinity/linkedin-engine/linkedin-comment-warmer)
Your own site
<a href="https://agentmods.dev/skills/styfinity/linkedin-engine/linkedin-comment-warmer"><img src="https://agentmods.dev/badge/skills/styfinity/linkedin-engine/linkedin-comment-warmer/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 linkedin-comment-warmer

Your own site · 80×15
<a href="https://agentmods.dev/skills/styfinity/linkedin-engine/linkedin-comment-warmer"><img src="https://agentmods.dev/badge/skills/styfinity/linkedin-engine/linkedin-comment-warmer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 469 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 $0.00042 $0.00469
Opus 5.5 $0.00017 $0.00188
Sonnet 5.5 $0.00008 $0.00094
Haiku 4.5 $0.00004 $0.00047

Measured 2mo ago against content hash d4131df489cb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

linkedin-comment-warmer 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 2mo 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.

skills/linkedin-comment-warmer/SKILL.md · 31 lines

What it actually says

LinkedIn Comment Warmer

Warming before connecting. This skill drafts real, value-add comments on a target's recent posts so your name and face register before you ever send the request.

Inputs

  • The target (name, role) and a paste of their recent post(s): $ARGUMENTS
  • The brief (your voice profile, the offer, the buyer's pains) loads automatically.

Do this

  1. Read each pasted post. Find the one real thing in it worth responding to: a claim to extend, a tension to name, a question they left open.
  2. Write 2-3 comments, one per post, each adding a single genuine insight in your voice. No pitch, no link, no "great post".
  3. Make each comment stand on its own as something a peer would say. Reference a specific line from their post.
  4. Sequence them so they read as authentic engagement over days, not a blitz: one comment, wait, then the next.

Output

2-3 ready comments, each tagged to the post it belongs on (Post 1, Post 2, Post 3). End with a one-line spacing note: leave a day or two between comments, never all in one sitting.

Rules

  • Real value only. One actual insight per comment. Never flattery, never "love this", never a thinly veiled pitch.
  • No pitch and no link inside a comment. The comment earns the recognition; the connect comes later.
  • Space them out. A blitz of comments in one hour reads as a tactic, not a person.
  • No em-dashes, no exclamation marks.
  • Draft only. Claude drafts, the operator reads each comment and posts it themselves. Posting comments is manual here, not the send layer.
  • Once they have engaged back or you have commented 2-3 times, hand the connect to /linkedin-connection-note and run /linkedin-humanizer on the draft before sending.
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. 2mo ago First seen · 31 lines · 42 tokens per session scan A d4131df489cb

Subscribe to this mod's changes

linkedin-comment-warmer is a skill published in the GitHub repository styfinity/linkedin-engine (9 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 469 once invoked, about $0.0002 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 2026-08-31.

Related

linkedin-comment-warmer compared with skills from other repositories

linkedin-comment-warmer next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
linkedin-comment-warmerstyfinity/linkedin-engineSkill94mo agoMITA42Claude Code
LinkedIn Automationclaude-office-skills/skillsSkill5679mo agoMITA19Claude Code, Codex
linkedin-carouselsericrisco/rsc-harnessSkill5294d agoMITA91Claude Code, Codex
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deepline-enginegetaero-io/gtm-eng-skillsSkill73yesterdayMITA43Claude Code, Codex
deepline-gtmgetaero-io/gtm-eng-skillsSkill73yesterdayMITA332Claude Code, Codex