linkedin-comment-engine

linkedin-comment-engine is a skill for Claude Code from styfinity/linkedin-engine. It costs 39 tokens per session (522 once invoked), scanned A, original, MIT.

A LinkedIn comment drafting tool that creates two comments for someone else's post. The comments add a specific insight, detail, or real-world number instead of offering generic praise.

In plain words
What is it for?
Use it when commenting on a post written by a buyer, peer, connector, or overlapping audience. It creates one short comment and one longer teaching-style comment, each with a subtle reason to visit the writer's profile.
Why use it?
It helps build visibility through useful public replies rather than starting with private messages. It also gives the writer a way to show relevant expertise without making a sales pitch or adding a link.

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 when commenting on a post written by a buyer, peer, connector, or overlapping audience. It creates one short comment and one longer teaching-style comment, each with a subtle reason to visit the writer's profile.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/styfinity/linkedin-engine/linkedin-comment-engine
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-engine
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-engine

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/styfinity/linkedin-engine/linkedin-comment-engine"><img src="https://agentmods.dev/badge/skills/styfinity/linkedin-engine/linkedin-comment-engine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 522 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 $0.00039 $0.00522
Opus 5.5 $0.00016 $0.00209
Sonnet 5.5 $0.00008 $0.00104
Haiku 4.5 $0.00004 $0.00052

Measured 1mo ago against content hash 097e37e926f6, 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-engine 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 1mo 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-engine/SKILL.md · 31 lines

What it actually says

LinkedIn Comment Engine

Warming starts in the comments, not the inbox. This skill writes comments that add real value to someone else's post and quietly pull profile views from the people watching.

Inputs

  • The post you want to comment on (paste the full text) and the author's relevance to your ICP (a buyer, a peer, a connector, an audience overlap): $ARGUMENTS
  • The brief (persona tone, pains, offer, voice) loads automatically.

Do this

Write two comment variants the user can pick from:

  1. Add ONE genuine thing. Either a specific insight, a build detail, or a number from real work (attribute to "a post" or "a client", never a named entity). Not "great post", not generic praise.
  2. Agree-then-extend, or respectfully reframe. Build on the author's point with a second-order observation, or offer a sharper angle without making them wrong.
  3. Leave a soft hook at the end that earns a profile click: a half-told result, a "here is what most miss", or a question that signals you operate in their world. No pitch, no link.
  4. Variant A is short and punchy (one or two lines). Variant B is a longer mini-teaching comment (three to five lines) that demonstrates competence.

Output

Two labelled comment variants (A short, B mini-teaching) plus a one-line note on which to use when: A for high-traffic posts where brevity wins attention, B when the author is a direct ICP and you want them to read your whole profile.

Rules

  • Never pitch in a comment. Never argue to win. The job is to make a decision-maker click your profile, not to score a point.
  • Add value the author would thank you for, even if they never reply.
  • Anonymise all proof: keep the numbers, drop the names.
  • No em-dashes, no exclamation marks, no "great post", no "thanks for sharing".
  • Draft only. Claude drafts, the operator reads and posts. When a reply comes back warm, hand it to /linkedin-reply-triager. Run /linkedin-humanizer on either variant if it reads stiff.
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. 1mo ago First seen · 31 lines · 39 tokens per session scan A 097e37e926f6

Subscribe to this mod's changes

linkedin-comment-engine is a skill published in the GitHub repository styfinity/linkedin-engine (9 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 522 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.

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