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.
npx agentmods add skills/styfinity/linkedin-engine/linkedin-comment-warmernpx skills add styfinity/linkedin-engine --skill linkedin-comment-warmergit clone --depth 1 https://github.com/styfinity/linkedin-engineWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00042 | $0.00469 |
| Opus 5 | $0.00021 | $0.00234 |
| Sonnet 5 | $0.00008 | $0.00094 |
| Haiku 4.5 | $0.00004 | $0.00047 |
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 2d 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.
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
- 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.
- Write 2-3 comments, one per post, each adding a single genuine insight in your voice. No pitch, no link, no "great post".
- Make each comment stand on its own as something a peer would say. Reference a specific line from their post.
- 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.
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.
- 2d ago First seen · 31 lines · 42 tokens per session scan A d4131df489cb
linkedin-comment-warmer is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 469 once invoked, about $0.0002 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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