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-engager-analyticsnpx skills add styfinity/linkedin-engine --skill linkedin-engager-analyticsgit 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 | $0.00058 | $0.00560 |
| Opus 5 | $0.00029 | $0.00280 |
| Sonnet 5 | $0.00012 | $0.00112 |
| Haiku 4.5 | $0.00006 | $0.00056 |
Grade A, and why
linkedin-engager-analytics 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 Engager Analytics
Every post that lands buries warm prospects in its likes and comments. This skill pulls them out, scores them, and hands you the ones worth a conversation.
Inputs
- A post URL (pulled live if the LinkedIn CLI/MCP layer is connected) OR a pasted list of engagers with name, title, company: $ARGUMENTS
- The brief (ICP, persona, pains, offer) loads automatically.
Do this
- Get the engagers. Live via the connected LinkedIn CLI/MCP layer if it is wired in. If it is not, work off the pasted list and say so in one line.
- Score each engager against the brief's ICP, 0 to 100, on title, seniority, company size, industry, and any buying signal. Show the score logic in one phrase per person.
- Compute the ICP match rate: the share of total engagers who clear the fit bar.
- Rank everyone and return the top 10. For each, write a one-line outreach note that references their REAL signal: the exact comment they left, their role, or a recent post of theirs. No generic openers.
- Flag the 3 hottest profiles to action first.
Output
Lead with the ICP match rate headline (e.g. "31% of 64 engagers fit the ICP"). Then a top-10 table: name, title, company, score, outreach note. Then a short "Action these 3 first" list, ready to hand to /linkedin-outreach. Run any draft note through /linkedin-humanizer before it goes out.
Rules
- Pulling live engagers needs the connected CLI/MCP layer. Without it, state plainly that you are scoring the pasted list only.
- Draft notes only. Nothing sends from this skill. Sending happens through the opt-in CLI/MCP layer, on explicit approval, capped at 20 actions a day for new accounts.
- Every outreach note must cite a real, specific signal. If a person's signal is just a like with no comment, say so and rank accordingly.
- No invented titles, companies, or signals. If a field is missing, mark it unknown rather than guessing.
- No em-dashes. No guaranteed-result promises.
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 · 58 tokens per session scan A c12871b33e47
linkedin-engager-analytics is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 560 once invoked, about $0.0003 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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