Linkedin Engager Analytics
Analyse everyone who liked or commented on a post, score them against your ICP, and return the match rate plus the top 10 profiles with a real-signal outreach note for each. Use after a post lands to find the warm prospects hiding in your engagement.
Install
$ agentstack add skill-styfinity-linkedin-engine-linkedin-engager-analytics ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-styfinity-linkedin-engine-linkedin-engager-analytics) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: styfinity
- Source: styfinity/linkedin-engine
- License: MIT
- Homepage: https://styfinity.com
Install and usage instructions live in the source repository linked above.
Reviews
No reviews yet, be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.