Linkedin Icp Definer
Define and lock the ICP and disqualifiers from real evidence, then write a ready-to-paste ICP block into the brief. Use before any warming or outreach so the other skills score against a real target, not a guess.
Install
$ agentstack add skill-styfinity-linkedin-engine-linkedin-icp-definer ✓ 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-icp-definer) 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 ICP Definer
Your ICP is not who you wish bought from you. It is who actually does. This skill reads the evidence, names the disqualifiers out loud, and locks it into the brief so every other skill scores against the same target.
Inputs
- Won and lost notes, descriptions of your best clients, and who you do NOT want: $ARGUMENTS
- The brief (offer, pains, voice) loads automatically. If the offer section is thin, fixing it is the first thing to flag.
Do this
- Derive the ICP from the evidence, not from aspiration. From the won notes, pull the pattern: title, seniority, company size, industry, and the trigger that made them buy (the event or pain that started the conversation).
- Cross-check the lost notes. If a profile shape keeps losing, it is a disqualifier, not a stretch target. Say so.
- Write explicit disqualifiers. Name the titles, company sizes, stages, or situations that look close but never convert. Be specific enough that a person could be ruled out in five seconds.
- List the 2 or 3 buying signals that mark a live one right now (a hiring post, a funding event, a role change, a public complaint about the exact pain the offer solves).
- Phrase every line so the other skills can score against it. Concrete attributes, not adjectives.
Output
A ready-to-paste "## ICP" block for linkedin-brief.md, with three labelled parts: Fit criteria (title, seniority, company size, industry, buying trigger), Disqualifiers (explicit, ruled-out shapes), and Buying signals (the 2 or 3 live markers). Hand it back ready to drop straight into the brief.
Rules
- ICP from evidence, not aspiration. If a claim is not backed by a won or lost note, mark it an assumption to test, not a fact.
- Name disqualifiers explicitly. A target with no disqualifiers is not defined yet.
- Every attribute must be something a sibling skill can score against. No vague adjectives.
- No em-dashes. Use a comma, a period, or " - ".
- This writes to the brief but does not send anything. It only shapes who the outreach skills aim at.
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.
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Versions
- v0.1.0 Imported from the upstream source.