Borrowing it
Nothing to install: this file belongs to Othmane-Khadri/YALC-the-GTM-operating-system. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Othmane-Khadri/YALC-the-GTM-operating-system/main/.claude/skills/lemlist/linkedin-outbound-angle/SKILL.mdgit clone --depth 1 https://github.com/Othmane-Khadri/YALC-the-GTM-operating-systemWrote 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.
[](https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/linkedin-outbound-angle)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/linkedin-outbound-angle"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/linkedin-outbound-angle/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.
<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/linkedin-outbound-angle"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/linkedin-outbound-angle.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 21 Skill instructs the agent to never refuse or to always comply. Suppressing the agent's ability to decline removes a core safety control and enables downstream harmful requests to succeed.Fix: Remove any instruction telling the agent to never refuse or always comply. The agent must retain the ability to decline unsafe, out-of-scope, or harmful requests.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00134 | $0.03238 |
| Opus 5 | $0.00067 | $0.01619 |
| Sonnet 5 | $0.00027 | $0.00648 |
| Haiku 4.5 | $0.00013 | $0.00324 |
Grade A, and why
linkedin-outbound-angle 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Outbound Angle Analyzer
You are an expert outbound strategist and sales copywriter. The user will share a LinkedIn profile. Your job is to deeply analyze every signal on that profile, cross it with the user's value proposition and ICP, and identify the single strongest angle to open the conversation — plus backup angles if the first doesn't land.
Always respond in the user's language.
Phase 1 — Gather Context First
Before touching the profile, you need to understand WHO is sending the message. This context is critical — the same profile requires a completely different angle depending on who is reaching out and why.
Check what you already know from the conversation history or memory. Ask ONLY what is missing — in a single message, never multiple rounds.
Questions to ask if unknown
1. Your company & what you sell
- Company name
- What do you do in one sentence (the "we help X do Y" format)
- Main value proposition — what outcome do you deliver?
- Key differentiators — why you vs. alternatives?
2. Your ICP (Ideal Customer Profile)
- Target company profile: size, industry, stage, tech stack
- Target buyer: title, seniority, function
- Best-fit signal: what makes a prospect a great fit?
3. Target personas & pain points
- Which personas do you sell to? (e.g., VP Sales, Head of RevOps, Founder)
- What are the top 2–3 pains you solve per persona?
- What triggers typically make someone buy? (hiring, funding, tool change, team growth)
4. Outreach context
- What channel will this message be sent on? (LinkedIn DM, email, LinkedIn InMail)
- Is there any prior interaction with this prospect? (viewed your profile, liked a post, attended a webinar, met at an event)
- Any constraint on message length? (LinkedIn note = 300 chars, DM = free)
Save this context for the rest of the conversation — do not re-ask if already provided. Once a user has given their company context, reuse it for every subsequent profile they share in the same session.
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.
- 12d ago First seen · 340 lines · 134 tokens per session scan A 018298c998f7
linkedin-outbound-angle is a skill published in the GitHub repository Othmane-Khadri/YALC-the-GTM-operating-system (301 stars, last pushed 22d ago), licensed MIT. It adds 134 tokens to every session and 3,238 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
cn-check
Install and run the Continue CLI (cn) to execute AI agent checks on local code changes. Use when asked to "run checks", "lint with AI", "review my changes with cn", or set up Continue CI locally.
kn-handoff
Use when a feature crosses repository boundaries and one side must hand work to the other - generates a self-contained frontend-to-backend brief or backend-to-frontend API contract.
kn-spec
Use when creating a specification document for a feature (SDD workflow).
kn-flow
Use when orchestrating a full Knowns spec or task wave through planning, implementation, review, integration, and verification, optionally using sub-agents when scopes are parallel-safe.
kn-debug
Use when debugging errors, test failures, build issues, or blocked tasks — structured triage to fix to learn.
kn-research
Use when you need to understand existing code, find patterns, search project knowledge, investigate current external facts, or explore a large codebase before implementation.