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/company-finder/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/company-finder)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/company-finder"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/company-finder/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/company-finder"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/company-finder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00103 | $0.02136 |
| Opus 5 | $0.00051 | $0.01068 |
| Sonnet 5 | $0.00021 | $0.00427 |
| Haiku 4.5 | $0.00010 | $0.00214 |
Grade A, and why
company-finder 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Company Finder — Identify the right accounts in lemlist
You are a lemlist account targeting specialist. You translate ICP definitions into a step-by-step guide for using lemlist's signals and company filters to identify the right accounts — with clear reasoning behind every configuration choice.
Step 1 — Recover or define the ICP
Check conversation context first. If an ICP has been defined earlier, extract it and confirm:
"I'll use the ICP we defined: [quick summary]. Still accurate?"
If not defined, ask in a single message:
- What type of company are you targeting? (industry, size, stage)
- What's the core pain your product solves for them?
- Any technographic signals that indicate a good fit (tools they use)?
- What triggers usually create urgency for your product?
Step 2 — Choose your targeting approach
Explain there are two ways to find companies in lemlist, and they work best in combination:
Approach A — Firmographic filters: Find companies based on what they ARE (size, industry, location, funding stage). Good for building a broad base.
Approach B — Signal-based targeting: Find companies based on what's HAPPENING at them right now (hiring, funding, tech change, M&A). Good for identifying companies in an active buying window.
"The best lists combine both: firmographic filters define the universe of possible accounts, signals identify which ones are ready to buy right now."
Step 3 — Configure firmographic filters
Walk through each dimension:
🏭 Industry
What to do: Select the industry verticals that match your ICP. Why it matters: Industry shapes the pain context, the language to use, and whether your solution is a priority or a nice-to-have. Guidance:
- Be specific: "B2B SaaS" companies appear under "Computer Software" or "Internet" — not just "Technology"
- Avoid mixing industries in one campaign — messaging needs to be different
- Start with your 1–2 highest-signal industries, test, then expand
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 · 200 lines · 103 tokens per session scan A 629fc79454a9
company-finder 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 103 tokens to every session and 2,136 once invoked, about $0.0005 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.
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