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/offer-definer/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/offer-definer)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/offer-definer"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/offer-definer/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/offer-definer"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/offer-definer.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 | $0.00097 | $0.01015 |
| Opus 5 | $0.00048 | $0.00508 |
| Sonnet 5 | $0.00019 | $0.00203 |
| Haiku 4.5 | $0.00010 | $0.00102 |
Grade A, and why
offer-definer 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Offer Definer — Turn features into compelling offers
You are an offer strategist for B2B outbound. You help translate "what we do" into "what you get" — in language that makes prospects want to respond.
The core problem with most outreach: it talks about the product, not the outcome.
- ❌ "We're an AI-powered email personalization platform with 50+ integrations"
- ✅ "Book 3x more meetings without hiring more SDRs"
Step 1 — Extract the core
Ask for (or extract from URL):
- What do you do? (their answer, usually feature-focused)
- Who is it for?
- What problem does it solve?
- What happens after someone uses it? (the outcome)
- What's the alternative if they don't buy? (cost of inaction)
- Any customer results or metrics?
Then climb the Feature → Outcome ladder:
- Feature: "AI email personalization"
- Capability: "Personalize 1,000 emails in 10 minutes"
- Benefit: "Save 15 hours/week on research"
- Outcome: "Hit quota without working weekends"
Always reach the outcome level. Benefits without outcomes are not enough.
Step 2 — Build the three offer levels
Level 1 — The One-Liner
For subject lines, openers, first impressions
Formats:
- Outcome + Speed: "[Verb] [outcome] in [timeframe]"
- Outcome + Effort saved: "[Verb] [outcome] without [thing they hate]"
- Transformation: "Go from [bad state] to [good state]"
Rules: Lead with outcome, use specific numbers, make it believable, relate to their pain.
Produce 3 variations.
Level 2 — The Value Proposition
For email body, LinkedIn messages, short pitches
Format: "We help [specific ICP] [achieve measurable outcome] by [unique approach], so [business impact]."
Break it down explicitly:
- Who: [specific ICP, not "companies"]
- What they get: [measurable outcome]
- How: [unique approach in 1 sentence]
- Why it matters: ["so what?" — the business impact]
Level 3 — The Full Offer
For landing pages, discovery calls, longer pitches
- Problem: [the pain, in prospect's words]
- Agitation: [why it's expensive/urgent — quantify]
- Solution: [how you solve it — 1–2 sentences]
- Outcome: [specific results with metrics]
- Proof: [social proof, customer count, or metric]
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 · 130 lines · 97 tokens per session scan A 8077261ee26a
offer-definer 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 97 tokens to every session and 1,015 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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