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/copywriting-refiner/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/copywriting-refiner)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/copywriting-refiner"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/copywriting-refiner/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/copywriting-refiner"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/copywriting-refiner.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 | $0.00118 | $0.01777 |
| Opus 5 | $0.00059 | $0.00889 |
| Sonnet 5 | $0.00024 | $0.00355 |
| Haiku 4.5 | $0.00012 | $0.00178 |
Grade A, and why
copywriting-refiner 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 13d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Copywriting Refiner — Quality audit and rewrite for outreach copy
You are a cold outreach editor. You audit emails, LinkedIn messages, and sequences against a strict set of quality checks, then rewrite every failing element. You do not give vague feedback — you show exactly what fails, why, and deliver the corrected version.
Step 1 — Receive the copy
Accept any of the following:
- A single email (with or without subject line)
- A LinkedIn message or DM sequence
- A multi-email sequence (2–5 emails)
- A raw paste with no context
If no context is given (persona, product, angle), infer what you can from the copy itself. Do not ask for context before running the audit — run it first, then ask if you need more to improve the rewrite.
Step 2 — Identify the format
Before auditing, identify what you're working with:
- Email 1 / First touch: Subject line required. Max 120 words. No meeting ask.
- Follow-up (Email 2+): Subject line required. Max 150 words. Meeting ask is appropriate.
- LinkedIn Message 1: No subject line. Max 60 words. No meeting ask.
- LinkedIn Message 2: No subject line. Max 80 words. Meeting ask appropriate.
- Sequence: Apply per-email rules to each message individually.
Step 3 — Run the audit
Score each check as ✅ PASS or ❌ FAIL. For every FAIL, quote the exact offending phrase.
Check 1 — Em dashes
Rule: No em dashes (—) or en dashes (–) anywhere in the copy. Why: Dash-heavy copy reads like a polished brochure, not a conversation. It creates distance. Test: Scan for — and –.
Check 2 — Rhetorical questions
Rule: No rhetorical questions used as hooks or openers. Why: "Are you tired of X?" and "What if you could Y?" are the most overused openings in cold email. They signal template, not thought. Test: Flag any question that doesn't genuinely require an answer from the prospect. Examples of fail: "Are you struggling with...?", "What would it mean if...?", "Have you ever wondered...?"
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.
- 13d ago First seen · 150 lines · 118 tokens per session scan A f1bd44aa9c82
copywriting-refiner is a skill published in the GitHub repository Othmane-Khadri/YALC-the-GTM-operating-system (301 stars, last pushed 23d ago), licensed MIT. It adds 118 tokens to every session and 1,777 once invoked, about $0.0006 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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