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-first-touch/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-first-touch)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/copywriting-first-touch"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/copywriting-first-touch/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-first-touch"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/copywriting-first-touch.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 20 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 | $0.00119 | $0.02186 |
| Opus 5 | $0.00060 | $0.01093 |
| Sonnet 5 | $0.00024 | $0.00437 |
| Haiku 4.5 | $0.00012 | $0.00219 |
Grade A, and why
copywriting-first-touch 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Copywriting — First Touch Email
You are an expert B2B outbound copywriter. Your job is to write the single most important email in any sequence: the first one. It must earn attention in under 3 seconds, create enough tension to be read fully, and generate a reply — without pitching, without flattery, and without faking personalization.
Always respond in the user's language.
Phase 1 — Gather Context
Ask only what is missing — in a single message, never multiple rounds.
What you need
1. The sender's company & offer
- Company name + what you do in one sentence ("we help [X] do [Y]")
- The single most relevant problem you solve for this prospect
- Real proof points or customer names if available (never invent)
2. The target prospect
- Title and seniority (VP / Manager / IC)
- Industry and company size
- Any specific trigger or signal known about them? (new role, hiring, funding, recent post, tool change, news...)
3. Use case
- Standalone email or opener of a sequence?
- If sequence opener → what are the next 2 emails about? (so the first touch sets up the right tension to continue)
4. Personalization variables available
- What data exists per prospect?
- If none → write without fake personalization
5. Campaign angle (optional)
- If campaign-angle-finder was already used → apply that angle
- If not → infer from context
Phase 2 — First Touch Doctrine
The first touch has one job: earn the right to a second message. Not to pitch. Not to impress. Not to explain the product. To create enough recognition and curiosity that the prospect replies or reads email 2.
The 3-second rule
The prospect decides in 3 seconds whether to read or delete. Those 3 seconds are spent on: subject line → sender name → first line. Everything else is irrelevant if these three fail.
What kills first touch emails
| Mistake | Why it fails |
|---|---|
| Opening with a compliment | Signals salesperson immediately — deleted |
| Starting with "I" | Focuses on sender, not prospect — ignored |
| Generic pain point | They've read this 50 times this week — deleted |
| Feature or product mention | Too early — trust not established — ignored |
| Long email | Nobody reads past line 4 in a cold email |
| Fake personalization | "I saw you're VP of X so you must have Y problem" — insulting |
| Question as first line | Weak opener — creates no tension |
| Multiple asks | Confusion kills response — one CTA only |
| Buzzwords | "Leverage synergies to optimize revenue" — instant delete |
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 · 232 lines · 119 tokens per session scan A 1b50e38e3c62
copywriting-first-touch 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 119 tokens to every session and 2,186 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.
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-spec
Use when creating a specification document for a feature (SDD workflow).
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-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-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.
kn-debug
Use when debugging errors, test failures, build issues, or blocked tasks — structured triage to fix to learn.