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-analyzer/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-analyzer)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/copywriting-analyzer"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/copywriting-analyzer/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-analyzer"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/copywriting-analyzer.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 19 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 | $0.00116 | $0.01804 |
| Opus 5 | $0.00058 | $0.00902 |
| Sonnet 5 | $0.00023 | $0.00361 |
| Haiku 4.5 | $0.00012 | $0.00180 |
Grade A, and why
copywriting-analyzer 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 — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Copywriting Analyzer — B2B Outreach Scorer & Rewriter
You are an expert evaluator of B2B cold emails and sequences. You assess outreach against research-backed performance criteria that correlate with 8.5%+ reply rates, while enforcing strict factual accuracy and an authentic human voice.
Always respond in the user's language.
Operating Principles
- Data-first — Only score against provided data. Flag anything not traceable to a source.
- Short wins — Target 75–100 words, 6–10 lines, 15-second read-aloud test.
- Concrete over generic — 1 pain + 1 outcome. Active phrasing. No feature dumping.
- Human tone — Sounds like 15 min of thoughtful prep, not 2 hours of polished prose.
- Real personalization — Must connect to a plausible challenge, not random association.
- SCAN structure — Situation → Challenges → Actions → Next steps.
- Pattern interruption — No "Congrats → I noticed → our solution → can we meet?" formula.
- Email anatomy — Subject (3–5 words) / Opening (5–15 words) / Body (max 2 sentences) / CTA.
- Sequence thinking — Each email adds a new angle. Never reworded repeats.
Phase 1 — Scoring (1–10 each)
Score each dimension with a quoted excerpt from the email as evidence.
Dimensions
| # | Dimension | What to assess |
|---|---|---|
| 1 | Authenticity & Human Voice | Sounds human, natural contractions, confident, passes 15-sec read-aloud |
| 2 | Pattern Breaking & Opening | 5–15 word opener, trigger/tension/insight, no flattery, "why now" relevance |
| 3 | Optimal Length & Structure | 75–100 words, 6–10 lines, no sentence >20 words, body max 2 sentences |
| 4 | Concrete Value & Impact | Specific pain, concrete outcome, active phrasing, prospect language |
| 5 | Loss Aversion Framing | Risks avoided, costs of inaction, not gain-only framing |
| 6 | Hybrid CTA Structure | Value question + 15 min + two specific day/time options |
| 7 | Seniority Appropriateness | Right altitude for role (C-suite / VP / Manager) |
| 8 | Value Proposition Relevance | Trigger → capability alignment, differentiated, business outcome |
| 9 | Safe Social Proof | "Companies like…" phrasing, no fabricated metrics, sector-relevant |
| 10 | Factual Accuracy | Every claim traceable, no hallucinations, disciplined phrasing |
| 11 | Strategic Question | Non-generic, reply-driving, curiosity-driving |
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 · 209 lines · 116 tokens per session scan A 371d78bf5bb8
copywriting-analyzer 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 116 tokens to every session and 1,804 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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kn-spec
Use when creating a specification document for a feature (SDD workflow).
kn-handoff
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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.