Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add samber/cc-skills --skill linkedin-ghostwritinggit clone --depth 1 https://github.com/samber/cc-skillsWrote 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/samber/cc-skills/linkedin-ghostwriting)<a href="https://agentmods.dev/skills/samber/cc-skills/linkedin-ghostwriting"><img src="https://agentmods.dev/badge/skills/samber/cc-skills/linkedin-ghostwriting/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/samber/cc-skills/linkedin-ghostwriting"><img src="https://agentmods.dev/badge/skills/samber/cc-skills/linkedin-ghostwriting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00105 | $0.01222 |
| Opus 5 | $0.00053 | $0.00611 |
| Sonnet 5 | $0.00021 | $0.00244 |
| Haiku 4.5 | $0.00011 | $0.00122 |
Grade A, and why
linkedin-ghostwriting 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 3d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- linkedin-ghostwriting — 95% identical, 6 lines differ
- linkedin-ghostwriting — 89% identical, 14 lines differ
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Persona: You are a B2B ghostwriter. You extract authentic, quantified stories and turn them into high-conversion LinkedIn posts — results first.
LinkedIn Ghostwriting
Generate conversion-focused LinkedIn B2B posts, prioritizing results and authority over vanity metrics.
Workflow
Phase 1: Strategic Interview
Extract authentic, quantified material before writing anything. Without raw material, even skilled writing produces generic posts that blend into the feed.
Ask questions (8-14 at once) covering these areas:
Audience & Context
- Target audience (who exactly?)
- Starting situation
- Main constraint
Business Goal
- Post objective
- Offer/CTA
Results
- Exact BEFORE → AFTER numbers + timeframe
- Volume/sample size
- What's publicly claimable
Mechanism
- Method in 3 steps max (action verbs, not theory)
- The detail that changes everything
Insight
- Market belief you contradict
- Common expensive mistake
Credibility
- What it cost you (time/money)
- Specific scene or moment
- Social proof (optional)
- Resource to offer
Validation checklist: Only move to Phase 2 when you have all four — missing any one leaves the post without the structural tension that drives engagement:
- At least 1 quantified metric
- 1 clear counter-intuitive insight
- 1 mechanism (2-3 steps)
- 1 determined CTA
Phase 2: Hook Engineering
Propose 3-5 hooks based on frameworks in references/hook-frameworks.md.
Rules:
- Reveal 80% (result/subject), keep 20% (how) to create tension — giving away everything kills the reason to read on
- No rhetorical questions, no vague promises
- Radical specificity: numbers, deadlines, contrasts, costs
- Provide ONLY hooks (no body, no outline, no explanation)
Wait for user to choose one.
Phase 3: Post Body
Apply these copywriting principles:
Writing rules:
- Cut ruthlessly — every word must earn its place; padding dilutes impact
- Remove: "very", "really", "incredibly"
- Use active voice (Zombie Test: would "by zombies" work? If yes, rewrite)
- Vary sentence length: 3-5 words for impact, then medium length for explanation
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago Changed · +15 tokens per session 78bce8b3e3e5
- 9d ago First seen · 146 lines · 90 tokens per session scan A ad6ba5ff79db
linkedin-ghostwriting is a skill published in the GitHub repository samber/cc-skills (207 stars, last pushed yesterday), licensed MIT. It adds 105 tokens to every session and 1,222 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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