linkedin-ghostwriting

linkedin-ghostwriting is a skill for Claude Code, Codex from swan-gtm/gtm-skills. It costs 141 tokens per session (1,382 once invoked), scanned A, original, MIT.

A writing workflow for drafting LinkedIn posts in the voice of a named founder or executive without publishing them automatically.

In plain words
What is it for?
It helps create and rank post options for industry news, reposts, and executive thought leadership while checking for repeated ideas.
Why use it?
It reduces repetitive posts and generic marketing language by using the person's real writing history, current topics, and approval process.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps create and rank post options for industry news, reposts, and executive thought leadership while checking for repeated ideas.

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Install with agentmods
npx agentmods add skills/swan-gtm/gtm-skills/linkedin-ghostwriting
Install

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.

Any agent
npx skills add swan-gtm/gtm-skills --skill linkedin-ghostwriting
Clone the repo
git clone --depth 1 https://github.com/swan-gtm/gtm-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for linkedin-ghostwriting

README.md
[![agentmods](https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-ghostwriting/github.svg)](https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-ghostwriting)
Your own site
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-ghostwriting"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-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.

agentmods 80×15 button for linkedin-ghostwriting

Your own site · 80×15
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-ghostwriting"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-ghostwriting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 141 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,382 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00141 $0.01382
Opus 5 $0.00071 $0.00691
Sonnet 5 $0.00028 $0.00276
Haiku 4.5 $0.00014 $0.00138

Measured 8d ago against content hash c6d38d564215, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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 8d 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.

skills/gali-firon/linkedin-ghostwriting/SKILL.md · 47 lines

How it starts

The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Applies when ghostwriting a named executive's next LinkedIn posts. Produces three ranked, typed options per exec, each a few lines, each with an attachment, each cleared by an anti-repeat gate.

This skill exists to fix the two failure modes of exec ghostwriting: posts that repeat what the exec already said, and posts that read like marketing copy instead of the person. It runs in one pass and hands the exec ready-to-post options to approve by hand. It never posts anything.

Required inputs

Gather these before drafting. A thin input set produces generic posts.

  • A voice card per exec - their real diction, the arguments they own, what they will never say, and one or two of their best past posts. Build it from their actual posting history, not a guess. Format and starter questions are in references/voice-card-and-cadence.md.
  • A post ledger - every post that has gone out, with hook, core argument, attachment, and any performance note. This is what the anti-repeat gate reads.
  • A fresh signal feed - this week's news, competitor moves, earnings, and regulatory shifts on the exec's beat, plus repost candidates. Verify each peg's date at the primary source, not the commentary's date.

The play

  1. Read the history first. Before generating anything, read the ledger and the last several live posts: what argument, what attachment, what landed. This feeds the anti-repeat gate and keeps cadence honest.
  2. Mine the signal, then decide the angles. Read the whole signal feed and generate the angles from it. Treat any pre-suggested angle as one raw input, never the plan.
  3. Run the anti-repeat gate. Diff every candidate angle against the ledger and the exec's off-limits list. Kill or re-angle anything on the off-limits list, in the same argument family as a recent post, or inside the same-argument cooldown window. A rejected or deleted past option counts as a rejection - never re-serve it. The full gate logic, cooldown windows, and cadence targets are in references/anti-repeat-and-cadence.md.
  4. Lock the three types and the attachments. Assign one of each type per exec: a news share plus comment, a repost, and a thought-leadership piece with a graphic. For the graphic, recommend single image versus carousel and hand over a ready-to-paste image-generation prompt. The format gates, attachment rules, and image-prompt spec are in references/format-and-attachments.md.
  5. Check messenger and lane fit. The exec must plausibly follow that source and their audience must care about it - not merely "is the argument sound". Route the peg to the exec whose lane it is (regulatory to the policy voice, product to the builder, numbers to the finance voice). Two execs never comment on the same article.
  6. Draft in voice, then two finishing passes. Draft each option against the voice card and closest samples. Then strip AI tells to a clean baseline, and add one or two real human moves so the post reads as the person, not a clean bot. One or two moves, never invented detail.
  7. Score, rank, and deliver. Score each on hook strength and on commercial value (audience precision, proof, narrative fit, why-now). Below bar gets one revision. Deliver three ranked options per exec with attachments and, for the graphic, the image recommendation. Dependencies and posting-order notes travel with the delivery, never on the post itself.
  8. Close the loop. After the exec picks, edits, or posts, append the chosen post to the ledger - it re-enters play only after the cooldown window, never sooner. If they rewrote it, that rewrite becomes the new gold line - update the voice card. Any angle the exec killed or rejected goes to the off-limits map as a permanent block. (Ledger entries return after the cooldown; off-limits entries never do.)

Read the full file on GitHub · 47 lines

Files

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.

Changes

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.

  1. 8d ago First seen · 47 lines · 141 tokens per session scan A c6d38d564215

Subscribe to this mod's changes

linkedin-ghostwriting is a skill published in the GitHub repository swan-gtm/gtm-skills (148 stars, last pushed yesterday), licensed MIT. It adds 141 tokens to every session and 1,382 once invoked, about $0.0007 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 2026-09-03.

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