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 swan-gtm/gtm-skills --skill linkedin-ghostwritinggit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/linkedin-ghostwriting)<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.
<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>- 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 | $0.00141 | $0.01382 |
| Opus 5 | $0.00071 | $0.00691 |
| Sonnet 5 | $0.00028 | $0.00276 |
| Haiku 4.5 | $0.00014 | $0.00138 |
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.
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
- 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.
- 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.
- 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. - 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. - 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.
- 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.
- 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.
- 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.)
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.
- 8d ago First seen · 47 lines · 141 tokens per session scan A c6d38d564215
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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