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 tang-vu/social-posting-skills --skill post-linkedingit clone --depth 1 https://github.com/tang-vu/social-posting-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/tang-vu/social-posting-skills/post-linkedin)<a href="https://agentmods.dev/skills/tang-vu/social-posting-skills/post-linkedin"><img src="https://agentmods.dev/badge/skills/tang-vu/social-posting-skills/post-linkedin/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/tang-vu/social-posting-skills/post-linkedin"><img src="https://agentmods.dev/badge/skills/tang-vu/social-posting-skills/post-linkedin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00016 | $0.00434 |
| Opus 5.5 | $0.00006 | $0.00174 |
| Sonnet 5.5 | $0.00003 | $0.00087 |
| Haiku 4.5 | $0.00002 | $0.00043 |
Grade A, and why
post-linkedin 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 1mo 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.
What it actually says
LinkedIn Adapter
Facts live in platforms/linkedin.json (3000-char body, media bounds,
publish modes). Do not duplicate numbers here.
What LinkedIn rewards (culture)
- First-person, specific, professional narrative. "I built X and learned Y."
- The first line decides the "see more" click — lead with the outcome.
- Short paragraphs and line breaks; mobile readability matters.
- Document carousels and native media over external links.
- Genuine expertise signaled through specifics, not adjectives.
Drafting from the content graph
core-claim→ opening line (the scroll-stopper).context→ 1–2 lines of why/who.proof/technical→ arrow-point bullets with the strongest specifics.cta→ closing ask + engagement question.- Link → first comment (community practice; flagged as heuristic in data).
- Disclosures read natively: "I built this", "Open source project".
AI-content caution
LinkedIn's feed visibly deprioritizes generic AI-template text. Write in a real voice: concrete details, an actual opinion, a small imperfection. Human review before publishing is mandatory anyway — use it.
Publishing
manual→ draft package; user pastes. LinkedIn ToS restricts automation — this adapter deliberately offers no browser mode.api→ official Posts API exists (OAuth app required); adapter not implemented. Env contract documented inplatforms/linkedin.json.
Genuine engagement
- Comment on others' posts with real substance (LinkedIn surfaces long, substantive comments).
- Reply to every comment on your post with actual answers.
- Never mass-tag people for reach; never DM-blast.
Pitfalls
- Hashtags: a few relevant ones at the bottom; more looks spammy.
- "Link in comments" is strategy folklore, not a documented penalty — the data labels it honestly.
What ships with it
1 file 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.
- 1mo ago Changed · -179 lines · +1 tokens per session 1da7bebc9226
- 1mo ago First seen · 232 lines · 15 tokens per session scan A f877328096bb
post-linkedin is a skill published in the GitHub repository tang-vu/social-posting-skills (12 stars, last pushed 22d ago), licensed MIT. It adds 16 tokens to every session and 434 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
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