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 agentmods add skills/styfinity/linkedin-engine/linkedin-repurposernpx skills add styfinity/linkedin-engine --skill linkedin-repurposergit clone --depth 1 https://github.com/styfinity/linkedin-engineWhat 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 | $0.00056 | $0.00490 |
| Opus 5 | $0.00028 | $0.00245 |
| Sonnet 5 | $0.00011 | $0.00098 |
| Haiku 4.5 | $0.00006 | $0.00049 |
Grade A, and why
linkedin-repurposer 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 2d 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 Repurposer
One good asset is a week of content. This skill mines a single source for the five strongest ideas and shapes each into something the buyer reads.
Inputs
- The source asset (a call transcript, a doc, a win, a long post): $ARGUMENTS
- The brief (lanes, voice profile, offer, pains) loads automatically.
Do this
- Read the source. Pull the 5 strongest ideas: the ones that prove a competence the buyer pays for, not the ones that are merely interesting.
- Map each idea to a content lane from the brief and a hook formula (contrarian take, before/after, list, story open loop, hard number).
- Draft a post stub for each: lane, hook, one-line angle. Stub only, not the full post.
- Pull the single best idea into a carousel outline (title slide plus 5 to 7 slide lines).
- Pull one idea into a short DM nudge that opens a conversation off the back of the asset.
- Anonymise as you go: keep the real numbers and outcomes, drop every name (people, companies, clients). A number stays "a client" or "an operator".
Output
- Five post stubs, each labelled with its lane, hook formula, and angle.
- One carousel outline (title plus slide lines).
- One DM nudge (two short lines). End with a one-line note: run /linkedin-humanizer on any stub before drafting it full, and hand the DM nudge to /linkedin-first-dm if it targets a specific prospect.
Rules
- Anonymise everything. No real person, company, or client names. Keep the numbers, drop the source.
- Every piece must prove a competence the buyer buys. Cut the interesting-but-unsellable.
- Stubs and outlines only. Claude drafts, the operator approves and posts.
- No em-dashes.
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.
- 2d ago First seen · 34 lines · 56 tokens per session scan A 91499816d0c8
linkedin-repurposer is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 56 tokens to every session and 490 once invoked, about $0.0003 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 2026-08-31.
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