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 styfinity/linkedin-engine --skill linkedin-engage-plangit clone --depth 1 https://github.com/styfinity/linkedin-engineWrote 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/styfinity/linkedin-engine/linkedin-engage-plan)<a href="https://agentmods.dev/skills/styfinity/linkedin-engine/linkedin-engage-plan"><img src="https://agentmods.dev/badge/skills/styfinity/linkedin-engine/linkedin-engage-plan/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/styfinity/linkedin-engine/linkedin-engage-plan"><img src="https://agentmods.dev/badge/skills/styfinity/linkedin-engine/linkedin-engage-plan.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 | $0.00038 | $0.00529 |
| Opus 5.5 | $0.00015 | $0.00212 |
| Sonnet 5.5 | $0.00008 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
linkedin-engage-plan 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.
How it starts
The opening of the file, as written. The whole thing — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Engage Plan
Warming is recognition before the message. This skill lays out a conditional, stop-on-reply arc that earns familiarity first, so the eventual DM lands on a name they already know, never a cold pitch.
Inputs
- The target (name, role, company) and how warm they already are (never seen you, follow each other, you've commented before, a mutual connection): $ARGUMENTS
- The brief (persona, pains, offer, voice) loads automatically.
Do this
- Read the starting warmth. Skip steps they're already past. A target who follows you starts further along than a stranger.
- Lay out a day-by-day arc, each step conditional on the prior one landing and on no reply yet:
- Day 1: react to a recent, relevant post of theirs. Specific, not the latest by default.
- Day 2: leave a real value-add comment on their content. Hand the draft to /linkedin-comment-warmer.
- Day 3: send a connection note that references the prior touch. Hand to /linkedin-connection-note.
- Day 4+ (only after the connection is accepted): a first DM. Hand to /linkedin-first-dm only once the connection is accepted.
- Set the stop conditions plainly: the moment they reply or engage back, stop the arc and move to a real conversation.
- Note the gates: each step waits on the prior one. No connection note before a comment has landed; no DM before the connection is accepted.
Output
A day-by-day arc table: day, action, the skill to run for that step, and the stop/advance condition. End with the single next touch to make today and what has to be true before the step after it.
Rules
- Warm before you message. Recognition first, pitch never in the arc itself.
- Stop the moment they reply. Every step is conditional on no reply yet.
- This skill drafts and plans only. Nothing here sends automatically.
- Any sending happens through the connected CLI/MCP layer wired to your own LinkedIn session, drafts shown first, explicit approval required, hard-capped at 20 actions per day for new accounts.
- No em-dashes. No guaranteed-result or fast-growth promises.
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 First seen · 34 lines · 38 tokens per session scan A a422c4de1576
linkedin-engage-plan is a skill published in the GitHub repository styfinity/linkedin-engine (9 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 529 once invoked, about $0.0002 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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