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-first-dmnpx skills add styfinity/linkedin-engine --skill linkedin-first-dmgit 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 | $0.00038 | $0.00469 |
| Opus 5 | $0.00019 | $0.00234 |
| Sonnet 5 | $0.00008 | $0.00094 |
| Haiku 4.5 | $0.00004 | $0.00047 |
Grade A, and why
linkedin-first-dm 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 First DM
The accept is not the win, the conversation is. This skill writes the opener that earns a reply without asking for anything.
Inputs
- The accepted target and the hook that connected you (a post, a comment thread, a mutual, a shared signal): $ARGUMENTS
- The brief (persona tone, pains, offer) loads automatically.
Do this
Write two first-DM variants so the operator can pick:
- Variant A - value-first. Reference the real hook, give one genuine, specific observation about their world, then end on an open question.
- Variant B - genuinely curious. Lead with curiosity about something specific to them, no observation needed, then end on a different open question. Both must be UNDER 150 characters. Count every character. Ask for nothing: no call, no offer, no link, no pitch. End each on an open question they would actually want to answer.
Output
Variant A and Variant B, each labelled with its exact character count. After both, a one-line note: stop-on-reply (do not send a follow-up until they reply). If the operator wants the message smoothed before sending, run /linkedin-humanizer on the draft. When replies land, hand them to /linkedin-reply-triager.
Rules
- Under 150 characters, counted and shown. If over, cut and recount.
- No ask, no pitch, no link. Value or curiosity only.
- Every variant ends on an open question about their world.
- No em-dashes, no exclamation marks, no "hope you're well", no "I came across your profile".
- Reads like a text from a peer, references one real, specific hook.
- Draft only. Claude drafts, the operator approves. Nothing is sent without explicit approval, and sending happens only through the connected CLI/MCP layer wired to your own LinkedIn session or a provider key, hard-capped at 20 actions per day for new accounts.
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 · 32 lines · 38 tokens per session scan A 18db231f26b7
linkedin-first-dm is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 38 tokens to every session and 469 once invoked, about $0.0002 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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