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-outreachnpx skills add styfinity/linkedin-engine --skill linkedin-outreachgit 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 | $0.00051 | $0.00528 |
| Opus 5 | $0.00026 | $0.00264 |
| Sonnet 5 | $0.00010 | $0.00106 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
linkedin-outreach 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 Outreach
The hero outbound skill. Find the right decision-makers, write a short human connection note for each, and send only what the operator approves, inside the ban-safe daily cap.
Inputs
- Target title(s), seniority, geography, and how many: $ARGUMENTS
- The brief (ICP, voice profile, offer, pains) loads automatically.
Do this
- Search for matching decision-makers, live via the connected MCP layer if it is wired, or from a pasted or seed list if not. State which source you used.
- Filter the results to the brief's ICP. Drop anyone outside the title, seniority, or geography rules. Say who you cut and why.
- For each person, draft a connection note UNDER 200 characters that references one real specific detail (a post they wrote, a recent role move, a shared context). Count the characters and show the count.
- Present the whole queue as DRAFTS for approval. Nothing leaves yet.
- On explicit approval, send through the CLI's send command. It hard-caps at 20 per day for new accounts and logs every send. If approval covers more than today's remaining 20, send what fits and hold the rest.
Output
- The drafted queue: each row is target (name, role, company), the under-200-char note with its character count, and the real detail referenced.
- After approval: a send summary - sent count, remaining in today's 20, and what was held for tomorrow.
- For people who accept the request, hand the thread to /linkedin-reply-triager and run /linkedin-humanizer on any reply draft.
Rules
- Nothing sends without the connected CLI/MCP layer AND explicit approval. Draft-first, always.
- The 20-per-day cap is a ban-safety guardrail for new accounts. Do not exceed it.
- Every note references one real, specific detail. No generic "I'd love to connect" or "great to connect with people in your space".
- No em-dashes. No exclamation marks. No fake familiarity.
- Real numbers are fine, but never name the person or company they came from.
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 · 33 lines · 51 tokens per session scan A 12da63025622
linkedin-outreach is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 528 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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