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-warm-trackernpx skills add styfinity/linkedin-engine --skill linkedin-warm-trackergit 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 | $0.00047 | $0.00519 |
| Opus 5 | $0.00023 | $0.00260 |
| Sonnet 5 | $0.00009 | $0.00104 |
| Haiku 4.5 | $0.00005 | $0.00052 |
Grade A, and why
linkedin-warm-tracker 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 Warm Tracker
The day-driver for warming and outreach. One row per person, one next action each, nothing leaks.
Inputs
- The current list of people in play with their last touch and last reply state (paste the list or give a path): $ARGUMENTS
- The brief (persona, offer, pains) loads automatically.
Do this
For each person:
- Read their stage: new / warming / connect-sent / accepted / in-conversation / qualifying / booked / cold.
- Read their last touch date and last reply state (replied, no reply yet, went quiet).
- Compute the single NEXT ACTION for that stage. Examples: warming = engage on a recent post; connect-sent (no accept, 4+ days) = withdraw or wait; accepted = send first DM; in-conversation = reply with the next angle; qualifying = book the call; went quiet (7+ days) = one soft re-open then mark cold.
- Compute the DUE date from the last touch plus the stage cadence. Anything overdue is due today.
- Sort the table by due date, soonest first, and within today put the warmest stage (qualifying, in-conversation) at the top.
Output
A table with columns: person, stage, last touch, NEXT ACTION, due. Sorted by due date. Below it, a one-line count of how many rows are due today. End with the single warmest row to action first.
Rules
- One row per person. Every person always gets a next action, even if that action is "mark cold". Nothing leaks.
- This drives the day. It does not send anything. Drafting a message is /linkedin-first-dm; running /linkedin-humanizer on the draft; hot replies go to /linkedin-reply-triager.
- When the action is to send, sending happens only through the connected CLI/MCP layer on the user's own session or provider key, drafts are shown and approved first, and the 20 actions/day cap applies to new accounts.
- No em-dashes. No guaranteed-result 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.
- 2d ago First seen · 31 lines · 47 tokens per session scan A ec4efd16d2f9
linkedin-warm-tracker is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 47 tokens to every session and 519 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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