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-list-buildernpx skills add styfinity/linkedin-engine --skill linkedin-warm-list-buildergit 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 | $0.00046 | $0.00483 |
| Opus 5 | $0.00023 | $0.00242 |
| Sonnet 5 | $0.00009 | $0.00097 |
| Haiku 4.5 | $0.00005 | $0.00048 |
Grade A, and why
linkedin-warm-list-builder 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 List Builder
Warming is wasted on the wrong people. This skill assembles the right ones, scores them against the ICP, and sorts so the warmest sit at the top, because viral and signal warmth both decay fast.
Inputs
- Any seed lists, search filters, or signals to weight (recent posters, role changes, mutuals, event attendees): $ARGUMENTS
- The ICP, pains, and offer load automatically from the brief.
Do this
- Assemble candidates. If the connected layer is available, run a live search off the filters. Otherwise work off the pasted or seed lists. Do not scrape.
- Score each against the ICP. Use a 0 to 10 fit score on role, company size, and pain match. Drop anything under the floor the brief implies.
- Attach the strongest why-now signal to each: a recent post, a role change, a funding event, a comment thread, a mutual. One signal per target, the freshest one.
- Sort warmest to coldest. A strong recent signal beats a marginally higher fit score, because warmth decays and fit does not.
- Suggest a first touch per target: engage on the post, comment, or a connect via /linkedin-connection-note.
Output
A ranked table: name, title, company, ICP score (0 to 10), why-now signal (with the date), suggested first touch. Top of the table is who to warm today. End with a one-line note on which rows are signal-fresh (act this week) versus evergreen.
Rules
- Live search needs the connected CLI/MCP layer. Without it, work off seed lists only. No scraping by default.
- This is a list to warm, not a list to blast. Building it sends nothing.
- No em-dashes. Every why-now signal must be real and dated, not invented.
- If a target has no genuine why-now signal, mark it evergreen, do not fabricate one.
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 · 30 lines · 46 tokens per session scan A ec0759b31189
linkedin-warm-list-builder is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 483 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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