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-signal-stacknpx skills add styfinity/linkedin-engine --skill linkedin-signal-stackgit 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 | $0.00049 | $0.00515 |
| Opus 5 | $0.00024 | $0.00258 |
| Sonnet 5 | $0.00010 | $0.00103 |
| Haiku 4.5 | $0.00005 | $0.00052 |
Grade A, and why
linkedin-signal-stack 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 Signal Stack
The lift in outreach is not the message, it is the signal stack underneath it. This skill does the homework: five-plus real signals per target, distilled to the one sharpest thing to open on.
Inputs
- A target (profile and/or company) to research: $ARGUMENTS
- The brief (ICP, voice, offer, pains) loads automatically, so the signals are filtered for what matters to this operator.
Do this
- Gather signals that matter on this one target. Aim for five or more from across:
- Recent funding, raise, or revenue milestone.
- An exec move or a new hire (especially in growth, sales, or rev ops).
- A phrase or priority lifted straight from a current job description they have open.
- Their last post (the hook, the claim, the question they raised).
- Any company news from the last 14 days (launch, press, partnership, hiring spike).
- Verify each one. If a signal cannot be checked against a real, dated source, leave it out. No guessing, no inference dressed as fact.
- Stack them into a 3-line signal brief: what is going on, why it is happening now, and the angle it opens.
- Pick the single sharpest signal: the one a peer would notice and the target would feel seen by.
Output
A signal brief per target, labelled clearly: the five-plus verified signals (each with its source), then the 3-line brief, then "Open on:" with the single sharpest signal. Hand this to a message-writing skill like /linkedin-first-dm or /linkedin-connection-note.
Rules
- Only real, checkable signals. Every one carries its source. If it cannot be verified, it does not go in.
- Drafts research only. Pulling signals live (profiles, posts, company pages) needs the connected CLI/MCP layer wired to the operator's own session or a provider key; nothing is scraped by default.
- The operator approves what gets used. This skill never sends.
- No em-dashes. No filler. Operator-grade signals only.
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 · 34 lines · 49 tokens per session scan A e16c9c2f584c
linkedin-signal-stack is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 515 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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