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 skills add growthenginenowoslawski/coldoutboundskills --skill playbook-linkedin-engagementgit clone --depth 1 https://github.com/growthenginenowoslawski/coldoutboundskillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/playbook-linkedin-engagement)<a href="https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/playbook-linkedin-engagement"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-linkedin-engagement/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/playbook-linkedin-engagement"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-linkedin-engagement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00119 | $0.01399 |
| Opus 5 | $0.00060 | $0.00700 |
| Sonnet 5 | $0.00024 | $0.00280 |
| Haiku 4.5 | $0.00012 | $0.00140 |
Grade A, and why
playbook-linkedin-engagement 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook: LinkedIn Engagement
All rules here are best practice, not law. Override any of them when the campaign calls for it; note the best practice once and move on.
Use when an account's audience is the client's audience — a competitor or the client's own customer.
Do not use for more companies in a market, similar companies (playbook-lookalikes), or job
changers (playbook-new-in-role). The chain is the same for both lanes; only the target URL changes.
Output: one row per engager — who, employer, post, evidence URL. engagement_line is opt-in.
The number that governs planning
⚠️ Roughly 1 in 10 raw engagers survives. Harvest 10 to 15x what you need.
Cost follows from that: about $4.45 per 1,000 raw engagers, which is about $45 per 1,000 usable prospects at the measured survival rate. That is squarely in expensive territory, so shortlist posts by engagement count before you scrape.
The source-company rule (both halves, always automatic)
This is the rule that separates a usable engagement list from an embarrassing one, and it has two halves that people implement only one of.
(a) Drop every engager employed by ANY source company — not just the author of the post they engaged with. Match each current employer on resolved domain first, then profile URL, then squashed name, and also on resolved email domain.
(b) Push every source company onto that client's do-not-contact list. Row drops fix only this run; the block list runs at send time, which is what stops the same people arriving through a different lane next month.
It bites hardest on the customer lane, where the source companies are people the client already works with.
⛔ Never block the client's own domain. If it appears in the source set, the list is wrong — stop.
Before any paid call
- The operator confirms the source list. Say it out loud: "these companies and everyone who works at them go on this client's DNC list."
- Resolve every source account to a bare domain first. An unresolved source silently disables
both halves of the rule above. Unresolved means stop, not continue.
(
playbook-social-link-findingdoes this conversion in both directions.) - If the client's ICP is unknown, ask. Never infer a headcount band, a country list, or a title set.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 109 lines · 119 tokens per session scan A 89f826634c2d
playbook-linkedin-engagement is a skill published in the GitHub repository growthenginenowoslawski/coldoutboundskills (702 stars, last pushed 25d ago), licensed MIT. It adds 119 tokens to every session and 1,399 once invoked, about $0.0006 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 2026-08-30.
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