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 jared833/claude-code-hooks --skill linkedin-engagegit clone --depth 1 https://github.com/jared833/claude-code-hooksWrote 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/jared833/claude-code-hooks/linkedin-engage)<a href="https://agentmods.dev/skills/jared833/claude-code-hooks/linkedin-engage"><img src="https://agentmods.dev/badge/skills/jared833/claude-code-hooks/linkedin-engage/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/jared833/claude-code-hooks/linkedin-engage"><img src="https://agentmods.dev/badge/skills/jared833/claude-code-hooks/linkedin-engage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00099 | $0.02012 |
| Opus 5.5 | $0.00040 | $0.00805 |
| Sonnet 5.5 | $0.00020 | $0.00402 |
| Haiku 4.5 | $0.00010 | $0.00201 |
Grade A, and why
linkedin-engage 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 1mo 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn engagement session
Goal: grow Jared's LinkedIn presence (linkedin.com/in/jaredhebb) by commenting with substance, not broadcasting. As of 2026-09-25 LinkedIn is a posting channel again, for authority with the Role Build buyer (owners and managers of firms of roughly 20 to 249 staff), and comments are what put his name in front of that buyer between posts. Claude drafts; Jared posts by hand. This is the LinkedIn twin of the x-engage skill and uses the same Engage app; the only API difference is platform: "linkedin" in the session payload.
NO BROWSER AUTOMATION ON LINKEDIN (hard rule, 2026-07-19)
Never drive Jared's logged-in LinkedIn account through Claude in Chrome (or any browser tool): no opening his feed, no scrolling/harvesting, no auto-posting comments, no clicking Follow/Connect. LinkedIn is even more aggressive than X about automation detection, and browser-driven sessions put his reach at risk. Every outward action is Jared's hands only; Claude's job here is draft-and-hand-off. Holds until we pay for a compliant LinkedIn API path (first revenue spend). Do not reintroduce browser control before then; if a session seems to need it, stop and ask.
Session flow
-
Start the Engage app (
<HOME>\projects\engage). Checkhttp://localhost:3220/api/health; if it is not up, runnpm run devthere in the background and wait for the health check to pass. -
Get the raw material from Jared. He pastes the post URLs (or screenshots) he wants to comment on, and his current follower/following counts if he wants them tracked. Claude does not open LinkedIn to find posts. Topic lanes he can search himself: small-firm operations and office admin work (the Role Build buyer's own feed: owners in wholesale distribution, freight brokerage, property management, accounting and bookkeeping, insurance and wealth management, title and escrow, law), AI adoption in small businesses, Claude Code / AI coding agents, IT leadership. Prefer the first lane: a useful comment on an owner's post about a swamped office seat does more for Role Build than one more comment in a builder thread. WebFetch on a public post URL for context is fine; driving his account is not.
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.
- 1mo ago Changed · +1 lines · +11 tokens per session a11acd8c4738
- 2mo ago First seen · 63 lines · 88 tokens per session scan A 610ff741a8b9
linkedin-engage is a skill published in the GitHub repository jared833/claude-code-hooks (2 stars, last pushed 4d ago), licensed MIT. It adds 99 tokens to every session and 2,012 once invoked, about $0.0004 per session on Opus 5.5. 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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