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 ishandutta2007/Awesome-Agent-Skills --skill linkedingit clone --depth 1 https://github.com/ishandutta2007/Awesome-Agent-SkillsWrote 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/ishandutta2007/awesome-agent-skills/linkedin)<a href="https://agentmods.dev/skills/ishandutta2007/awesome-agent-skills/linkedin"><img src="https://agentmods.dev/badge/skills/ishandutta2007/awesome-agent-skills/linkedin/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/ishandutta2007/awesome-agent-skills/linkedin"><img src="https://agentmods.dev/badge/skills/ishandutta2007/awesome-agent-skills/linkedin.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 | $0.00039 | $0.04517 |
| Opus 5 | $0.00019 | $0.02259 |
| Sonnet 5 | $0.00008 | $0.00903 |
| Haiku 4.5 | $0.00004 | $0.00452 |
Grade A, and why
linkedin 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 10d 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.
This is a copy
92% identical to linkedin — 40 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 550 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Skill
Overview
Use this skill to operate linkedin, a CLI tool for LinkedIn automation. Use it to fetch profiles, search people and companies, send messages, manage connections, create posts, react, comment, and more.
Each command sends a request to Linked API, which runs a real cloud browser to perform the action on LinkedIn. Operations are not instant - expect 30 seconds to several minutes depending on complexity.
When to Use
- Fetch public profile, company, post, or connection data from LinkedIn.
- Search people or companies with structured filters.
- Send messages, connection requests, posts, reactions, or comments for an authenticated account.
- Run a custom LinkedIn workflow when the built-in commands are not enough.
If linkedin is not available, install it:
npm install -g @linkedapi/linkedin-cli
Authentication
If a command fails with exit code 2 (authentication error), ask the user to set up their account:
- Go to app.linkedapi.io and sign up or log in
- Connect their LinkedIn account
- Copy the Linked API Token and Identification Token from the dashboard
Once the user provides the tokens, run:
linkedin setup --linked-api-token=TOKEN --identification-token=TOKEN
Global Flags
Always use --json and -q for machine-readable output:
LINKEDAPI_CLIENT=skill:linkedin linkedin <command> --json -q
When using this skill, run every linkedin ... example below with the
LINKEDAPI_CLIENT=skill:linkedin prefix so Linked API can attribute usage to the skill.
| Flag | Description |
|---|---|
--json |
Structured JSON output |
--quiet / -q |
Suppress stderr progress messages |
--fields name,url,... |
Select specific fields in output |
--no-color |
Disable colors |
--account "Name" |
Use a specific account for this command |
Output Format
Success:
{"success": true, "data": {"name": "John Doe", "headline": "Engineer"}}
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.
- 10d ago First seen · 550 lines · 39 tokens per session scan A 7f625630ed17
linkedin is a skill published in the GitHub repository ishandutta2007/Awesome-Agent-Skills (21 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 4,517 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to linkedin, differing in 40 lines, and is treated as a copy.
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