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 ArabelaTso/Skills-4-SE --skill linkedingit clone --depth 1 https://github.com/ArabelaTso/Skills-4-SEWrote 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/arabelatso/skills-4-se/linkedin)<a href="https://agentmods.dev/skills/arabelatso/skills-4-se/linkedin"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/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/arabelatso/skills-4-se/linkedin"><img src="https://agentmods.dev/badge/skills/arabelatso/skills-4-se/linkedin.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 | $0.00039 | $0.04166 |
| Opus 5.5 | $0.00016 | $0.01666 |
| Sonnet 5.5 | $0.00008 | $0.00833 |
| Haiku 4.5 | $0.00004 | $0.00417 |
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 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 — 516 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Skill
You have access to 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.
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"}}
Error:
{"success": false, "error": {"type": "personNotFound", "message": "Person not found"}}
Exit code 0 means the API call succeeded – always check the success field for the action outcome. Non-zero exit codes indicate infrastructure errors:
| Exit Code | Meaning |
|---|---|
| 0 | Success (check success field – action may have returned an error like "person not found") |
| 1 | General/unexpected error |
| 2 | Missing or invalid tokens |
| 3 | Subscription/plan required |
| 4 | LinkedIn account issue |
| 5 | Invalid arguments |
| 6 | Rate limited |
| 7 | Network error |
| 8 | Workflow timeout (workflowId returned for recovery) |
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 First seen · 516 lines · 39 tokens per session scan A c44a28e14f22
linkedin is a skill published in the GitHub repository ArabelaTso/Skills-4-SE (259 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 4,166 once invoked, about $0.0002 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 2026-09-03.
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skill-code-quality
This skill should be used when implementing code quality standards, setting up linting/testing/security scans, enforcing coverage thresholds, or establishing CI/CD guardrails.
skill-pr-review
Comprehensive code review for pull requests using parallel multi-agent analysis. Audits CLAUDE.md compliance, checks for bugs, analyzes git history, reviews comments, and filters by confidence score. Use when reviewing a GitHub PR, mentions "code review", "review this PR", or "/code-review".
teach
Teach the user a new skill or concept, within this workspace.
improve-codebase-architecture
Find deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI-navigable.
skill-developer-relations
SDK development, external developer communication, code samples, API client libraries, community engagement, and developer experience. Use when creating SDKs, writing external guides, creating code samples, or improving developer experience.
diagnose
Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.