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 sandbaseai/sandbase-skills --skill linkedin-researchgit clone --depth 1 https://github.com/sandbaseai/sandbase-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/sandbaseai/sandbase-skills/linkedin-research)<a href="https://agentmods.dev/skills/sandbaseai/sandbase-skills/linkedin-research"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/linkedin-research/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/sandbaseai/sandbase-skills/linkedin-research"><img src="https://agentmods.dev/badge/skills/sandbaseai/sandbase-skills/linkedin-research.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 | $0.00047 | $0.00486 |
| Opus 5 | $0.00023 | $0.00243 |
| Sonnet 5 | $0.00009 | $0.00097 |
| Haiku 4.5 | $0.00005 | $0.00049 |
Grade A, and why
linkedin-research 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.
How it starts
The opening of the file, as written. The whole thing — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Research
LinkedIn professional intelligence through SandBase. Research companies, analyze professional profiles, track industry content, and monitor job markets. Read the API map before selecting a capability.
Call SandBase capabilities
For every selected tool, call sandbase_describe_tool first and use only arguments in its current input schema. Then call sandbase_call_tool with the exact tool_name.
Operating principles
- Use LinkedIn data for professional research and B2B intelligence only.
- Respect professional privacy — report on public information only.
- Preserve context: include company names, titles, dates, and engagement.
- Never attempt to connect, message, or apply on behalf of the user.
Workflow
1. Company research
Use linkedin_web_v2_company_profile for company details (size, industry, description, specialties).
Use linkedin_web_v2_company_posts for company content strategy and engagement.
2. Professional research
Use linkedin_web_v2_user_profile for professional background and current role.
Use linkedin_web_v2_user_posts for thought leadership and content activity.
3. Job market research
Use linkedin_web_v2_search_jobs to find open positions by keyword, location, or company.
Use linkedin_web_v2_job_detail for detailed job requirements and qualifications.
4. Content analysis
Use linkedin_web_v2_post_detail for specific post metrics.
Use linkedin_web_v2_post_comments for professional discourse and reactions.
Output
Return: company overview, team structure insights, content strategy analysis, job market signals, and competitive positioning.
Example tasks
- "Research [company] on LinkedIn — size, industry positioning, recent posts."
- "What is [person]'s professional background and current role?"
- "Find open [role] positions at companies in [industry] in [location]."
- "What content is [company] posting on LinkedIn? Analyze their strategy."
- "Compare hiring patterns between [company A] and [company B]."
What ships with it
1 file 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.
- 10d ago First seen · 54 lines · 47 tokens per session scan A bbc6326da7e6
linkedin-research is a skill published in the GitHub repository sandbaseai/sandbase-skills (145 stars, last pushed yesterday), licensed Apache-2.0. It adds 47 tokens to every session and 486 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 2026-08-30.
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