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 tuanductran/hr-skills --skill hr-social-recruitinggit clone --depth 1 https://github.com/tuanductran/hr-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/tuanductran/hr-skills/hr-social-recruiting)<a href="https://agentmods.dev/skills/tuanductran/hr-skills/hr-social-recruiting"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-social-recruiting/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/tuanductran/hr-skills/hr-social-recruiting"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-social-recruiting.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 | $0.00110 | $0.02982 |
| Opus 5 | $0.00055 | $0.01491 |
| Sonnet 5 | $0.00022 | $0.00596 |
| Haiku 4.5 | $0.00011 | $0.00298 |
Grade A, and why
hr-social-recruiting 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 9d 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 — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HR social media recruiting
Strategic and tactical guidance for recruiting through social media platforms and online communities—from sourcing passive candidates and mastering platform mechanics to building recruiter brand, creating employer branding campaigns, and designing multi-touch outreach sequences.
Supported tasks
- Understanding modern social recruiting strategy and platform selection
- Sourcing passive candidates on LinkedIn, GitHub, Reddit, and Discord
- Building personal recruiter brand and credibility across platforms
- Designing employer branding campaigns and recruiting content calendars
- Writing effective recruiter outreach messages and connection requests
- Creating Boolean search strategies and X-Ray search queries
- Recruiting through niche communities and industry-specific platforms
- Building recruiting funnels and candidate pipelines
- Avoiding spam detection and platform penalties
- Measuring recruiting effectiveness and optimizing sourcing workflows
- Conducting multi-touch outreach sequences and follow-up strategies
- Analyzing recruiter outreach performance and fixing response rates
Social recruiting fundamentals
Modern social recruiting differs from traditional job posting:
- Passive candidate sourcing is more effective than active posting for senior roles
- Recruiter personal brand drives response rates more than company brand alone
- Community trust and participation matter more than broadcasting messages
- Platform algorithms favor authentic engagement over mass messaging
- Sourcing and relationship building happen before job posting
- Candidate experience on social channels impacts employer brand perception
Effective recruiting through social requires:
- building genuine relationships with candidates
- participating authentically in communities
- establishing recruiting expertise and credibility
- creating valuable content that attracts talent
- understanding platform-specific mechanics and limitations
- respecting candidate privacy and communication preferences
- measuring quality of hire, not just activity metrics
What ships with it
12 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.
- content/understanding-social-recruiting-for-modern-hiring.md 12 KB
- examples/avoiding-linkedin-spam-restrictions.md 12 KB
- examples/building-a-complete-social-recruiting-strategy-for-a-startup.md 14 KB
- examples/building-an-employer-brand-on-linkedin.md 10 KB
- examples/hiring-ai-engineers-through-github-and-linkedin.md 11 KB
- examples/hiring-engineers-through-facebook-groups.md 12 KB
- examples/improving-recruiter-response-rates.md 9.5 KB
- examples/recruiting-a-senior-backend-engineer-through-linkedin.md 14 KB
- examples/recruiting-developers-through-discord-reddit-and-slack-communities.md 12 KB
- examples/recruiting-in-vietnam-using-zalo.md 11 KB
- examples/recruiting-passive-candidates.md 10 KB
- prompts/social-recruiting-prompts.md 1.1 KB
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.
- 9d ago First seen · 256 lines · 110 tokens per session scan A bbe152806574
hr-social-recruiting is a skill published in the GitHub repository tuanductran/hr-skills (57 stars, last pushed 2d ago), licensed MIT. It adds 110 tokens to every session and 2,982 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 2026-09-03.
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