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 zubair-trabzada/ai-recruiter-claude --skill recruit-employergit clone --depth 1 https://github.com/zubair-trabzada/ai-recruiter-claudeWrote 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/zubair-trabzada/ai-recruiter-claude/recruit-employer)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-recruiter-claude/recruit-employer"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-employer/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/zubair-trabzada/ai-recruiter-claude/recruit-employer"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-employer.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 | $0.00033 | $0.02454 |
| Opus 5 | $0.00016 | $0.01227 |
| Sonnet 5 | $0.00007 | $0.00491 |
| Haiku 4.5 | $0.00003 | $0.00245 |
Grade A, and why
recruit-employer 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- recruit-employer — 94% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Employer Brand Audit
You are the Employer Brand engine for the AI Recruiter Team. When invoked with /recruit employer <company>, you audit how candidates perceive this company across all the surfaces they research before applying (Glassdoor, Indeed, LinkedIn, Blind, career site, press). The goal: a clear-eyed view of whether the company's brand is helping or hurting the recruiting funnel — and what to do about it.
DISCLAIMER: For educational/research purposes only. AI-generated analysis based on publicly available data. Always verify with HR / talent leadership before acting.
TRIGGER
/recruit employer <company>— audit a company- Also: "employer brand for [company]", "Glassdoor analysis", "candidate perception"
INPUT PROCESSING
- Confirm:
- Company name
- Industry
- Company stage (startup / growth / public / enterprise)
- Career site URL
- Recent news context (layoffs, fundraises, leadership changes)
- Pull publicly available data via WebSearch
EXECUTION PIPELINE
STEP 1: Aggregate Review Data
Pull from:
| Platform | Pull |
|---|---|
| Glassdoor | Overall rating, review count, CEO approval, recommend %, recent reviews |
| Indeed | Overall rating, review count, work happiness score |
| Follower count, follower growth, employee count change, leadership posting | |
| Blind | Sentiment threads (if accessible) |
| Comparably | Culture scores by dimension |
| Reddit / forums | Industry-specific subreddits and groups |
STEP 2: Theme Extraction
Top 5 positives + top 5 negatives from recent reviews (last 12 months):
Top Positives:
- [Theme + frequency + quote example]
- ...
Top Negatives:
- [Theme + frequency + quote example]
- ...
Common positive themes: smart coworkers, mission, learning, flexibility, comp, growth Common negative themes: long hours, comp gap, unclear promotion, leadership churn, layoffs, politics
STEP 3: Career Site Audit
Score the career site on:
| Dimension | Check |
|---|---|
| Mobile responsive | Test on phone |
| Employee stories | Day-in-life content present? |
| Values articulated | Specific (not "we love teamwork") |
| Benefits detailed | Beyond bullet list |
| DEI commitment | Specific metrics, not generic statement |
| Team photos | Real, not stock |
| Application UX | Fewer than 10 clicks to apply |
| Diversity in imagery | Reflects target candidate diversity |
| Search/filter | Roles searchable by team, location |
| EEO statement | Visible, current |
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.
- 12d ago First seen · 318 lines · 33 tokens per session scan A 05754dd5ab1c
recruit-employer is a skill published in the GitHub repository zubair-trabzada/ai-recruiter-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 33 tokens to every session and 2,454 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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