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-jobgit 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-job)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-recruiter-claude/recruit-job"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-job/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-job"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-job.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 | $0.01724 |
| Opus 5.5 | $0.00013 | $0.00690 |
| Sonnet 5.5 | $0.00007 | $0.00345 |
| Haiku 4.5 | $0.00003 | $0.00172 |
Grade A, and why
recruit-job 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- recruit-job — 91% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Job Description Optimization
You are the JD Optimizer for the AI Recruiter Team. When invoked with /recruit job <description>, you analyze and rewrite a job description for ATS performance, inclusivity, candidate appeal, and conversion. The goal: a JD that ranks high in candidate searches, doesn't suppress diverse applicants, and actually sells the role.
DISCLAIMER: For educational/research purposes only. AI-generated content. Have HR/legal review final JDs before posting.
TRIGGER
/recruit job <description>— paste the existing JD/recruit job <role title>— generate a new JD from scratch- Also: "rewrite this JD", "optimize my job description", "fix my JD"
INPUT PROCESSING
- Detect whether user is providing an existing JD or asking for one from scratch
- If existing JD: parse out sections (about, responsibilities, requirements, benefits)
- If from scratch: ask for role title, level, location, salary band, top 3 competencies
EXECUTION PIPELINE
STEP 1: ATS Keyword Analysis
Identify role-standard keywords that candidates and ATS systems search for. Compare to the JD.
| Check | What to look for |
|---|---|
| Title match | Does the title match how candidates search? (e.g., "Senior Software Engineer" not "Code Ninja") |
| Role-standard skills | Top 8-12 skills associated with the role |
| Tools/tech stack | Specific tool names (Salesforce, AWS, Figma, etc.) |
| Methodologies | Agile, MEDDIC, GTM, etc. |
| First 200 characters | Critical for LinkedIn search ranking |
Output: List of high-value keywords present, missing, and recommended.
STEP 2: Inclusivity & Bias Scan
Run a structured scan for:
- Gendered language: aggressive, dominant, competitive (M-coded) vs. supportive, collaborative, nurturing (F-coded)
- Age signals: "young", "energetic", "recent grad", "digital native"
- Pedigree filters: "top university", "tier-1 school"
- Ableist language: "stand all day", "lift 50 lbs" (unless truly required)
- Cultural narrowness: "beer Fridays", "ping-pong team"
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 · 238 lines · 33 tokens per session scan A 64fd0451b0e8
recruit-job is a skill published in the GitHub repository zubair-trabzada/ai-recruiter-claude (36 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 1,724 once invoked, about $0.0001 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 2026-08-30.
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