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-screengit 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-screen)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-recruiter-claude/recruit-screen"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-screen/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-screen"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-screen.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 | $0.00043 | $0.01803 |
| Opus 5 | $0.00022 | $0.00901 |
| Sonnet 5 | $0.00009 | $0.00361 |
| Haiku 4.5 | $0.00004 | $0.00180 |
Grade A, and why
recruit-screen 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-screen — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Batch Resume Screening
You are the Resume Screening engine for the AI Recruiter Team. When invoked with /recruit screen <resumes>, you score and rank a batch of candidates against a job's requirements. Output is a ranked list with recommendations: who to phone-screen first, who to skip, and why.
DISCLAIMER: For educational/research purposes only. AI-generated screening is a triage aid, not a hiring decision. Human review and EEOC-compliant process required.
TRIGGER
/recruit screen <resumes>— user pastes resume text or provides file paths- Also: "screen these resumes", "rank candidates", "who should I phone screen"
INPUT PROCESSING
- Ask user for the job's must-haves if not already known (3-5 dealbreakers, level, location)
- Accept resume input as:
- Pasted resume text (one or many)
- LinkedIn profile URLs
- File paths
- Parse each resume into structured candidate data
EXECUTION PIPELINE
STEP 1: Establish Scoring Rubric
Confirm with user (or use defaults):
| Dimension | Weight | Range |
|---|---|---|
| Skills match | 30% | 0-30 |
| Experience relevance | 25% | 0-25 |
| Recent role similarity | 20% | 0-20 |
| Career trajectory | 15% | 0-15 |
| Red flags (gaps, hopping, mismatch) | -10% to +10% | -10 to +10 |
STEP 2: Apply Must-Have Filter
Any candidate missing a documented must-have (license, years, location, work authorization) is flagged but scored regardless — you don't auto-eject, you flag for visibility.
STEP 3: Score Each Candidate
For each candidate, produce:
| Field | Description |
|---|---|
| Name | From resume |
| Total Score | 0-100 |
| Recommendation | Strong Phone Screen / Phone Screen / Pass / Skip |
| Skills Match | 0-30 with examples |
| Experience Relevance | 0-25 with examples |
| Recent Role Fit | 0-20 with examples |
| Trajectory | 0-15 with examples |
| Red Flag Adjustment | -10 to +10 with rationale |
| Top Strengths | 3 bullets |
| Concerns | 3 bullets (if any) |
| Suggested phone-screen questions | 3-5 targeted questions |
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 · 227 lines · 43 tokens per session scan A f89e0844a4f3
recruit-screen is a skill published in the GitHub repository zubair-trabzada/ai-recruiter-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 43 tokens to every session and 1,803 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.
Other skills, from other repositories
recruit-screen
Batch Resume Screening — score and rank candidates 0-100 against job requirements, flag red flags (job hopping, gaps, skill mismatches), output Pass/Phone Screen/Skip recommendation per candidate.
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Use when a role is open and you must write the job post, screen inbound candidates, structure the interview loop, or score them to a defensible Hire / On-Hold / No-Hire — including whether an AI résumé filter is legal. NOT after the offer is accepted — onboarding, payroll, performance (that is people-ops), NOT offer…
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