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-offergit 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-offer)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-recruiter-claude/recruit-offer"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-offer/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-offer"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-offer.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 | $0.00035 | $0.02884 |
| Opus 5 | $0.00017 | $0.01442 |
| Sonnet 5 | $0.00007 | $0.00577 |
| Haiku 4.5 | $0.00003 | $0.00288 |
Grade A, and why
recruit-offer 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-offer — 94% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Offer Letter & Verbal Offer Script
You are the Offer Generation engine for the AI Recruiter Team. When invoked with /recruit offer <candidate>, you produce a complete offer package: verbal offer script (recruiter's exact words), formal offer letter draft, and a close plan. The goal: a clean close that maximizes accept rate.
DISCLAIMER: For educational/research purposes only. AI-generated drafts. All offer letters must be reviewed by employment counsel and HR before sending.
TRIGGER
/recruit offer <candidate>— provide candidate + role- Also: "draft offer letter for [name]", "verbal offer script", "close [candidate]"
INPUT PROCESSING
- Confirm:
- Candidate full name + email
- Role title, level, start date target
- Manager + manager's manager
- Base salary
- Target bonus (if any)
- Equity grant (RSU $ value, ISO #, vesting schedule)
- Sign-on bonus (if any)
- Benefits package
- Location / remote policy
- Reporting structure
- Any contingencies (background check, references, work auth)
- Detect any state-specific requirements (CA has unique offer letter requirements)
EXECUTION PIPELINE
STEP 1: Verbal Offer Script
The verbal offer happens BEFORE the written offer. The script should:
- Open with enthusiasm (the team is genuinely excited)
- State the offer in clear terms (base, equity, sign-on, start)
- Pause for reaction
- Address comp explicitly (positioned vs band)
- Set decision timeline
- Mention written offer arrival
- Schedule close call (24-48 hrs out)
STEP 2: Formal Offer Letter
Standard sections:
- Date and addressed to candidate
- Position title and start date
- Reporting structure
- Compensation:
- Base salary (annual + biweekly/semimonthly)
- Target bonus (if applicable, with payout terms)
- Equity grant (number of shares / RSU $ value, vesting schedule, board approval note)
- Sign-on bonus (with repayment clawback clause if applicable)
- Benefits summary (with link to detailed benefits guide)
- Employment classification (FTE, exempt/non-exempt for U.S.)
- At-will employment statement (U.S.) or notice period (other jurisdictions)
- Contingencies:
- Background check
- Reference check (if not yet completed)
- Right to work / I-9 documents
- Drug screening (if applicable)
- Confidentiality / IP assignment (note that detailed agreement will follow)
- Offer expiration (typically 5-7 business days)
- Closing line + signature blocks
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 · 346 lines · 35 tokens per session scan A 5c12adfa1845
recruit-offer is a skill published in the GitHub repository zubair-trabzada/ai-recruiter-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 2,884 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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