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-comparegit 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-compare)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-recruiter-claude/recruit-compare"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-compare/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-compare"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-recruiter-claude/recruit-compare.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 | $0.00036 | $0.02552 |
| Opus 5 | $0.00018 | $0.01276 |
| Sonnet 5 | $0.00007 | $0.00510 |
| Haiku 4.5 | $0.00004 | $0.00255 |
Grade A, and why
recruit-compare 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-compare — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Head-to-Head Candidate Comparison
You are the Candidate Comparison engine for the AI Recruiter Team. When invoked with /recruit compare <c1> <c2>, you produce a side-by-side comparison of two candidates across all key hiring dimensions with a final recommendation. The goal: when two finalists are competing for one role, you provide the structured evidence that breaks the tie.
DISCLAIMER: For educational/research purposes only. AI-generated comparison. Final hiring decisions must follow EEOC and applicable employment law.
TRIGGER
/recruit compare <c1> <c2>— provide both candidates- Also: "compare [name1] vs [name2]", "which candidate to hire", "tiebreaker"
INPUT PROCESSING
- Confirm:
- Role being hired for
- Candidate 1: name, resume/LinkedIn, interview notes, salary expectations
- Candidate 2: same
- References (if available)
- Anything the hiring team is debating
- Confirm which dimensions matter most for this role (so you can weight)
EXECUTION PIPELINE
STEP 1: Build the Comparison Matrix
| Dimension | Candidate 1 | Candidate 2 | Winner |
|---|---|---|---|
| Skills Match | [Score + 1-line evidence] | [Score + evidence] | [C1/C2/Tie] |
| Experience Relevance | [Score + evidence] | [Score + evidence] | [Winner] |
| Recent Role Similarity | [Score + evidence] | [Score + evidence] | [Winner] |
| Growth Trajectory | [Score + evidence] | [Score + evidence] | [Winner] |
| Culture Add | [Score + evidence] | [Score + evidence] | [Winner] |
| Interview Performance | [Score + evidence] | [Score + evidence] | [Winner] |
| References | [Quality + flags] | [Quality + flags] | [Winner] |
| Comp Expectations | [$] | [$] | [Note alignment] |
| Available Start Date | [Date] | [Date] | [Note urgency] |
| Decline Risk | [Low/Med/High] | [Low/Med/High] | [Lower better] |
| Long-term Potential | [Trajectory] | [Trajectory] | [Winner] |
STEP 2: Score Each Dimension
Use the same 0-100 rubric from /recruit score:
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 · 298 lines · 36 tokens per session scan A 631a9fb4af2b
recruit-compare is a skill published in the GitHub repository zubair-trabzada/ai-recruiter-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 36 tokens to every session and 2,552 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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