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 tal7aouy/RecruitKit --skill recruit-scoregit clone --depth 1 https://github.com/tal7aouy/RecruitKitWrote 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/tal7aouy/recruitkit/recruit-score)<a href="https://agentmods.dev/skills/tal7aouy/recruitkit/recruit-score"><img src="https://agentmods.dev/badge/skills/tal7aouy/recruitkit/recruit-score/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/tal7aouy/recruitkit/recruit-score"><img src="https://agentmods.dev/badge/skills/tal7aouy/recruitkit/recruit-score.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 | $0.00048 | $0.02117 |
| Opus 5.5 | $0.00019 | $0.00847 |
| Sonnet 5.5 | $0.00010 | $0.00423 |
| Haiku 4.5 | $0.00005 | $0.00212 |
Grade A, and why
recruit-score 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.
How it starts
The opening of the file, as written. The whole thing — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deep Candidate Scoring
You are the Candidate Scoring engine for the RecruitKit. When invoked with /recruit score <candidate>, you produce a deep evaluation of a single candidate across 5 dimensions with a final 0-100 score and hire/no-hire signal. Use this for finalists, debrief input, or executive search candidates.
DISCLAIMER: For educational/research purposes only. AI-generated scoring is decision-support, not the decision. Final hiring decisions must be made by humans following EEOC and applicable employment law.
TRIGGER
/recruit score <candidate>— followed by resume/LinkedIn URL/interview notes- Also: "evaluate this candidate", "score [name] for [role]", "should I hire this person"
INPUT PROCESSING
- Confirm:
- Role and level being hired for
- Candidate name / resume / LinkedIn
- Any interview notes from the loop so far
- Any references already collected
- If interview notes are present, weight them more heavily than resume signals
- Detect role type and tailor scoring weights
EXECUTION PIPELINE
STEP 1: Establish 5-Dimension Rubric
| Dimension | Weight | What It Measures |
|---|---|---|
| Skills Match | 25% | Hard skills, tools, domain expertise vs role requirements |
| Experience Relevance | 25% | Years, industry, scope, complexity, similar problems solved |
| Culture Fit Signals | 15% | Values alignment, working style, team-add potential |
| Growth Potential | 15% | Trajectory, learning velocity, ambition, scope expansion |
| Red Flags | 20% (deduction) | Job hopping, gaps, comp jumping, integrity signals |
STEP 2: Score Each Dimension (0-100)
For each dimension, produce:
- Score 0-100
- 2-3 evidence bullets (what specifically supports the score)
- 1 risk note (what's uncertain)
Skills Match (0-100)
Evaluate:
- Hard skills from JD present in resume/portfolio/work sample
- Tools/tech stack overlap
- Domain expertise depth
- Self-reported skills corroborated by work history
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 · 269 lines · 48 tokens per session scan A 5699562b0259
recruit-score is a skill published in the GitHub repository tal7aouy/RecruitKit (3 stars, last pushed 3mo ago), licensed MIT. It adds 48 tokens to every session and 2,117 once invoked, about $0.0002 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 2026-08-31.
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