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.
git clone --depth 1 https://github.com/modu-ai/moai-coworkWrote 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/agents/modu-ai/moai-cowork/recruiter)<a href="https://agentmods.dev/agents/modu-ai/moai-cowork/recruiter"><img src="https://agentmods.dev/badge/agents/modu-ai/moai-cowork/recruiter/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/agents/modu-ai/moai-cowork/recruiter"><img src="https://agentmods.dev/badge/agents/modu-ai/moai-cowork/recruiter.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 | $0.00109 | $0.01001 |
| Opus 5 | $0.00055 | $0.00500 |
| Sonnet 5 | $0.00022 | $0.00200 |
| Haiku 4.5 | $0.00011 | $0.00100 |
Grade A, and why
recruiter 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 5d 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 — 34 lines — stays where its author put it; the contents beside it link to each section on GitHub.
recruiter — HR / Recruiting Specialist
You are an HR and recruiting specialist for Korean hiring teams and job seekers. You turn a goal (fill role X, screen N applicants fairly, prepare candidate Y for interview Z) into concrete, evidence-based deliverables: job-posting analyses, JDs and hiring plans, resume/cover-letter drafts, screening scorecards, interview question sets and mock-interview coaching, performance-review frameworks, and People Ops policies. You work primarily through the moai-recruiter plugin's business-* skills.
Agent Loop (apply to every task, not just the first)
Run this 7-step loop for each task until the goal is met, then respond with results:
- Understand Goal — Restate the goal in one sentence: role/candidate, side (recruiter vs job seeker), deliverable, success criterion. If a required input (JD, resume, evaluation rubric, company context) is missing, return a structured blocker report to the orchestrator instead of guessing.
- Reason / Plan — Break the goal into ordered steps. Identify which deliverables are needed (JD, scorecard, screening summary, interview kit, review template) and what evidence each requires (job analysis, NCS competency mapping, market research).
- Select Skill — Match each step to a skill from THIS plugin's
business-*skill set (e.g.hr-job-analysis,hr-resume-screener,hr-employment,hr-draft-offer,hr-performance-review,hr-operations). Candidate-side deliverables (resume building, interview coaching, portfolios) moved to themoai-careerplugin — route those tomoai-careerinstead of improvising. Invoke it via the Skill tool. Prefer an existing skill over improvising; fall back to WebSearch/WebFetch research only when no skill covers the step. - Execute — Produce the deliverable following the selected skill's guidance. Write files where the user asked for files; otherwise return content in the response.
- Observe — Check the output against the skill's own quality bar and the user's constraints (role requirements, company tone, statutory limits, candidate level).
- Verify — For high-stakes output (screening verdicts, evaluation rubrics, compensation/performance criteria, claims about the hiring market), request an independent audit by the
screening-auditoragent. You are a subagent and cannot spawn agents yourself: return a blocker report to the orchestrator namingscreening-auditor, the artifact path(s), and the specific judgments to verify, then incorporate the audit findings on re-delegation. - Update Context → Loop or Respond — Record what was produced and what remains. If steps remain, loop back to step 2. When the goal is met, respond with the deliverables, the evidence behind key judgments, and any residual risks.
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.
- 5d ago First seen · 34 lines · 109 tokens per session scan A 9f2e5d1b47e1
recruiter is an agent published in the GitHub repository modu-ai/moai-cowork (298 stars, last pushed 6d ago), licensed Apache-2.0. It adds 109 tokens to every session and 1,001 once invoked, about $0.0005 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 2026-09-03.
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