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 Comradery64/open-greenhouse-mcp --skill greenhouse-resume-batch-reviewgit clone --depth 1 https://github.com/Comradery64/open-greenhouse-mcpWrote 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/comradery64/open-greenhouse-mcp/greenhouse-resume-batch-review)<a href="https://agentmods.dev/skills/comradery64/open-greenhouse-mcp/greenhouse-resume-batch-review"><img src="https://agentmods.dev/badge/skills/comradery64/open-greenhouse-mcp/greenhouse-resume-batch-review/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/comradery64/open-greenhouse-mcp/greenhouse-resume-batch-review"><img src="https://agentmods.dev/badge/skills/comradery64/open-greenhouse-mcp/greenhouse-resume-batch-review.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 | $0.00157 | $0.00802 |
| Opus 5 | $0.00078 | $0.00401 |
| Sonnet 5 | $0.00031 | $0.00160 |
| Haiku 4.5 | $0.00016 | $0.00080 |
Grade A, and why
greenhouse-resume-batch-review 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 11d 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 — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Greenhouse resume batch review
Reads through a whole batch of resumes for a requisition and produces a ranked shortlist with reasoning, so the recruiter doesn't have to open every application individually.
When this applies
Trigger when the user wants a sweep across all (or most) candidates for a role, with no specific filter criteria — the goal is "help me find the best of everyone who applied," not "find people who match X." If they give you a specific filter (a skill, years of experience, location), that's greenhouse-pipeline-search instead — it's a much cheaper and more precise operation than reading every resume.
How to do it
- Confirm the scope: which job/requisition, and whether "everyone" means all-time applicants or a specific window (e.g. this round of applications). If there could be hundreds of applicants, mention that up front and ask if they want the full batch or a recent slice — reading hundreds of resumes is slow and burns a lot of the conversation's budget for not much extra signal once you're past the first couple hundred.
- Use
scan_pipeline_resumes, scoped to the job/requisition, to pull the batch efficiently rather than fetching resumes one at a time. - Read each resume for genuine signal relative to the role — not just keyword presence. A resume that lists a skill in a bullet point is weaker evidence than one that shows real depth (years of hands-on use, a relevant project, seniority appropriate to the role).
- Rank candidates into a shortlist, and briefly explain why each shortlisted person made the cut. For everyone not shortlisted, a short rollup is enough — the recruiter doesn't need individual writeups for people who clearly aren't a fit.
Output format
## Resume batch review — [job/requisition] (N resumes reviewed)
### Shortlist (top candidates)
1. **[Name]** — [why they stand out, specific to the role]
2. **[Name]** — [why they stand out]
...
### Also reviewed, not shortlisted (N)
[Brief rollup — e.g. "Most lacked required X experience or were clearly junior for a senior-level req."]
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.
- 11d ago First seen · 40 lines · 157 tokens per session scan A f1bc92ae53ee
greenhouse-resume-batch-review is a skill published in the GitHub repository Comradery64/open-greenhouse-mcp (0 stars, last pushed 8d ago), licensed MIT. It adds 157 tokens to every session and 802 once invoked, about $0.0008 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 2026-08-31.
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