greenhouse-resume-batch-review

greenhouse-resume-batch-review is a skill for Claude Code, Codex from Comradery64/open-greenhouse-mcp. It costs 157 tokens per session (802 once invoked), scanned A, original, MIT.

A tool-assisted process for reading every applicant's resume for a Greenhouse job opening and producing a ranked shortlist. Greenhouse is a recruiting system for managing job applications.

In plain words
What is it for?
Use it for a full-batch review of applicants for one job, with reasons for the ranking. It is not intended for searches based on specific filters.
Why use it?
It removes the need to open and compare a large set of applications one by one when the goal is to review everyone.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for a full-batch review of applicants for one job, with reasons for the ranking. It is not intended for searches based on specific filters.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/comradery64/open-greenhouse-mcp/greenhouse-resume-batch-review
Install

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.

Any agent
npx skills add Comradery64/open-greenhouse-mcp --skill greenhouse-resume-batch-review
Clone the repo
git clone --depth 1 https://github.com/Comradery64/open-greenhouse-mcp

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for greenhouse-resume-batch-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/comradery64/open-greenhouse-mcp/greenhouse-resume-batch-review/github.svg)](https://agentmods.dev/skills/comradery64/open-greenhouse-mcp/greenhouse-resume-batch-review)
Your own site
<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.

agentmods 80×15 button for greenhouse-resume-batch-review

Your own site · 80×15
<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>
Per session 157 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 802 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash f1bc92ae53ee, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

skills/greenhouse-resume-batch-review/SKILL.md · 40 lines

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

  1. 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.
  2. Use scan_pipeline_resumes, scoped to the job/requisition, to pull the batch efficiently rather than fetching resumes one at a time.
  3. 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).
  4. 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."]

Read the full file on GitHub · 40 lines

Changes

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.

  1. 11d ago First seen · 40 lines · 157 tokens per session scan A f1bc92ae53ee

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens