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 pnp/sharepoint-skills --skill resume-screening-summarizergit clone --depth 1 https://github.com/pnp/sharepoint-skillsWrote 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/pnp/sharepoint-skills/resume-screening-summarizer)<a href="https://agentmods.dev/skills/pnp/sharepoint-skills/resume-screening-summarizer"><img src="https://agentmods.dev/badge/skills/pnp/sharepoint-skills/resume-screening-summarizer/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/pnp/sharepoint-skills/resume-screening-summarizer"><img src="https://agentmods.dev/badge/skills/pnp/sharepoint-skills/resume-screening-summarizer.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 | $0.00000 | $0.02073 |
| Opus 5.5 | $0.00000 | $0.00829 |
| Sonnet 5.5 | $0.00000 | $0.00415 |
| Haiku 4.5 | $0.00000 | $0.00207 |
Grade A, and why
resume-screening-summarizer 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 7d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume Screening Summarizer
Purpose
Hiring teams often collect dozens of resumes for an open role in a SharePoint library, then manually re-read each one against the job description. This skill reads the job description and every resume/application in a specified library, extracts each candidate's relevant qualifications, scores them against the job description's must-have and nice-to-have criteria, and produces a ranked, self-contained HTML summary the hiring team can use to build a shortlist. It is strictly read-only: it never contacts candidates, edits resumes, or writes anything back except the final report.
Trigger Phrases
Activate this skill when the user says any of the following (or close variations):
- "screen these resumes" / "screen resumes for this role"
- "rank candidates for this role" / "rank these applicants"
- "shortlist applicants" / "build a shortlist"
- "match resumes to the job description"
- "resume screening" / "application screening"
- "review applications for this position"
- "which candidates best fit this job description"
Inputs & Scope
Determine the scope from the user's request:
- Job description — the user must point to or paste a job description (a document in the library, a page, or pasted text). If none can be found, ask the user to provide one before proceeding — do not guess at requirements.
- Resume library (default) — the current document library or folder, scoped to files that are clearly resumes/applications (common formats: .pdf, .docx, .doc; skip unrelated files).
- Named library or folder — if the user names a specific library or folder, resolve and scope to that instead.
If the resume library contains only a handful of files or an unusually large number (50+), note that in the report rather than silently truncating results.
Steps
Step 1 — Resolve the job description
Locate and read the job description. Extract:
- Job title
- Must-have requirements (skills, years of experience, certifications, education, location/work authorization if stated)
- Nice-to-have / preferred qualifications
- Any explicit disqualifiers stated in the posting (e.g., "must be willing to relocate")
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.
- 7d ago First seen · 156 lines · 0 tokens per session scan A 65b2900af1ef
resume-screening-summarizer is a skill published in the GitHub repository pnp/sharepoint-skills (137 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,073 tokens. 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 2026-10-10.
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