CV Scanner Skill

    1

    Anonymized resume screening and evidence-based ranking with full audit trails for compliant hiring.

    $25

    Secure checkout via Stripe

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    Converts to your local currency at checkout

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    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    CV Scanner Skill

    Example session with this skill installed

    Screen these 50 CVs against the Senior DevOps Engineer rubric. Weight Kubernetes experience as a must-have. Provide a shortlist of 10 and an audit CSV.

    • Read your context and instructions
    • Compiled the cv scanner
    • Generated the data export

    Processing 50 files.
    Identities pseudonymized (C-0001 to C-0050).
    Scoring complete: 8 candidates 'shortlist_ready', 5 flagged for 'manual_review_must_have'.
    Generated: candidate_results.csv, criterion_evidence.csv.

    candidate_results.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Manual resume screening is prone to unconscious bias and inconsistent grading. Processing high volumes of CVs while maintaining an audit trail for compliance is slow and prone to human error.

    What it does

    • Anonymizes CVs by stripping protected identity characteristics before the scoring pass.
    • Extracts and validates candidate evidence against a structured, weighted scoring rubric.
    • Calculates confirmed score bounds and identifies candidates requiring manual oversight due to OCR quality or missing data.
    • Generates a spreadsheet-ready audit package including evidence logs, result CSVs, and a run manifest.
    • Ranks candidates based on verified evidence while flagging must-have gaps for human review.

    Frameworks & tools

    Python scripts for extraction and validation, YAML for rubric schemas, and CSV/JSON for auditable outputs.

    Why this beats prompting it yourself

    Simple prompts often hallucinate candidate details or fail to maintain strict anonymity. This skill enforces a structured pipeline that separates identity from evidence, uses deterministic IDs, and provides a multi-step audit trail that ad-hoc prompts lack.

    Use cases

    • Initial blind screening for high-volume recruitment cycles.
    • Compliance-focused hiring where scoring must be mapped to specific CV locators.
    • Standardizing candidate evaluation across multiple hiring managers using a shared rubric.
    • Auditing recruitment agency submissions against internal role requirements.

    Known limitations

    Requires an approved rubric and job description to begin. Cannot make final hiring decisions or send automated rejections.

    Requires pre-defined scoring rubrics. Does not automate final hiring decisions. Performance depends on OCR quality for image-based PDFs.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Fresh listing

    Recently published to Agensi

    Money-back guarantee

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    Trust & safety

    Security scan

    Security scan passed 3 months ago

    Software
    Python

    Permissions

    Terminal / Shell
    Listed3 months ago

    What's inside

    Frequently Asked Questions

    The skill uses a multi-step pipeline that strips protected identity characteristics before the scoring phase, ensuring candidates are evaluated solely on their skills and experience rather than demographic data.

    Upon purchase, you receive the full Python-based execution scripts, YAML schema templates for your rubrics, and detailed documentation for setting up the auditable workflow.

    This skill is designed to run in any environment that supports Python, making it compatible with local developer environments, custom AI agents, and enterprise HR automation platforms.

    While the skill accurately ranks candidates and flags evidence, it is designed as a decision-support tool; it cannot issue rejections or job offers, ensuring a human stays in the loop for final hiring choices.

    The skill generates a comprehensive audit package for every run, including a run manifest and CSV logs that map every score back to specific evidence found in the candidate\'s CV.

    New in Business & Operations