# Resume vs Job Description Match Score (explainable) (`anamika67/resume-jd-match-score`) Actor

Use when a recruiting agent or ATS needs to score a resume against a job description: 0-100 fit, matched and missing must-have skills, experience fit and interview questions for gaps. Explainable, skills-only, bias-safe; resume never stored.

- **URL**: https://apify.com/anamika67/resume-jd-match-score.md
- **Developed by:** [Anamika Singh](https://apify.com/anamika67) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $50.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Resume vs Job Description Match Score — explainable, skills-only

> **Stop your AI from guessing.** AI resume screening is a black box and gets accused of bias. This tool returns a sourced, structured answer in seconds.

**Use when:** a recruiting agent or ATS needs to score a resume against a job description: 0-100 fit, matched and missing must-have skills, experience fit and interview questions for gaps. Explainable, skills-only, bias-safe; resume never stored.

For **recruiting AI agents, ATS integrations and hiring teams**: send a resume and a job description as text, get back:

- **Score 0–100** and verdict: `STRONG_MATCH` (75+), `POSSIBLE_MATCH` (55–74), `WEAK_MATCH`
- **Must-have and nice-to-have skills**: matched vs missing (read from the JD's Requirements / Nice-to-have sections)
- **Experience fit**: years from job date ranges (education lines ignored) vs years required
- **Gaps** and **interview questions** for missing or thinly evidenced skills, with what a strong answer sounds like

### Why this one

- **Explainable**: every point of the score traces to a listed skill or the experience rule. No black box.
- **Fair by design**: scores only skills and experience. Never name, age, gender, photo, caste, religion, marital status, location or college.
- **Private**: resume text is processed in memory, never stored, never returned. Output contains skill names and numbers only.
- **Built by a recruiter** who places senior AI/ML, MLOps and DevOps engineers.

### Input

```json
{ "resume_text": "…", "job_description": "…" }
```

### Output (example)

```json
{ "score": 85, "verdict": "STRONG_MATCH",
  "summary": "5/6 must-have skills, 1/2 nice-to-have; experience 8.3 years vs 5+ required.",
  "must_have": { "matched": ["Python","Kubernetes","Docker","CI/CD","GitHub Actions"], "missing": ["Terraform"] },
  "interview_questions": [{ "skill": "Terraform", "question": "Tell me about the last real project where you used Terraform…", "strong_answer": "…" }] }
```

### Scoring

Must-have skills 65%, nice-to-have 15%, experience 20% (must-have 80% when the JD has no nice-to-have list). ~130 tech skills with common aliases (EKS → Kubernetes, PyTorch → Deep Learning).

### Important

Decision support for a human recruiter — **not an automated hiring decision**. Plain text only in this version.

### Pricing

Pay per match. Invalid input is not charged.

# Actor input Schema

## `resume_text` (type: `string`):

Full resume text. Processed in memory only; never stored or returned.

## `job_description` (type: `string`):

Full JD, ideally with Requirements and Nice-to-have sections.

## Actor input object example

```json
{
  "resume_text": "Skills: Python, Docker, Kubernetes, GitHub Actions, MLflow\nMLOps Engineer, Jan 2021 - Present\nData Engineer, Jun 2018 - Dec 2020",
  "job_description": "Requirements:\n- 5+ years of experience\n- Python, Kubernetes, Docker, Terraform\nNice to have:\n- Kafka, MLflow"
}
```

# Actor output Schema

## `results` (type: `string`):

The match result.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "resume_text": `Skills: Python, Docker, Kubernetes, GitHub Actions, MLflow
MLOps Engineer, Jan 2021 - Present
Data Engineer, Jun 2018 - Dec 2020`,
    "job_description": `Requirements:
- 5+ years of experience
- Python, Kubernetes, Docker, Terraform
Nice to have:
- Kafka, MLflow`
};

// Run the Actor and wait for it to finish
const run = await client.actor("anamika67/resume-jd-match-score").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "resume_text": """Skills: Python, Docker, Kubernetes, GitHub Actions, MLflow
MLOps Engineer, Jan 2021 - Present
Data Engineer, Jun 2018 - Dec 2020""",
    "job_description": """Requirements:
- 5+ years of experience
- Python, Kubernetes, Docker, Terraform
Nice to have:
- Kafka, MLflow""",
}

# Run the Actor and wait for it to finish
run = client.actor("anamika67/resume-jd-match-score").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "resume_text": "Skills: Python, Docker, Kubernetes, GitHub Actions, MLflow\\nMLOps Engineer, Jan 2021 - Present\\nData Engineer, Jun 2018 - Dec 2020",
  "job_description": "Requirements:\\n- 5+ years of experience\\n- Python, Kubernetes, Docker, Terraform\\nNice to have:\\n- Kafka, MLflow"
}' |
apify call anamika67/resume-jd-match-score --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,anamika67/resume-jd-match-score"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/QzQtqakgdH30gdlX7/builds/asn7bbVThKBoL61QR/openapi.json