# AU Resume vs Job Description - Skill Gap Analyser (`data_lattice_au/data-cv-jd-skill-gap-analyser`) Actor

Close the gap between your CV and your dream AU data role. Compares any data, analytics, BI, ML or AI resume against a target JD and returns covered skills, missing skills, and concrete TF-IDF-ranked amendments to add. .docx, .pdf, .txt or .md .

- **URL**: https://apify.com/data\_lattice\_au/data-cv-jd-skill-gap-analyser.md
- **Developed by:** [data\_lattice](https://apify.com/data_lattice_au) (community)
- **Categories:** Jobs
- **Stats:** 0 total users, 0 monthly users, 100.0% runs succeeded, NaN 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/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):

```bash
npm install apify-client
```

In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):

```bash
pip install apify-client
```

In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):

````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash

In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).

If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).


# README

## Data Jobs - CV vs JD Skill Gap (AU)

Close the gap between your CV and your dream data role. Compares any
analytics, BI, ML or AI resume against a target JD and returns covered
skills, missing skills, coverage percentage, semantic similarity and
TF-IDF-ranked amendment suggestions.

### What this is for

The cheaper, lighter sibling of the ATS Scorer. Where the ATS Scorer
runs nine rubrics and gives you weighted scores, this Actor answers the
single most actionable question:

> **Of every skill the JD asks for, which ones does my CV demonstrably
> cover, and which are missing?**

Output is a clean coverage breakdown plus a ranked amendment list
("adding `dbt` would move your coverage from 64% to 71%; here's the
predicted score uplift").

### How this helps you

| You are a... | You use this to... |
|---|---|
| Job seeker | Get a plain-English skill gap report before applying - know which 3-4 keywords to add |
| LinkedIn / resume coach | Generate concrete amendment suggestions instead of vague advice |
| Career-tech app | Power a "score my fit" feature in your product without building skill-extraction infrastructure |
| Bootcamp curriculum designer | Show students exactly what skills the JD wants vs what their portfolio currently covers |

### What you get back

```jsonc
{
  "jd_skill_count": 22,                     // distinct skills detected in JD
  "cv_skill_count": 31,                     // distinct skills detected in CV
  "covered": ["Python","SQL","AWS","Spark","..."],
  "gaps":    ["dbt","Airflow","Snowflake"],  // demanded but missing
  "coverage_pct": 64.0,                      // covered / jd_skill_count
  "semantic_cosine_pct": 71.3,               // CV vs JD meaning similarity
  "amendments": [
    {"term": "dbt", "uplift": +0.08, "reason": "JD mentions 3x"},
    ...
  ]
}
````

Full schema in `OUTPUT_SCHEMA.json`.

### Pricing

**$0.05 per run** (PAY\_PER\_EVENT, fired as `gap_analysed` once per
analysis). Job-seekers typically run 5-20 analyses over a search → less
than $1 of usage. Half the per-run price of the full ATS Scorer because
it runs lighter compute.

### CV input formats

Provide ONE of:

| Field | When |
|---|---|
| `cv_url` | Hosted publicly (S3, Dropbox, GitHub raw). URL must end in `.docx`, `.pdf`, `.txt` or `.md` |
| `cv_base64` + `cv_format` | Calling from a script. Base64-encode the file, set `cv_format` to `docx`/`pdf`/`txt`/`md` |
| `cv_text` | Quick paste. Plain text, no formatting required |

### Run it

```json
{
  "jd_text": "Senior Analytics Engineer (Snowflake + dbt + Airflow)...",
  "cv_url": "https://github.com/me/cv/raw/main/cv.pdf"
}
```

### Brand

Part of the [Data Lattice](https://datalattice.com.au) portfolio. Pairs
with the [ATS Scorer](https://apify.com/data_lattice_au/data-cv-ats-scorer)
when you want full per-rubric scores instead of a single coverage %.

# Actor input Schema

## `cv_file` (type: `string`):

Upload your resume from your computer. Accepts .docx, .pdf, .txt, or .md. Pair with the 'CV file type' dropdown below so the parser knows the format.

## `cv_filetype` (type: `string`):

Select the file format you uploaded above. Required when 'Upload CV file' is used.

## `cv_url` (type: `string`):

Public link to the CV. Must end in .docx, .pdf, .txt or .md.

## `cv_text` (type: `string`):

Paste the resume body as text. Used when neither file upload nor URL is provided.

## `jd_text` (type: `string`):

Full job description text to compare the CV against. Required.

## Actor input object example

```json
{
  "cv_filetype": "docx"
}
```

# Actor output Schema

## `jd_skill_count` (type: `string`):

Distinct skills detected in the JD.

## `cv_skill_count` (type: `string`):

Distinct skills detected in the CV.

## `covered` (type: `string`):

Full list of skills present in both CV and JD (technical + soft + qualifications).

## `covered_technical` (type: `string`):

Technical skills covered (tools, languages, ML/data concepts) - the meaningful match-quality signal.

## `covered_soft` (type: `string`):

Soft / behavioural skills covered (communication, leadership, agile, etc).

## `covered_qualifications` (type: `string`):

Formal qualifications covered (Bachelor, Masters, PhD).

## `gaps` (type: `string`):

Full list of skills the JD demands but the CV is missing.

## `gaps_technical` (type: `string`):

Technical skills missing from the CV - real upskilling work.

## `gaps_soft` (type: `string`):

Soft skills missing from the CV - usually a rephrasing problem rather than a real gap.

## `gaps_qualifications` (type: `string`):

Formal qualifications missing from the CV.

## `amendments` (type: `string`):

TF-IDF-ranked amendment suggestions, sorted by predicted score uplift across all categories.

## `technical_amendments` (type: `string`):

Subset of `amendments` containing only technical terms - prioritise these for highest impact.

## `soft_amendments` (type: `string`):

Subset of `amendments` containing only soft-skill terms.

## `qualification_amendments` (type: `string`):

Subset of `amendments` containing formal qualifications.

## `coverage_pct` (type: `string`):

Headline coverage % across ALL skills (technical + soft + qualifications).

## `coverage_technical_pct` (type: `string`):

Coverage % computed against technical skills only - the metric that actually predicts ATS pass-through.

## `semantic_cosine_pct` (type: `string`):

Semantic similarity % between CV and JD text (TF-IDF cosine).

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("data_lattice_au/data-cv-jd-skill-gap-analyser").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("data_lattice_au/data-cv-jd-skill-gap-analyser").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{}' |
apify call data_lattice_au/data-cv-jd-skill-gap-analyser --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=data_lattice_au/data-cv-jd-skill-gap-analyser",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

```json
{
    "openapi": "3.0.1",
    "info": {
        "title": "AU Resume vs Job Description - Skill Gap Analyser",
        "description": "Close the gap between your CV and your dream AU data role. Compares any data, analytics, BI, ML or AI resume against a target JD and returns covered skills, missing skills, and concrete TF-IDF-ranked amendments to add. .docx, .pdf, .txt or .md .",
        "version": "0.48",
        "x-build-id": "jprhKRQa4JbhPx1u8"
    },
    "servers": [
        {
            "url": "https://api.apify.com/v2"
        }
    ],
    "paths": {
        "/acts/data_lattice_au~data-cv-jd-skill-gap-analyser/run-sync-get-dataset-items": {
            "post": {
                "operationId": "run-sync-get-dataset-items-data_lattice_au-data-cv-jd-skill-gap-analyser",
                "x-openai-isConsequential": false,
                "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
                "tags": [
                    "Run Actor"
                ],
                "requestBody": {
                    "required": true,
                    "content": {
                        "application/json": {
                            "schema": {
                                "$ref": "#/components/schemas/inputSchema"
                            }
                        }
                    }
                },
                "parameters": [
                    {
                        "name": "token",
                        "in": "query",
                        "required": true,
                        "schema": {
                            "type": "string"
                        },
                        "description": "Enter your Apify token here"
                    }
                ],
                "responses": {
                    "200": {
                        "description": "OK"
                    }
                }
            }
        },
        "/acts/data_lattice_au~data-cv-jd-skill-gap-analyser/runs": {
            "post": {
                "operationId": "runs-sync-data_lattice_au-data-cv-jd-skill-gap-analyser",
                "x-openai-isConsequential": false,
                "summary": "Executes an Actor and returns information about the initiated run in response.",
                "tags": [
                    "Run Actor"
                ],
                "requestBody": {
                    "required": true,
                    "content": {
                        "application/json": {
                            "schema": {
                                "$ref": "#/components/schemas/inputSchema"
                            }
                        }
                    }
                },
                "parameters": [
                    {
                        "name": "token",
                        "in": "query",
                        "required": true,
                        "schema": {
                            "type": "string"
                        },
                        "description": "Enter your Apify token here"
                    }
                ],
                "responses": {
                    "200": {
                        "description": "OK",
                        "content": {
                            "application/json": {
                                "schema": {
                                    "$ref": "#/components/schemas/runsResponseSchema"
                                }
                            }
                        }
                    }
                }
            }
        },
        "/acts/data_lattice_au~data-cv-jd-skill-gap-analyser/run-sync": {
            "post": {
                "operationId": "run-sync-data_lattice_au-data-cv-jd-skill-gap-analyser",
                "x-openai-isConsequential": false,
                "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
                "tags": [
                    "Run Actor"
                ],
                "requestBody": {
                    "required": true,
                    "content": {
                        "application/json": {
                            "schema": {
                                "$ref": "#/components/schemas/inputSchema"
                            }
                        }
                    }
                },
                "parameters": [
                    {
                        "name": "token",
                        "in": "query",
                        "required": true,
                        "schema": {
                            "type": "string"
                        },
                        "description": "Enter your Apify token here"
                    }
                ],
                "responses": {
                    "200": {
                        "description": "OK"
                    }
                }
            }
        }
    },
    "components": {
        "schemas": {
            "inputSchema": {
                "type": "object",
                "required": [
                    "jd_text"
                ],
                "properties": {
                    "cv_file": {
                        "title": "Upload CV file",
                        "type": "string",
                        "description": "Upload your resume from your computer. Accepts .docx, .pdf, .txt, or .md. Pair with the 'CV file type' dropdown below so the parser knows the format."
                    },
                    "cv_filetype": {
                        "title": "CV file type",
                        "enum": [
                            "docx",
                            "pdf",
                            "txt",
                            "md"
                        ],
                        "type": "string",
                        "description": "Select the file format you uploaded above. Required when 'Upload CV file' is used.",
                        "default": "docx"
                    },
                    "cv_url": {
                        "title": "CV URL (alternative)",
                        "type": "string",
                        "description": "Public link to the CV. Must end in .docx, .pdf, .txt or .md."
                    },
                    "cv_text": {
                        "title": "CV plain text (alternative)",
                        "type": "string",
                        "description": "Paste the resume body as text. Used when neither file upload nor URL is provided."
                    },
                    "jd_text": {
                        "title": "Job description text",
                        "type": "string",
                        "description": "Full job description text to compare the CV against. Required."
                    }
                }
            },
            "runsResponseSchema": {
                "type": "object",
                "properties": {
                    "data": {
                        "type": "object",
                        "properties": {
                            "id": {
                                "type": "string"
                            },
                            "actId": {
                                "type": "string"
                            },
                            "userId": {
                                "type": "string"
                            },
                            "startedAt": {
                                "type": "string",
                                "format": "date-time",
                                "example": "2025-01-08T00:00:00.000Z"
                            },
                            "finishedAt": {
                                "type": "string",
                                "format": "date-time",
                                "example": "2025-01-08T00:00:00.000Z"
                            },
                            "status": {
                                "type": "string",
                                "example": "READY"
                            },
                            "meta": {
                                "type": "object",
                                "properties": {
                                    "origin": {
                                        "type": "string",
                                        "example": "API"
                                    },
                                    "userAgent": {
                                        "type": "string"
                                    }
                                }
                            },
                            "stats": {
                                "type": "object",
                                "properties": {
                                    "inputBodyLen": {
                                        "type": "integer",
                                        "example": 2000
                                    },
                                    "rebootCount": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "restartCount": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "resurrectCount": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "computeUnits": {
                                        "type": "integer",
                                        "example": 0
                                    }
                                }
                            },
                            "options": {
                                "type": "object",
                                "properties": {
                                    "build": {
                                        "type": "string",
                                        "example": "latest"
                                    },
                                    "timeoutSecs": {
                                        "type": "integer",
                                        "example": 300
                                    },
                                    "memoryMbytes": {
                                        "type": "integer",
                                        "example": 1024
                                    },
                                    "diskMbytes": {
                                        "type": "integer",
                                        "example": 2048
                                    }
                                }
                            },
                            "buildId": {
                                "type": "string"
                            },
                            "defaultKeyValueStoreId": {
                                "type": "string"
                            },
                            "defaultDatasetId": {
                                "type": "string"
                            },
                            "defaultRequestQueueId": {
                                "type": "string"
                            },
                            "buildNumber": {
                                "type": "string",
                                "example": "1.0.0"
                            },
                            "containerUrl": {
                                "type": "string"
                            },
                            "usage": {
                                "type": "object",
                                "properties": {
                                    "ACTOR_COMPUTE_UNITS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATASET_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATASET_WRITES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "KEY_VALUE_STORE_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "KEY_VALUE_STORE_WRITES": {
                                        "type": "integer",
                                        "example": 1
                                    },
                                    "KEY_VALUE_STORE_LISTS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "REQUEST_QUEUE_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "REQUEST_QUEUE_WRITES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATA_TRANSFER_INTERNAL_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATA_TRANSFER_EXTERNAL_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "PROXY_SERPS": {
                                        "type": "integer",
                                        "example": 0
                                    }
                                }
                            },
                            "usageTotalUsd": {
                                "type": "number",
                                "example": 0.00005
                            },
                            "usageUsd": {
                                "type": "object",
                                "properties": {
                                    "ACTOR_COMPUTE_UNITS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATASET_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATASET_WRITES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "KEY_VALUE_STORE_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "KEY_VALUE_STORE_WRITES": {
                                        "type": "number",
                                        "example": 0.00005
                                    },
                                    "KEY_VALUE_STORE_LISTS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "REQUEST_QUEUE_READS": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "REQUEST_QUEUE_WRITES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATA_TRANSFER_INTERNAL_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "DATA_TRANSFER_EXTERNAL_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
                                        "type": "integer",
                                        "example": 0
                                    },
                                    "PROXY_SERPS": {
                                        "type": "integer",
                                        "example": 0
                                    }
                                }
                            }
                        }
                    }
                }
            }
        }
    }
}
```