# My Actor (`learning-edge/my-actor`) Actor

- **URL**: https://apify.com/learning-edge/my-actor.md
- **Developed by:** [Learning-Edge](https://apify.com/learning-edge) (community)
- **Categories:** AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

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

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## A Practical Route From Notes to Finished Work

Strong academic work depends on decisions made before the first polished sentence appears. Read the assignment carefully and identify its actual demand: explain, compare, analyze, evaluate, or defend a position. Check the word limit, source requirements, citation style, formatting rules, and assessment criteria. These details determine how much evidence you need and how deeply each point must be developed.

A workable topic is specific enough to investigate. A subject such as environmental policy may be far too broad, while a question about how one policy affected a particular group during a defined period gives the research a clearer boundary. Narrowing the subject also makes it easier to recognize irrelevant material.

Use a simple research system from the beginning:

1. Record the full details of every useful source.
2. Write the author's central claim in your own notes.
3. Separate direct quotations from paraphrases.
4. Add a brief note explaining how each source could support your argument.
5. Mark questions or contradictions that deserve further investigation.

This approach prevents research from becoming an endless collection of copied passages. It also makes citations easier to manage when the draft becomes longer.

An outline should show the logic of the paper rather than merely repeat its required headings. Begin with the main argument, then arrange the claims that support it. Under each claim, identify the evidence you expect to use and explain its purpose. If a section contains several unrelated ideas, divide them before drafting. If a planned section has no convincing evidence, return to the research instead of filling the gap with general statements. The structure can change when new evidence changes your understanding of the topic.

Paragraphs should not all look alike. A short paragraph can emphasize a precise point, while a longer paragraph may need to introduce evidence, interpret it, consider another explanation, and connect the discussion to the central argument. Let the idea determine the paragraph break. Uniform paragraph sizes can create a predictable rhythm that makes complicated reasoning harder to follow.

Evidence becomes useful only when its relevance is explained. A quotation does not establish a claim simply because an expert said it. A statistic can reveal a pattern while leaving its causes uncertain. After presenting evidence, explain what it demonstrates, how strongly it supports the claim, and whether another interpretation deserves attention. When sources disagree, compare their methods, assumptions, definitions, and contexts before deciding which position is more convincing.

Introductions need direction rather than decoration. Give readers enough background to understand the issue, identify the specific question being examined, and present a thesis that makes a defensible claim. Avoid opening with broad observations that could belong to almost any paper. The body should then develop the thesis through a sequence of connected claims instead of turning into a list of source summaries.

Scientific assignments require especially careful interpretation. Students preparing the [discussion for a lab report](https://speedypaper.com/blog/how-to-write-a-discussion-for-a-lab-report) should focus on what the findings mean, whether they support the hypothesis, and how they relate to expected results or relevant scientific knowledge. Explain important patterns and unexpected outcomes. Consider meaningful limitations, measurement issues, experimental conditions, or alternative explanations. Do not simply repeat the results section. The discussion should give those results an interpretation.

Time management matters more with longer assignments. Divide the workload into separate stages: research, planning, drafting, revision, proofreading, and formatting. Leave enough space between stages to notice weaknesses that were invisible during drafting. Returning to the thesis after writing the main body can be revealing. The evidence may support a narrower or more qualified argument than the one you originally intended.

Students comparing online resources may wonder which essay writing platform is the best. Rather than trusting a ranking or promotional statement, examine the platform's source practices, citation features, originality safeguards, transparency, and usefulness for independent learning. A polished interface says little about the quality of the academic material behind it.

Revision should also have an order. First inspect the argument and structure. Then verify evidence and citations. Finally, correct grammar, punctuation, wording, and formatting. Reading the paper aloud can expose awkward sentences, repeated expressions, and abrupt transitions that remain unnoticed during silent proofreading. Check the reference list against the citations and compare the finished document with the original assignment requirements.

A useful graphic for planning can be a simple flowchart showing **assignment → research question → sources → outline → draft → revision → final checks**. It reinforces the sequence without adding unnecessary decoration. The goal is not to make the writing process complicated. It is to give each stage a clear purpose.

# Actor input Schema

## `startUrls` (type: `array`):

URLs to start with.

## `maxRequestsPerCrawl` (type: `integer`):

Maximum number of requests that can be made by this crawler.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://crawlee.dev"
    }
  ],
  "maxRequestsPerCrawl": 100
}
```

# Actor output Schema

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

No description

# 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 = {
    "startUrls": [
        {
            "url": "https://crawlee.dev"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("learning-edge/my-actor").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 = { "startUrls": [{ "url": "https://crawlee.dev" }] }

# Run the Actor and wait for it to finish
run = client.actor("learning-edge/my-actor").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 '{
  "startUrls": [
    {
      "url": "https://crawlee.dev"
    }
  ]
}' |
apify call learning-edge/my-actor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,learning-edge/my-actor"
        }
    }
}

```

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/PXgQkRDRZB4Ov9bAh/builds/K8GF7d5dYDZqcWXfX/openapi.json