# Repository readiness evidence for AI agents (`rare_sunset/repository-readiness`) Actor

Unofficial GitHub integration; not affiliated with GitHub. Check an explicit public repository handoff checklist and return source-linked file hashes, line counts, and link-presence evidence.

- **URL**: https://apify.com/rare\_sunset/repository-readiness.md
- **Developed by:** [Abdulrahman Baidaq](https://apify.com/rare_sunset) (community)
- **Categories:** Developer tools, Agents, Integrations
- **Stats:** 1 total users, 0 monthly users, 0.0% 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/actors/running/actors-in-store.md#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

## Repository readiness evidence for AI coding agents

This Actor checks a small, explicit handoff checklist for a public GitHub repository. It returns source links, byte counts, line counts, SHA-256 hashes, and literal required-link checks as structured dataset records.

### What it does

Give it a public repository URL, a branch/tag/commit, and the paths that an agent must be able to inspect. It fetches the Git tree, reads only the requested files within the configured size limit, and writes:

- one `summary` record with `ready` or `incomplete` status;
- one `path` record for each required path;
- one `link` record for each required link.

The Actor does not run tests, execute repository code, scan private repositories, or claim that the code is correct. A `present` record proves that a particular public file was available at the selected ref and gives the agent a source URL and content hash. A separate CI or local test run is still required for behavioral correctness.

### Example input

```json
{
  "repository_url": "https://github.com/Abood991B/apify-content-program-actors",
  "ref": "main",
  "required_paths": [
    "README.md",
    "repository-readiness-actor/src/readiness.py",
    "repository-readiness-actor/tests/test_readiness.py"
  ],
  "required_links": [
    "https://docs.apify.com/integrations/mcp"
  ],
  "max_file_bytes": 1000000,
  "include_content_preview": false
}
```

### Why an AI agent can use it

The output is intentionally boring: a checklist result, not a generated review. An agent can decide what to do next from explicit fields and follow the `sourceUrl` or `rawUrl` without guessing which branch or file was inspected. The original request is also kept in the default key-value store under `REQUEST`, and the summary is stored under `SUMMARY`.

### Limits and responsible use

The implementation calls GitHub's public REST and raw-content endpoints. It does not bypass authentication. Respect GitHub's terms, rate limits, and the repository's license. The tree endpoint can report `repository_tree_truncated`; in that case an `incomplete` result is safer than treating an absent path as proof that a file does not exist.

### Local development

```powershell
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -r requirements.txt
python -m unittest discover -s tests -v
```

The Actor is deployed from the repository root with the Apify CLI. The `.actor` folder contains the input and output schemas used by Apify Console and the Apify MCP server.

# Actor input Schema

## `repository_url` (type: `string`):

A public HTTPS GitHub repository URL such as https://github.com/owner/repository.

## `ref` (type: `string`):

Branch, tag, or commit to inspect. Use the exact ref used by the handoff.

## `required_paths` (type: `array`):

Relative file paths that must be present and readable. Keep the list focused on the handoff evidence.

## `required_links` (type: `array`):

HTTPS links that must appear literally in at least one readable checked file, usually a README or runbook.

## `max_file_bytes` (type: `integer`):

Do not download a checked file larger than this limit. The Actor reports it as too\_large instead.

## `include_content_preview` (type: `boolean`):

Include a short UTF-8 preview for readable files. Hashes and source URLs are returned regardless.

## Actor input object example

```json
{
  "ref": "main",
  "required_links": [],
  "max_file_bytes": 1000000,
  "include_content_preview": false
}
```

# Actor output Schema

## `evidence` (type: `string`):

No description

## `request` (type: `string`):

No description

## `summary` (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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("rare_sunset/repository-readiness").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("rare_sunset/repository-readiness").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 '{}' |
apify call rare_sunset/repository-readiness --silent --output-dataset

```

## MCP server setup

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

```

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/2c3dBWdEyNaWj8ASG/builds/azcbB8CIPoITUzjmO/openapi.json