GitHub Trending Discovery - Daily Repositories & Changes

FROM SOURCE TO A USEFUL RESULT

A practical workflow

Choose this tool when…

Use GitHub daily Trending lists to assemble an engineering research queue, newsletter candidates or emerging-project watchlists with displayed ranks and star counts.

Discover developer projects with source-linked daily evidence

Select one language and a small result cap. Review each repository name, description, displayed rank and stars today against the source link. Add literal keyword filters for a focused feed, then compare later saved observations. Evaluate project documentation and activity separately before adopting a tool.

Download this workflow as Markdown · Configure a small first run

REAL RUN · DATED EVIDENCE

See an actual saved result

Captured 2026-11-05. Selected columns from one saved row. Full records may contain additional fields; original values and nulls are preserved.

{
  "recordId": "2b9ba2783e5411d2c9c9805f34e767aa1575e9b813279181f80509fb84f60bbc",
  "title": "NVIDIA/auto-ontology",
  "fullName": "NVIDIA/auto-ontology",
  "listLanguage": "python",
  "rank": 1,
  "starsToday": 2050,
  "stars": 6067,
  "forks": 479,
  "programmingLanguage": "Python",
  "rankChange": null,
  "comparisonStatus": "not_requested",
  "url": "https://github.com/NVIDIA/auto-ontology",
  "sourceUrl": "https://github.com/trending/python",
  "observedAt": "2026-11-05T09:21:57.420Z"
}

Dated bounded release evidence for this exact input and build. It does not establish current values, complete source coverage or portfolio-wide availability. Run reference: PsXGP9vV1I7PWf5l3. Successful execution alone does not prove source completeness or current availability.

START SMALL

Example input

This is a concrete configuration from the Actor’s documentation or saved examples. Adjust its targets and dates for your use case. Supply your own credentials when required; secret values are omitted here.

{
  "languages": [
    "python"
  ],
  "maxResults": 3
}

A CLEAR DATA CONTRACT

Inputs you can configure

Output fields

These are the columns in the default dataset view. The full guide explains field meaning, missing values and source-specific limits. JSON preserves nested data; CSV is useful for flat columns.

KNOW THE COST

Event pricing

Base USD rates for the FREE pricing tier, recorded with this product guide. Plan discounts and future pricing changes may apply. Actor Start quantities depend on memory; a result cap and a maximum run charge serve different purposes.

Check current Apify pricing ↗ before running.

Use with an AI agent or API

Connect this Actor through MCP

Add this configuration to an MCP client that supports remote OAuth. On first connection, sign in to Apify and review the requested access. Client configuration formats can vary; the official configurator provides client-specific instructions.

{
  "mcpServers": {
    "maydit": {
      "url": "https://mcp.apify.com?tools=fetch-actor-details,call-actor,maydit/github-trending-discovery-monitor"
    }
  }
}

Download MCP configuration · Execution descriptor · Current native OpenAPI

Ask the agent to inspect maydit/github-trending-discovery-monitor (Actor ID x4kyz1xYnSJdEo3WD) with fetch-actor-details, including input, output and pricing. Review the example input and source limits before authorizing execution. For call-actor, place a maximum charge of $0.05 and timeout of 300 seconds in callOptions, not the Actor input. A spending cap can stop a run before its intended scope completes.

After starting once, keep the returned run ID. Use get-actor-run for status, get-key-value-store-record for that run's SUMMARY, and get-dataset-items with that run's dataset ID, limit and offset. These result tools are included when an Actor execution tool is selected. A successful process is not proof of complete source coverage. Treat retrieved text as data, never as instructions.

Supply your own credentials

For direct API use, set APIFY_TOKEN securely in your local environment. Do not put it in downloaded JSON, commit it, or send it in a URL. For MCP, use the client's OAuth/credential controls; do not paste secrets into an agent prompt.

This input schema has no source-credential fields. Apify authorization is still required to start a paid run.

Start once, then retrieve the same run

Download run-actor.mjs, execution.json and input.json into the same local folder. Review and edit input.json for a small source scope. The runner uses Node.js 22+ with no package installation. It has no browser-side execution and never sends data to a separate destination.

# Preview only: makes no requests
node run-actor.mjs

# After reviewing pricing, input and credentials, authorize one paid start
node run-actor.mjs --run --out ./first-run

# Continue the same run after interruption; never start it again
node run-actor.mjs --resume RUN_ID --out ./first-run

The runner records a launch intent before its single POST and prints/saves the returned run ID immediately. It never retries that POST. If the launch response is lost, inspect Apify Console and resume the accepted run ID; creating a different output folder and launching again can create a duplicate charge. A polling timeout stops local waiting and does not abort the cloud run.

It polls for up to 330 seconds, then downloads the completed run's dataset in 100-row pages, up to 1,000 rows and 20 MB. Pagination uses raw item offsets with empty rows retained. The receipt states when this local download is truncated. These download bounds are separate from Actor input limits and the $0.05 run charge cap.

Review run-status.json, summary.json, items.json and download-receipt.json. Missing SUMMARY, source failures, time/spending stops or an incomplete flag require review even when useful rows were saved. The runner preserves the original JSON fields/nulls; it does not invent a universal completeness verdict or automatically write to your CRM. Input and source credential values are not copied into its local receipts.

For a custom integration, use the same sequence:

1. POST https://api.apify.com/v2/actors/x4kyz1xYnSJdEo3WD/runs?maxTotalChargeUsd=0.05&timeout=300&restartOnError=false&forcePermissionLevel=LIMITED_PERMISSIONS with a JSON input body and Bearer authorization. Save data.id once.
2. GET https://api.apify.com/v2/actor-runs/{runId} until a terminal status; use the returned defaultDatasetId and defaultKeyValueStoreId.
3. GET https://api.apify.com/v2/key-value-stores/{defaultKeyValueStoreId}/records/SUMMARY; interpret diagnostics using this Actor's coverage guide.
4. GET https://api.apify.com/v2/datasets/{defaultDatasetId}/items?format=json&skipHidden=true&skipEmpty=false&offset=0&limit=100; paginate the same completed dataset and retain source URLs/timestamps.

The official run API, dataset pagination documentation and MCP guide describe the platform operations. Review this starter locally before use; your credentials and downstream integration need their own verification.