Reddit Keyword Monitor with Score & Karma Filters: MCP config, n8n workflow and API
Reddit keyword searches return matching posts, comments, communities and accounts, tagged by dataType, with post details such as author, subreddit, score, comment count and URL. A developer relations agent can search r/developers for product discussions and apply score or comment thresholds before preparing a reading list. Store data checked 2026-11-13.
Supply keywords as a list of search terms, with optional controls for result types, subreddit scope and post filters.
Fastest route, in Claude Code:
claude mcp add --transport http reddit-keyword-monitor-with-score-and-karma "https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma" One click, signs in with Apify on first use:
Any other client, one command (writes the config for 25+ agents, including Windsurf, Zed, Cline and Goose):
npx add-mcp "https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma" --name reddit-keyword-monitor-with-score-and-karma Every file on this page is generated from the Actor's published schema: 51 of 51 checks pass (validated, not run)
- MCP configs: 7 of 7 (Uses Apify's hosted MCP endpoint over HTTPS; The tools parameter lists exactly the chosen Actors as owner/name; JSON configs parse and use the client's top-level key and the same URL; Codex TOML has a [mcp_servers.<name>] table with url and bearer_token_env_var; CLI commands quote the same URL and use the streamable HTTP transport; One-click install links decode back to the same URL and carry no token; Only placeholders, no real token)
- n8n workflow: 11 of 11 (It has a nodes array and a connections object; Every node has a name, type, typeVersion, position and parameters; Node names are unique; Every node type is a known n8n core node (or the Apify node); Every node uses a typeVersion that n8n ships; Every connection points at an existing node; At least one trigger starts the workflow; Every working node is reachable from a trigger; Apify calls use POST and a credential for the token; Actor input matches the Actor's published input schema; No token or credential value is embedded)
- long-run workflow: 11 of 11 (It has a nodes array and a connections object; Every node has a name, type, typeVersion, position and parameters; Node names are unique; Every node type is a known n8n core node (or the Apify node); Every node uses a typeVersion that n8n ships; Every connection points at an existing node; At least one trigger starts the workflow; Every working node is reachable from a trigger; Apify calls use POST and a credential for the token; Actor input matches the Actor's published input schema; No token or credential value is embedded)
- example input: 5 of 5 (All 1 required input field is present; Every input key exists in the published input schema; Every value has the type the schema declares; Every choice field uses one of its published options; Numbers respect the published minimum and maximum)
- code snippets: 4 of 4 (curl calls the documented run-sync-get-dataset-items endpoint for this Actor; Python (apify-client 3.x typed Run) and JavaScript use the official apify-client call() then read the run's dataset; Multi-line shell commands continue every line with a backslash; Token read from APIFY_TOKEN, none embedded)
- SKILL.md: 13 of 13 (SKILL.md starts with YAML front matter; name is 1-64 lowercase letters, digits and single hyphens; name matches the skill folder name; description is present, at most 1024 characters and has no angle brackets; front matter uses only spec fields; compatibility is at most 500 characters; body has instructions; field descriptions end on whole sentences; no credential values are embedded; the token is read from APIFY_TOKEN; the run command calls this Actor's run-sync endpoint; the example input is valid JSON; the example input matches the published input schema)
Good jobs for this Actor
- Find discussions matching a product name and retain each post's title, author, subreddit and URL for review.
- Find overlooked posts by applying maxScore and compare their num_comments values.
- Search communities and accounts for a topic, then route the results by dataType.
Prompts to try
- Search posts for "machine learning" within r/developers and return titles, scores, comment counts and URLs.
- Find Reddit posts mentioning "developer documentation" that have link flair, and include the author and subreddit.
- Search communities and accounts for "machine learning" and group the matches by dataType.
What a run costs
Every run pays a start fee plus a price per result. On the Free plan this Actor's start fee is $0.40 (the start event, $0.10 per GB, times its default 4 GB of memory), $0.12 on Business; results cost $0.99 per 1,000 results (pay per event). Checked 2026-11-13.
| Results in one run | Start | Results | Run total |
|---|---|---|---|
| 10 | $0.40 | $0.010 | $0.41 |
| 100 | $0.40 | $0.10 | $0.50 |
| 1,000 | $0.40 | $0.99 | $1.39 |
Plus Apify platform usage for the run, which the caller pays. A smaller memory setting lowers the start fee if the Actor still finishes. Prices can change; the Store pricing tab is the source of truth.
MCP config
One URL works in every client: https:/. It exposes this Actor as a tool, plus fetch-actor-details so the agent can read the input schema before calling it. Pick your client:
Claude Code configterminal or .mcp.json
claude mcp add --transport http reddit-keyword-monitor-with-score-and-karma "https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma" Run the command in your project folder. Add --scope user to make it available in every project. Full Claude Code setup
Claude Desktop configclaude_desktop_config.json
{
"mcpServers": {
"reddit-keyword-monitor-with-score-and-karma": {
"command": "npx",
"args": [
"-y",
"@apify/actors-mcp-server",
"--tools",
"fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma"
],
"env": {
"APIFY_TOKEN": "YOUR_APIFY_TOKEN"
}
}
}
} Easiest: open Customize, then Connectors, choose Add custom connector, paste the server URL (the ChatGPT tab shows it on its own) and sign in to Apify. Full Claude Desktop setup
Cursor config~/.cursor/mcp.json
{
"mcpServers": {
"reddit-keyword-monitor-with-score-and-karma": {
"url": "https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma",
"headers": {
"Authorization": "Bearer ${env:APIFY_TOKEN}"
}
}
}
} Save as ~/.cursor/mcp.json for every project, or .cursor/mcp.json for one project. Merge with servers you already have. Full Cursor setup
VS Code config.vscode/mcp.json
{
"inputs": [
{
"type": "promptString",
"id": "apify-token",
"description": "Apify API token",
"password": true
}
],
"servers": {
"reddit-keyword-monitor-with-score-and-karma": {
"type": "http",
"url": "https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma",
"headers": {
"Authorization": "Bearer ${input:apify-token}"
}
}
}
} Save as .vscode/mcp.json in your workspace. Full VS Code setup
ChatGPT configPlugins > Add custom MCP server
https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma In ChatGPT on the web, open Plugins, select + and choose Add custom MCP server. Full ChatGPT setup
Codex CLI config~/.codex/config.toml
[mcp_servers.reddit-keyword-monitor-with-score-and-karma]
url = "https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma"
bearer_token_env_var = "APIFY_TOKEN" Add the block to ~/.codex/config.toml, or .codex/config.toml in a project. Full Codex CLI setup
Gemini CLI configterminal (writes ~/.gemini/settings.json)
gemini mcp add -t http -s user -H "Authorization: Bearer $APIFY_TOKEN" reddit-keyword-monitor-with-score-and-karma "https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma" Export APIFY_TOKEN first: the shell expands it and Gemini CLI saves the header, token included, in plain text in ~/.gemini/settings.json (keep that file private). In PowerShell write $env:APIFY_TOKEN instead of $APIFY_TOKEN. Drop -s user to save it in the project's .gemini/settings.json. Full Gemini CLI setup
n8n AI Agent configMCP Client Tool node
https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma Add an AI Agent node, then attach an MCP Client Tool node to its tool input. Full n8n AI Agent setup
Tools your agent gets
Listed by Apify's MCP server 0.21.9 for this exact URL on 2026-11-12 (a tools/list call; nothing was run).
fetch-actor-detailsreads the input schemaget-actor-runchecks a run's statusget-dataset-itemsreads a run's resultsget-key-value-store-recordreads a stored record such as a file or summaryabort-actor-runstops a run that is still goingxyrelith-data--reddit-keyword-monitor-with-score-and-karmaruns the Actor and returns its results
n8n workflow
A manual and a daily trigger call the Actor through the HTTP Request node with your Apify token, then hand each record to the destination you pick. The Actor input sits in the request body, already filled with the example below.
- Run by hand
- Every day
- Run Apify Actor
n8n workflowreddit-keyword-monitor-with-score-and-karma.n8n.json
{
"name": "Reddit Keyword Monitor with Score & Karma Filters to n8n only",
"nodes": [
{
"id": "3a93c00e-75a4-46a1-ae14-c608f0c13bb3",
"name": "Run by hand",
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
0,
0
],
"parameters": {}
},
{
"id": "219717b6-36cc-437f-abdf-49927bb96463",
"name": "Every day",
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.2,
"position": [
0,
200
],
"parameters": {
"rule": {
"interval": [
{
"triggerAtHour": 8
}
]
}
},
"notes": "Activate the workflow to run on this schedule."
},
{
"id": "5c052052-c341-475c-a22a-216d8c8be07f",
"name": "Run Apify Actor",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
260,
100
],
"parameters": {
"method": "POST",
"url": "https://api.apify.com/v2/actors/xyrelith-data~reddit-keyword-monitor-with-score-and-karma/run-sync-get-dataset-items",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "{\n \"keywords\": [\n \"machine learning\"\n ],\n \"searchPosts\": true,\n \"searchComments\": false,\n \"searchCommunities\": false,\n \"searchUsers\": false,\n \"sort\": \"relevance\",\n \"timeFilter\": \"all\",\n \"resultLimit\": 3,\n \"maxComments\": 100,\n \"maxCommunities\": 10,\n \"maxUsers\": 25,\n \"maxItems\": 3,\n \"includeNSFW\": false,\n \"onlyWithFlair\": false,\n \"excludeSpoilers\": false,\n \"onlyOriginalContent\": false,\n \"excludeStickied\": false,\n \"excludeCrossposts\": false,\n \"excludeArchived\": false,\n \"excludeLocked\": false,\n \"excludeDeleted\": false,\n \"onlyOPComments\": false\n}",
"options": {
"timeout": 310000
}
},
"notes": "Actor: xyrelith-data/reddit-keyword-monitor-with-score-and-karma. Select a Header Auth credential: name Authorization, value Bearer + your Apify token. Edit the JSON body to change the input.",
"notesInFlow": false
},
{
"id": "6dfa0836-9e65-4fbf-a6f3-f9ae49ecaf13",
"name": "Setup",
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
0,
-370
],
"parameters": {
"content": "## Reddit Keyword Monitor with Score & Karma Filters\n\nSource: [xyrelith-data/reddit-keyword-monitor-with-score-and-karma](https://apify.com/xyrelith-data/reddit-keyword-monitor-with-score-and-karma) on Apify.\n\n1. In **Run Apify Actor**, create a Header Auth credential: name `Authorization`, value `Bearer YOUR_APIFY_TOKEN` with your token.\n2. Click **Execute workflow**. Each record arrives as one n8n item.\n3. Edit the JSON body of **Run Apify Actor** to change the input.\n\nGenerated by recipeforagents.com. Validated, not executed: runs are billed to your Apify account.",
"height": 340,
"width": 460,
"color": 6
}
}
],
"connections": {
"Run by hand": {
"main": [
[
{
"node": "Run Apify Actor",
"type": "main",
"index": 0
}
]
]
},
"Every day": {
"main": [
[
{
"node": "Run Apify Actor",
"type": "main",
"index": 0
}
]
]
}
},
"pinData": {},
"active": false,
"settings": {
"executionOrder": "v1"
},
"meta": {
"templateCredsSetupCompleted": false
},
"tags": []
} Import with Workflows, Import from file, or paste into the canvas. Credentials are not included: you select them after import.
All 7 destination variants pass the same checks at build time. Built-in nodes only, at versions n8n has shipped since 1.80: they import on n8n Cloud and older self-hosted instances without community nodes. Prefer Apify's own node? The verified community node @apify/n8n-nodes-apify runs the same Actor with OAuth sign-in on n8n Cloud.
Runs longer than 5 minutes? The synchronous endpoint answers HTTP 408 after 300 seconds. The long-run variant starts the run, checks it every minute until it ends, then reads its dataset (HTTP Request, If and Stop and Error nodes only).
Download the long-run workflow11 of 11 checks pass, not executed
Call it from code
Run the Actor from a script or your own agent framework with the Apify API. The call waits for the run to finish and returns the records. The input is the example below.
curl
curl -X POST "https://api.apify.com/v2/actors/xyrelith-data~reddit-keyword-monitor-with-score-and-karma/run-sync-get-dataset-items" \
-H "Authorization: Bearer $APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"keywords":["machine learning"],"searchPosts":true,"searchComments":false,"searchCommunities":false,"searchUsers":false,"sort":"relevance","timeFilter":"all","resultLimit":3,"maxComments":100,"maxCommunities":10,"maxUsers":25,"maxItems":3,"includeNSFW":false,"onlyWithFlair":false,"excludeSpoilers":false,"onlyOriginalContent":false,"excludeStickied":false,"excludeCrossposts":false,"excludeArchived":false,"excludeLocked":false,"excludeDeleted":false,"onlyOPComments":false}' Install: pip install "apify-client>=3"
Python
import os
from apify_client import ApifyClient
client = ApifyClient(os.environ["APIFY_TOKEN"])
run_input = {
"keywords": [
"machine learning",
],
"searchPosts": True,
"searchComments": False,
"searchCommunities": False,
"searchUsers": False,
"sort": "relevance",
"timeFilter": "all",
"resultLimit": 3,
"maxComments": 100,
"maxCommunities": 10,
"maxUsers": 25,
"maxItems": 3,
"includeNSFW": False,
"onlyWithFlair": False,
"excludeSpoilers": False,
"onlyOriginalContent": False,
"excludeStickied": False,
"excludeCrossposts": False,
"excludeArchived": False,
"excludeLocked": False,
"excludeDeleted": False,
"onlyOPComments": False,
}
run = client.actor("xyrelith-data/reddit-keyword-monitor-with-score-and-karma").call(run_input=run_input)
if run is None:
raise RuntimeError("The Actor run could not be started")
print(run.status, "dataset", run.default_dataset_id)
for item in client.dataset(run.default_dataset_id).iterate_items():
print(item) Install: npm install apify-client
JavaScript
import { ApifyClient } from "apify-client";
const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const input = {
"keywords": [
"machine learning"
],
"searchPosts": true,
"searchComments": false,
"searchCommunities": false,
"searchUsers": false,
"sort": "relevance",
"timeFilter": "all",
"resultLimit": 3,
"maxComments": 100,
"maxCommunities": 10,
"maxUsers": 25,
"maxItems": 3,
"includeNSFW": false,
"onlyWithFlair": false,
"excludeSpoilers": false,
"onlyOriginalContent": false,
"excludeStickied": false,
"excludeCrossposts": false,
"excludeArchived": false,
"excludeLocked": false,
"excludeDeleted": false,
"onlyOPComments": false
};
const run = await client.actor("xyrelith-data/reddit-keyword-monitor-with-score-and-karma").call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.length, items[0]); In an agent framework
The same Actor and input in the frameworks Apify documents: as a LangChain or CrewAI tool, a LlamaIndex document loader, or through the scoped MCP URL in the OpenAI Agents SDK and Mastra. Each snippet is run against the current package before it is shown (stub API, nothing billed). Framework guides
Install: pip install langchain-apify
LangChain
# Reads APIFY_TOKEN from the environment.
from langchain_apify import ApifyActorsTool
tool = ApifyActorsTool("xyrelith-data/reddit-keyword-monitor-with-score-and-karma")
items = tool.invoke({"run_input": {
"keywords": [
"machine learning",
],
"searchPosts": True,
"searchComments": False,
"searchCommunities": False,
"searchUsers": False,
"sort": "relevance",
"timeFilter": "all",
"resultLimit": 3,
"maxComments": 100,
"maxCommunities": 10,
"maxUsers": 25,
"maxItems": 3,
"includeNSFW": False,
"onlyWithFlair": False,
"excludeSpoilers": False,
"onlyOriginalContent": False,
"excludeStickied": False,
"excludeCrossposts": False,
"excludeArchived": False,
"excludeLocked": False,
"excludeDeleted": False,
"onlyOPComments": False,
}})
print(len(items), "records", items[:1])
# Give the same tool to an agent (pip install langchain langchain-openai):
# from langchain.agents import create_agent
# agent = create_agent(ChatOpenAI(model="gpt-5.4-mini"), [tool]) Install: pip install 'crewai[tools]' langchain-apify
CrewAI
# Reads APIFY_API_TOKEN (Apify token) and OPENAI_API_KEY from the environment.
from crewai import Agent, Crew, Task
from crewai_tools import ApifyActorsTool
tool = ApifyActorsTool(actor_name="xyrelith-data/reddit-keyword-monitor-with-score-and-karma")
agent = Agent(
role="Data researcher",
goal="Collect records with the Apify Actor and summarise them",
backstory="You call Apify Actors to get live web data.",
tools=[tool],
)
task = Task(
description="Use Reddit Keyword Monitor with Score & Karma Filters to get a few records and summarise what came back, with links. Call the tool with this run_input: {\"keywords\":[\"machine learning\"],\"searchPosts\":true,\"searchComments\":false,\"searchCommunities\":false,\"searchUsers\":false,\"sort\":\"relevance\",\"timeFilter\":\"all\",\"resultLimit\":3,\"maxComments\":100,\"maxCommunities\":10,\"maxUsers\":25,\"maxItems\":3,\"includeNSFW\":false,\"onlyWithFlair\":false,\"excludeSpoilers\":false,\"onlyOriginalContent\":false,\"excludeStickied\":false,\"excludeCrossposts\":false,\"excludeArchived\":false,\"excludeLocked\":false,\"excludeDeleted\":false,\"onlyOPComments\":false}",
agent=agent,
expected_output="A short summary of the records with their links.",
)
print(Crew(agents=[agent], tasks=[task]).kickoff()) Install: pip install apify-client llama-index-core llama-index-readers-apify
LlamaIndex
import json
import os
from llama_index.core import Document
from llama_index.readers.apify import ApifyActor
reader = ApifyActor(os.environ["APIFY_TOKEN"])
documents = reader.load_data(
actor_id="xyrelith-data/reddit-keyword-monitor-with-score-and-karma",
run_input={
"keywords": [
"machine learning",
],
"searchPosts": True,
"searchComments": False,
"searchCommunities": False,
"searchUsers": False,
"sort": "relevance",
"timeFilter": "all",
"resultLimit": 3,
"maxComments": 100,
"maxCommunities": 10,
"maxUsers": 25,
"maxItems": 3,
"includeNSFW": False,
"onlyWithFlair": False,
"excludeSpoilers": False,
"onlyOriginalContent": False,
"excludeStickied": False,
"excludeCrossposts": False,
"excludeArchived": False,
"excludeLocked": False,
"excludeDeleted": False,
"onlyOPComments": False,
},
dataset_mapping_function=lambda item: Document(text=json.dumps(item), metadata={"url": item.get("url")}),
)
print(len(documents), "documents") Install: pip install openai-agents
OpenAI Agents SDK
import asyncio
import os
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main() -> None:
async with MCPServerStreamableHttp(
name="Apify",
params={
"url": "https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma",
"headers": {"Authorization": f"Bearer {os.environ['APIFY_TOKEN']}"},
"timeout": 120,
},
cache_tools_list=True,
) as server:
tools = await server.list_tools()
print("tools:", [t.name for t in tools])
agent = Agent(name="Assistant", instructions="Use the Apify tools to get live data.", mcp_servers=[server])
result = await Runner.run(agent, "Use Reddit Keyword Monitor with Score & Karma Filters to get a few records and summarise what came back, with links.")
print(result.final_output)
asyncio.run(main()) Install: npm install @mastra/core @mastra/mcp
Mastra
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
const mcp = new MCPClient({
servers: {
apify: {
url: new URL("https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma"),
requestInit: { headers: { Authorization: `Bearer ${process.env.APIFY_TOKEN}` } },
},
},
timeout: 300_000, // Actor runs can take minutes
});
const tools = await mcp.listTools();
console.log("tools:", Object.keys(tools));
const agent = new Agent({ id: "apify-agent", name: "Apify agent", instructions: "Use the Apify tools to get live data.", model: "openai/gpt-4o-mini", tools });
const result = await agent.generate("Use Reddit Keyword Monitor with Score & Karma Filters to get a few records and summarise what came back, with links.");
console.log(result.text);
await mcp.disconnect(); Example input
These values come from the Actor's Store prefill and required defaults. Change the search, URL or limit before your first run.
Actor inputinput.json
{
"keywords": [
"machine learning"
],
"searchPosts": true,
"searchComments": false,
"searchCommunities": false,
"searchUsers": false,
"sort": "relevance",
"timeFilter": "all",
"resultLimit": 3,
"maxComments": 100,
"maxCommunities": 10,
"maxUsers": 25,
"maxItems": 3,
"includeNSFW": false,
"onlyWithFlair": false,
"excludeSpoilers": false,
"onlyOriginalContent": false,
"excludeStickied": false,
"excludeCrossposts": false,
"excludeArchived": false,
"excludeLocked": false,
"excludeDeleted": false,
"onlyOPComments": false
} | Field | Type | What it does |
|---|---|---|
keywords required | array | List of keywords to search for on Reddit. Each keyword is searched separately and every result is tagged with search_term. Supports exact phrases ("machine learning"), boolean (python AND rust, python OR django, python… |
searchPosts | boolean | Include posts in the keyword search results. Posts come from the Arctic Shift Reddit archive, with Reddit's Atom feed as a fallback. |
searchComments | boolean | Include comments in the keyword search results. Comments come from the Arctic Shift Reddit archive. |
searchCommunities | boolean | Include subreddits in the keyword search results. Community search matches community names/prefixes from the Arctic Shift archive and returns full community data. |
searchUsers | boolean | Include Reddit accounts that match the keyword in the username. Account search matches username prefixes from the Arctic Shift archive and returns karma breakdowns. |
sort | string (5 options) | How the matched results are ranked. Arctic Shift returns candidates in creation order; Top, Hot and Comment count re-rank the retrieved matches, while New is strict newest-first. Relevance keeps the archive's… |
timeFilter | string (6 options) | Limit results to a recent creation window (applied to every sort, unlike Reddit's own UI where it only affects Top). |
resultLimit | integer | Maximum number of posts to return for each keyword (1-1000). Deep requests page through the archive in creation-time windows. |
maxComments | integer | Maximum number of comments to return per keyword (when 'Search for comments' is enabled). |
maxCommunities | integer | Maximum number of communities (subreddits) to return per keyword (when 'Search for communities' is enabled). |
maxUsers | integer | Maximum number of Reddit accounts to return per keyword (when 'Search for users' is enabled). |
maxItems | integer | Hard cap on the total number of records returned for the whole run (all keywords, all result types combined). The run stops as soon as this budget is spent. |
includeNSFW | boolean | Include NSFW posts in the results. |
onlyWithFlair | boolean | Keep only posts that have a link flair set (e.g. 'Discussion', 'News'). Posts without any flair are dropped. |
excludeSpoilers | boolean | When enabled, posts marked as a spoiler by their author are dropped from the results. |
onlyOriginalContent | boolean | When enabled, only posts the author tagged as original content are kept. |
excludeStickied | boolean | When enabled, posts pinned/stickied by moderators (and, when 'Search for comments' is enabled, comments stickied by a moderator) are dropped from the results. |
excludeCrossposts | boolean | When enabled, posts that are crossposted from another subreddit are dropped from the results. |
excludeArchived | boolean | When enabled, posts (and, when 'Search for comments' is enabled, comments) that Reddit has archived (locked from further voting/commenting after 6 months) are dropped from the results. |
excludeLocked | boolean | When enabled, posts that a moderator has locked from further comments (and, when 'Search for comments' is enabled, comments a moderator has locked from further replies) are dropped from the results. |
excludeDeleted | boolean | Drop posts and comments whose author account was deleted or whose content was removed by a moderator/admin (Reddit marks these with the literal '[deleted]' or '[removed]' placeholders). |
onlyOPComments | boolean | When enabled, keep only comments written by the post's original poster (self-replies). Applies when 'Search for comments' is enabled; ignored otherwise. |
23 more optional fields on the Store input tab.
What your agent receives
Each run returns a list of records. The Actor publishes 9 fields, starting with search_term, title, author, subreddit_prefixed.
| Field |
|---|
dataType |
search_term |
title |
author |
subreddit_prefixed |
score |
num_comments |
url |
created_at |
Agent skill
A SKILL.md in the open Agent Skills format. It teaches Claude Code, Codex and other skill-aware agents when to call this Actor, how to build the input and how to read the result, using the API directly.
SKILL.mdreddit-keyword-monitor-with-score-and-karma/SKILL.md
---
name: reddit-keyword-monitor-with-score-and-karma
description: "Reddit keyword search matches across posts, comments, communities and accounts, each tagged with a dataType, its source and archive freshness metadata. Runs the Apify Actor xyrelith-data/reddit-keyword-monitor-with-score-and-karma. Use when the user needs Reddit data with fields such as dataType, search_term, title, author, subreddit_prefixed, score and an Apify token is available."
compatibility: "Needs network access, curl, and an APIFY_TOKEN environment variable. Runs are billed to the token owner's Apify account."
metadata:
actor: "xyrelith-data/reddit-keyword-monitor-with-score-and-karma"
store-url: "https://apify.com/xyrelith-data/reddit-keyword-monitor-with-score-and-karma"
---
# Reddit Keyword Monitor with Score & Karma Filters
Use this skill to fetch Reddit data with the Apify Actor `xyrelith-data/reddit-keyword-monitor-with-score-and-karma` and work with the returned rows.
## Before running
1. Check that `APIFY_TOKEN` is set in the environment. Never print it or write it to a file.
2. Build `input.json` from the user's request using the fields below. Keep limits small for a first run.
3. Confirm the search terms or URLs with the user if the request is ambiguous.
## Input fields
| Field | Type | Meaning |
| --- | --- | --- |
| `keywords` | array, required | List of keywords to search for on Reddit. Each keyword is searched separately and every result is tagged with search_term. |
| `searchPosts` | boolean | Include posts in the keyword search results. Posts come from the Arctic Shift Reddit archive, with Reddit's Atom feed as a fallback. |
| `searchComments` | boolean | Include comments in the keyword search results. Comments come from the Arctic Shift Reddit archive. |
| `searchCommunities` | boolean | Include subreddits in the keyword search results. Community search matches community names/prefixes from the Arctic Shift archive and returns full community data. |
| `searchUsers` | boolean | Include Reddit accounts that match the keyword in the username. Account search matches username prefixes from the Arctic Shift archive and returns karma breakdowns. |
| `sort` | one of `relevance`, `hot`, `top`, `new`, `comments` | How the matched results are ranked. Arctic Shift returns candidates in creation order; Top, Hot and Comment count re-rank the retrieved matches, while New is strict newest-first. Relevance keeps the archive's keyword-search order. |
| `timeFilter` | one of `hour`, `day`, `week`, `month`, `year`, `all` | Limit results to a recent creation window (applied to every sort, unlike Reddit's own UI where it only affects Top). |
| `resultLimit` | integer | Maximum number of posts to return for each keyword (1-1000). Deep requests page through the archive in creation-time windows. |
| `maxComments` | integer | Maximum number of comments to return per keyword (when 'Search for comments' is enabled). |
| `maxCommunities` | integer | Maximum number of communities (subreddits) to return per keyword (when 'Search for communities' is enabled). |
| `maxUsers` | integer | Maximum number of Reddit accounts to return per keyword (when 'Search for users' is enabled). |
| `maxItems` | integer | Hard cap on the total number of records returned for the whole run (all keywords, all result types combined). The run stops as soon as this budget is spent. |
| `includeNSFW` | boolean | Include NSFW posts in the results. |
| `onlyWithFlair` | boolean | Keep only posts that have a link flair set (e.g. 'Discussion', 'News'). Posts without any flair are dropped. |
Example `input.json` (the Actor's published prefill):
```json
{
"keywords": [
"machine learning"
],
"searchPosts": true,
"searchComments": false,
"searchCommunities": false,
"searchUsers": false,
"sort": "relevance",
"timeFilter": "all",
"resultLimit": 3,
"maxComments": 100,
"maxCommunities": 10,
"maxUsers": 25,
"maxItems": 3,
"includeNSFW": false,
"onlyWithFlair": false,
"excludeSpoilers": false,
"onlyOriginalContent": false,
"excludeStickied": false,
"excludeCrossposts": false,
"excludeArchived": false,
"excludeLocked": false,
"excludeDeleted": false,
"onlyOPComments": false
}
```
## Run
```bash
curl -sS -X POST "https://api.apify.com/v2/acts/xyrelith-data~reddit-keyword-monitor-with-score-and-karma/run-sync-get-dataset-items" \
-H "Authorization: Bearer $APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d @input.json > results.json
```
The call waits for the run to finish and returns the dataset items as a JSON array. If the run needs more than 300 seconds the API answers with HTTP 408; then start the run with `POST /v2/acts/xyrelith-data~reddit-keyword-monitor-with-score-and-karma/runs`, poll `GET /v2/actor-runs/{runId}` until `SUCCEEDED`, and read `GET /v2/datasets/{defaultDatasetId}/items`.
## Output
Each item is one record. Published fields include: `dataType`, `search_term`, `title`, `author`, `subreddit_prefixed`, `score`, `num_comments`, `url`, `created_at`.
Summarise what was found, cite the record URLs when present, and say how many items came back. Do not invent values for empty fields.
- Claude Code: save it as
~/.claude/skills/reddit-keyword-monitor-with-score-and-karma/SKILL.md, or.claude/skills/reddit-keyword-monitor-with-score-and-karma/SKILL.mdin a project. - Codex and other agents:
~/.agents/skills/reddit-keyword-monitor-with-score-and-karma/SKILL.md, or.agents/skills/reddit-keyword-monitor-with-score-and-karma/in a project. - Or let the skills CLI place it for your agent:
npx skills add https://recipeforagents.com --skill reddit-keyword-monitor-with-score-and-karma - Set
APIFY_TOKENin the agent's environment.
Troubleshooting
Why does the client say Unauthorized or invalid token?
Every Apify Actor tool needs a signed-in Apify account. Reconnect the server so the OAuth sign-in runs again, or switch to the token variant: an Authorization: Bearer header with an API token from Apify Console, Settings, API and integrations. For a local server the token must be in the server's own env block, not only in your shell.
Why did my agent's tool list change after an update?
Without a tools parameter Apify's server loads its default tool set, which changes between releases. The URLs on this site pin the tools with ?tools=, so the agent always gets the same Actor tool plus fetch-actor-details. Apify's README gives the same advice for production.
What happens when the agent sends input the Actor rejects?
The server answers with the Actor's input schema, so the agent can fix the arguments and call again. The fetch-actor-details tool in every scoped URL lets it read that schema before the first call.
Questions
Does timeFilter apply to every sort order?
timeFilter applies to the Top sort, matching Reddit's search interface. Other sort orders ignore it.
How are searches and result limits applied?
Each entry in keywords is searched separately, with resultLimit controlling posts and separate limits controlling comments, communities and accounts. maxItems caps the combined records across all keywords and result types.
How do I connect Reddit Keyword Monitor with Score & Karma Filters to Claude?
In Claude Code run: claude mcp add --transport http reddit-keyword-monitor-with-score-and-karma "https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma". In Claude Desktop or claude.ai add a custom connector with the URL https://mcp.apify.com/?tools=fetch-actor-details,xyrelith-data/reddit-keyword-monitor-with-score-and-karma and sign in to Apify. The agent then gets one tool for this Actor plus a tool that reads its input schema.
What input does Reddit Keyword Monitor with Score & Karma Filters need?
The published input schema requires keywords. The example on this page uses the Actor's own Store prefill.
What does Reddit Keyword Monitor with Score & Karma Filters cost to run?
The Apify Store lists $0.99 per 1,000 results (pay per event) for this Actor, checked 2026-11-13. Runs are billed to the Apify account whose token or sign-in the agent uses; Apify platform usage can add to that.
Which tools does the MCP config give my agent?
6 tools: fetch-actor-details, get-actor-run, get-dataset-items, get-key-value-store-record, abort-actor-run, xyrelith-data--reddit-keyword-monitor-with-score-and-karma. Apify's MCP server listed them for this exact URL on 2026-11-12.
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