# Claude Code Help Channel Aggregator (`ianymu/claude-code-help-channel-aggregator`) Actor Aggregate Claude Code help discussions across Reddit (r/ClaudeAI) and GitHub Discussions on claude-code repos. Cluster recurring questions, rank by frequency and engagement, return top Q\&A patterns with example answers. Useful for docs teams and support engineers. - **URL**: https://apify.com/ianymu/claude-code-help-channel-aggregator.md - **Developed by:** [Yanlong Mu](https://apify.com/ianymu) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 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 ## Claude Code Help Channel Aggregator Aggregate **Claude Code help discussions** from across the web - Reddit (r/ClaudeAI, r/Anthropic) and GitHub Discussions on `anthropics/claude-code` and related repos - cluster recurring questions, and surface the **top Q&A patterns** with example threads and where they got answered. Built for documentation teams and support engineers who want to find FAQ gaps fast. Part of Ian Mu's 100-actor portfolio at [github.com/ianymu](https://github.com/ianymu). Inspired by the post-Stop self-verification flow at [github.com/ianymu/claude-verify-before-stop](https://github.com/ianymu/claude-verify-before-stop). ### What does Claude Code Help Channel Aggregator do? Given a list of sources (Reddit subreddits, GitHub repos with Discussions), the Actor pulls all question-style threads created within the last N days, normalizes their titles, extracts keyword fingerprints, and **clusters threads that ask the same thing**. Each cluster is ranked by frequency + engagement (upvotes + comment count) and the top N clusters are emitted to the dataset along with 3 example threads per cluster and a hint about how the question was answered (accepted answer, comment count, or 'none'). Discord is intentionally excluded because the official Claude Code Discord is invite-only and private. Apify platform advantages: built-in cron scheduling, dataset API access, retry handling, integrations with Slack and webhooks - all included so you can wire the output straight into your docs CMS or a weekly digest. ### Why use Claude Code Help Channel Aggregator? - **FAQ gap detection**: Find which questions keep recurring weekly and write them into the docs once and for all. - **Support engineering**: Build a canned-answer playbook from the real questions users ask. - **Product feedback loop**: Surface the same bug/confusion appearing across Reddit and GitHub Discussions simultaneously. - **Documentation prioritization**: Rank by `rankScore` to know what to write first. ### How to use Claude Code Help Channel Aggregator 1. Open the Input tab. 2. Accept the defaults (last 30 days, Reddit + GitHub Discussions) or customize sources/subreddits/repos. 3. For GitHub Discussions, add a `GITHUB_TOKEN` environment variable on the Actor's Settings tab (any GitHub personal access token with public-repo scope works). Without it, only Reddit is scanned. 4. Hit Start. Reddit is unauthenticated and rate-limited; GitHub uses the GraphQL API. 5. Inspect the Output tab. Each row = one question cluster with examples and a rank score. 6. Schedule it weekly to track shifts in the help conversation over time. ### Input | Field | Type | Default | Description | |---|---|---|---| | `sources` | array | `["reddit", "github-discussions"]` | Channels to aggregate. | | `daysBack` | int | `30` | How many days back to scan. | | `subreddits` | array | `["ClaudeAI", "Anthropic"]` | Subreddits without the `r/` prefix. | | `githubRepos` | array | 3 anthropics/* repos | `owner/name` format. Requires GITHUB_TOKEN. | | `topN` | int | `25` | Top N clusters to return. | ### Output Each dataset item is one question cluster: ```json { "clusterKey": "error|claude|code|terminal", "keywords": ["error", "claude", "code", "terminal", "node"], "questionCount": 7, "totalScore": 41, "totalComments": 23, "sources": ["reddit", "github-discussions"], "rankScore": 24.6, "examples": [ { "title": "Claude Code errors out on Node 18 in WSL2 - what's the fix?", "url": "https://www.reddit.com/r/ClaudeAI/comments/abc123/...", "source": "reddit", "sourceId": "r/ClaudeAI", "score": 12, "commentCount": 5, "answeredBy": "5 comment(s)", "createdAt": "2026-05-12T08:15:00Z" } ] } ```` You can download the dataset in JSON, HTML, CSV, or Excel. ### Data table | Field | Meaning | |---|---| | `clusterKey` | Sorted keyword fingerprint used to merge similar questions | | `keywords` | Top extracted keywords for this cluster | | `questionCount` | How many threads belong to this cluster | | `totalScore` | Sum of upvotes across cluster threads | | `totalComments` | Sum of comments across cluster threads | | `sources` | Which sources contributed to this cluster | | `rankScore` | Frequency + log-engagement composite score for sorting | | `examples` | Up to 3 representative threads with title, URL, score, and how-it-was-answered | ### Pricing / Cost estimation A typical run (2 subreddits + 3 GitHub repos, 30 days) is well under one compute unit on Apify Free; runtime is a couple minutes. Weekly scheduling stays comfortably inside the free tier. ### Tips and advanced options - **Token in env**: Add `GITHUB_TOKEN` via Actor Settings > Environment variables. The Actor reads `process.env.GITHUB_TOKEN`. - **Tighter clusters**: shorten `daysBack` to 7 to get a true 'this week' view. - **More repos**: append community-maintained Claude Code repos to `githubRepos` to widen coverage. - **Discord caveat**: official Claude Code Discord is invite-only; we do not scrape it. If you have a public mirror, treat it as another 'subreddit' equivalent in your own fork of this Actor. ### FAQ, disclaimers, and support - **Public data only.** Reddit and GitHub public posts are used; no private channels. - **Rate limits.** Reddit is 1 req/1.5s unauthenticated; GitHub GraphQL is bounded by token rate limits. - **Clustering is heuristic.** Keyword-fingerprint clustering merges most paraphrases but isn't semantic; the examples field always lets you verify. - **Issues / feature requests**: open an issue at [github.com/ianymu](https://github.com/ianymu). - **Verification methodology**: developed with the post-Stop verification flow at [github.com/ianymu/claude-verify-before-stop](https://github.com/ianymu/claude-verify-before-stop). Author: Ian Mu - One-person AI company experiments at [github.com/ianymu](https://github.com/ianymu). # Actor input Schema ## `sources` (type: `array`): Channels to aggregate. Supported: 'reddit', 'github-discussions'. ('discord' is not supported because the official Claude Code Discord is invite-only.) ## `daysBack` (type: `integer`): How many days back to aggregate. ## `subreddits` (type: `array`): Subreddits to scan (no 'r/' prefix). ## `githubRepos` (type: `array`): owner/name format. Requires GITHUB\_TOKEN env var on Apify Cloud for the Discussions GraphQL API. ## `topN` (type: `integer`): Return the top N most-repeated question clusters. ## Actor input object example ```json { "sources": [ "reddit", "github-discussions" ], "daysBack": 30, "subreddits": [ "ClaudeAI", "Anthropic" ], "githubRepos": [ "anthropics/claude-code", "anthropics/anthropic-cookbook", "anthropics/claude-code-action" ], "topN": 25 } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("ianymu/claude-code-help-channel-aggregator").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("ianymu/claude-code-help-channel-aggregator").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 ianymu/claude-code-help-channel-aggregator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ianymu/claude-code-help-channel-aggregator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Claude Code Help Channel Aggregator", "description": "Aggregate Claude Code help discussions across Reddit (r/ClaudeAI) and GitHub Discussions on claude-code repos. Cluster recurring questions, rank by frequency and engagement, return top Q&A patterns with example answers. Useful for docs teams and support engineers.", "version": "0.0", "x-build-id": "TyRD9wTticXQDzOBB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ianymu~claude-code-help-channel-aggregator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ianymu-claude-code-help-channel-aggregator", "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/ianymu~claude-code-help-channel-aggregator/runs": { "post": { "operationId": "runs-sync-ianymu-claude-code-help-channel-aggregator", "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/ianymu~claude-code-help-channel-aggregator/run-sync": { "post": { "operationId": "run-sync-ianymu-claude-code-help-channel-aggregator", "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", "properties": { "sources": { "title": "Sources", "type": "array", "description": "Channels to aggregate. Supported: 'reddit', 'github-discussions'. ('discord' is not supported because the official Claude Code Discord is invite-only.)", "default": [ "reddit", "github-discussions" ], "items": { "type": "string" } }, "daysBack": { "title": "Days back", "minimum": 1, "maximum": 365, "type": "integer", "description": "How many days back to aggregate.", "default": 30 }, "subreddits": { "title": "Subreddits", "type": "array", "description": "Subreddits to scan (no 'r/' prefix).", "default": [ "ClaudeAI", "Anthropic" ], "items": { "type": "string" } }, "githubRepos": { "title": "GitHub repos for Discussions", "type": "array", "description": "owner/name format. Requires GITHUB_TOKEN env var on Apify Cloud for the Discussions GraphQL API.", "default": [ "anthropics/claude-code", "anthropics/anthropic-cookbook", "anthropics/claude-code-action" ], "items": { "type": "string" } }, "topN": { "title": "Top N clusters", "minimum": 5, "maximum": 100, "type": "integer", "description": "Return the top N most-repeated question clusters.", "default": 25 } } }, "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 } } } } } } } } } } ```