A specialized skill for extracting structured Reddit posts and comments to perform competitor research and brand sentiment analysis without hallucinations or CAPTCHA issues.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install reddit-competitor-analysis-api-skill
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install reddit-competitor-analysis-api-skill using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Reddit Competitor Analysis API Skill?
The Reddit Competitor Analysis API Skill is a robust data extraction tool designed for the Openclaw Skills ecosystem. It leverages the BrowserAct API to perform deep-dives into Reddit discussions, bypassing common hurdles like IP rate limits, geofencing, and complex CAPTCHAs. By using pre-set browser automation workflows, it ensures that the data retrieved is precise, factual, and free from the hallucinations often associated with pure AI scraping methods.
This skill is highly cost-effective and faster than traditional AI-driven automation, making it ideal for developers and researchers who need reliable social listening capabilities. It transforms messy Reddit threads into clean, structured datasets, allowing for sophisticated analysis of community feedback, trending topics, and competitor mentions within specific subreddits.
Reddit Competitor Analysis API Skill Use Cases
- Tracking competitor mentions and brand sentiment across diverse Reddit communities.
- Extracting nested comment threads for deep market research and qualitative analysis.
- Monitoring community feedback on specific product launches or industry topics.
- Finding trending posts and discussions based on specific keywords and timeframes.
- Compiling structured datasets of user reviews and social media activity for data science projects.
How Reddit Competitor Analysis API Skill Works
- The skill receives search keywords and optional filters (date range, sorting preference) from the user.
- It verifies the presence of a valid BROWSERACT_API_KEY environment variable to authenticate the request.
- A Python-based automation script is executed, triggering a BrowserAct workflow to navigate Reddit search results.
- The script monitors real-time status logs to track progress during the browser automation phase.
- Upon completion, the raw Reddit data is parsed into a structured format containing post details and nested comments.
- The final structured result is returned to the user or saved for further analysis.
Reddit Competitor Analysis API Skill Setup
To use this skill within the Openclaw Skills framework, ensure you have python3 installed and a valid API key from BrowserAct.
# Set your API Key
export BROWSERACT_API_KEY='your_api_key_here'
# Run the analysis script
python3 -u ./scripts/reddit_competitor_analysis_api.py "keyword" "Past week" "Relevance" "Best" 10
Reddit Competitor Analysis API Skill Data Schema & Taxonomy
The skill outputs comprehensive metadata for both posts and their associated comments. The data is organized as follows:
| Field | Description |
|---|---|
| post_title | The full title of the Reddit post |
| publish_time | Timestamp of when the post was created |
| like_count | Number of upvotes received by the post |
| comment_count | Total volume of comments in the thread |
| publish_channel | The subreddit where the content originated |
| comment_author | Username of the specific commenter |
| comments | The actual text body of the comment |
| comment_upvotes | Upvote count for the individual comment |
| comment_level | The depth of the comment in a nested thread |
| reply_to | Reference to the parent comment or post |
Reddit Competitor Analysis API Skill Advanced Features
- Background Execution: Support for running long-duration scrapes (up to 30 minutes) using background mode in Openclaw Skills.
- Intelligent Polling: Automated process polling to monitor task status without blocking the agent's workflow.
- Smart Error Handling: Built-in logic to distinguish between authorization errors and transient network failures.
- Automated Retry Mechanism: One-click automatic retry for failed tasks to ensure data collection reliability.
- Flexible Filtering: Advanced parameters for sorting by hotness, relevance, or comment count to fine-tune research results.
SKILL.md
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