apify-brand-reputation-monitoring

Brand Reputation Monitoring

Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.

When to Use

• You need to monitor reviews, ratings, or brand mentions across social, travel, or map platforms.
• The task is to select and run an Apify Actor for brand sentiment or reputation tracking.
• You need exported monitoring results and a summary of reputation signals.

Prerequisites

Verify prerequisites before any paid run.

• .env file with APIFY_TOKEN
• Node.js 20.6+ (for native --env-file support)
• An approved Apify connector or Console access to inspect the chosen Actor and its schema; the bundled runner uses REST directly.
• Keep .env untracked and private; configure the token locally without pasting it into chat or command arguments.

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Determine data source (select Actor)
- [ ] Step 2: Fetch Actor schema via an approved connector or Console
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the monitoring script
- [ ] Step 5: Summarize results

Step 1: Determine Data Source

Select the appropriate Actor based on user needs:

Step 2: Fetch Actor Schema

Inspect the current Actor details and input schema using the authenticated Apify connector available in the session, or its Console page. Discover actual tool names and parameters before calling them. Do not parse .env with shell substitution or pass a token as a CLI header argument. Treat the Actor table as discovery candidates, not verified availability or schemas.

This returns:

• Actor description and README
• Required and optional input parameters
• Output fields (if available)

Step 3: Ask User Preferences

Before running, ask:

1. Output format:
   • Quick answer - Display top few results in chat (no file saved)
   • CSV - Bounded export, union of returned fields
   • JSON - Bounded export in JSON format
2. Number of results: Based on character of use case

Step 4: Run the Script

Quick answer (display in chat, no file):

node --env-file=.env "<resolved-skill-directory>/reference/scripts/run_actor.js" \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

CSV:

node --env-file=.env "<resolved-skill-directory>/reference/scripts/run_actor.js" \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

JSON:

node --env-file=.env "<resolved-skill-directory>/reference/scripts/run_actor.js" \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json

Step 5: Summarize Results

After completion, report:

• Number of reviews/mentions found
• File location and name
• Key fields available
• Suggested next steps (sentiment analysis, filtering)

Error Handling

APIFY_TOKEN not found - Ask user to create .env with APIFY_TOKEN=your_token Connector unavailable - Inspect the schema through the Console; do not claim an unavailable tool was called. Actor not found - Check Actor ID spelling Run FAILED - Ask user to check Apify console link in error output Timeout - The local timeout does not abort the remote run. Inspect the printed run ID in Console before retrying to avoid duplicate charges.

Run Boundaries

Before launch, confirm the selected Actor, authorized public-data scope, current input schema, result limits and pricing with the user’s request. Respect source terms and access controls; do not collect private group data or unnecessary personal contact information. Start with a small Actor-supported input limit. --max-items limits downloaded output only (default 1000); it does not cap Actor work or billing. The helper sends a skill-identifying User-Agent to Apify. Resolve the installed skill directory in the command placeholders. Output files must be new paths; exports preserve long values and heterogeneous fields. Quick answers fetch at most five records; minimize or redact personal fields before sharing.

Deduplicate reviews by platform and stable ID, record rating scale and retrieval time, and distinguish source rating from inferred sentiment. Counts from different platforms are not comparable without coverage and scale definitions. Preserve negative, neutral and uncertain cases; validate language/sarcasm on a small human-reviewed sample before labeling a trend. A single scrape is a snapshot, not continuous monitoring; scheduling and notifications require an actual configured service and authorized destination. Do not post replies, contact reviewers or remove content as part of data collection.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.