tracking-product-launch-buzz-on-twitter
Tracks product launch buzz and reactions on Twitter using apidojo's Twitter Search scraper. Triggers when the user asks to: track product launch buzz on Twitter, monitor Twitter reactions to a product launch, measure product launch sentiment on X, find tweets about a new product release, track how a product launch is being received on Twitter, monitor competitor product announcements on Twitter, or analyze the social media impact of a product launch. Returns tweet volume, sentiment distribution, top voices, geographic spread, and buzz score. Ideal for product marketing teams, PR professionals, and competitive analysts monitoring launches.
- Installs
- 739
- Last 24h
- +8
- Trending rank
- #9783
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill tracking-product-launch-buzz-on-twitter- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads tracking-product-launch-buzz-on-twitter automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Tracking Product Launch Buzz On Twitter
Executes tracking product launch buzz on twitter using apidojo scrapers. Part of the apidojo intelligence skills library.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
searchTerms | array | ✅ | [] | Twitter advanced search queries (e.g. ["#AI lang:en", "from:NASA"]) |
sort | string | Optional | Top | Sort order: Latest, Top, or Latest+Top |
tweetLanguage | string | Optional | — | ISO 639-1 language code (e.g. en) |
maxItems | number | Optional | Unlimited | Maximum tweets to return |
onlyVerifiedUsers | boolean | Optional | false | Only tweets from verified users |
onlyTwitterBlue | boolean | Optional | false | Only Twitter Blue subscribers |
onlyImage | boolean | Optional | false | Only tweets with images |
onlyVideo | boolean | Optional | false | Only tweets with videos |
onlyQuote | boolean | Optional | false | Only quote tweets |
author | string | Optional | — | Filter to a specific author handle |
inReplyTo | string | Optional | — | Tweets replying to a specific handle |
mentioning | string | Optional | — | Tweets mentioning a specific handle |
geotaggedNear | string | Optional | — | Tweets near a location |
withinRadius | string | Optional | — | Radius around geotaggedNear |
geocode | string | Optional | — | Lat/lng + radius string |
placeObjectId | string | Optional | — | Tweets tagged with a place |
minimumRetweets | number | Optional | — | Minimum retweet count |
minimumFavorites | number | Optional | — | Minimum like count |
minimumReplies | number | Optional | — | Minimum reply count |
start | string | Optional | — | Tweets after this date (YYYY-MM-DD) |
end | string | Optional | — | Tweets before this date (YYYY-MM-DD) |
includeSearchTerms | boolean | Optional | false | Add the matched search term to each tweet |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Workflow
Progress:
- [ ] Step 1: Define parameters
- [ ] Step 2: Run tweet-scraper
- [ ] Step 3: Filter and classify results
- [ ] Step 4: Score by quality and relevance
- [ ] Step 5: Deliver output
Step 2: Run the Actor
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.json --format json
APIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
"searchTerms": ["[PRODUCT] launch", "[PRODUCT] just launched", "new [PRODUCT]", "[PRODUCT] release"],
"maxItems": 100
}
REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"searchTerms": ["[PRODUCT] launch", "[PRODUCT] just launched", "new [PRODUCT]", "[PRODUCT] release"], "maxItems": 100}'
Wait for SUCCEEDED. Fetch dataset:
curl "https://api.apify.com/v2/actor-runs/$RUN_ID/dataset/items?token=$APIFY_TOKEN"
Step 3: Classify Results
classification: VIRAL (> 1000 mentions/hour) | HIGH_BUZZ (100-1000/hour) | MODERATE (10-100/hour) | LOW (< 10/hour)
Step 4: Score Each Result
score = buzz_score = tweet_volume_24h * 0.35 + weighted_sentiment * 0.35 + influencer_mention_count * 0.30
Step 5: Edge Cases
- Product launch tweets spike within 48h then decay — run within 24-72h of launch for peak signal; old launches produce misleading low volume
Additional fallbacks:
- < 20 results: Broaden search terms; remove secondary filters
- No results: Verify the search terms are correct; try alternate phrasings
- Data quality issues: Remove entries with missing key fields; note count in output
Output Format
# Tracking Product Launch Buzz On Twitter
Results: [N] | Date: [DATE]
| # | [Key Field] | [Metric 1] | [Metric 2] | [Classification] | [Score] |
|---|------------|-----------|-----------|-----------------|---------|
| 1 | [value] | [value] | [value] | [type] | [0.XX] |
## Summary
Top result: [description]
Key finding: [insight]
Troubleshooting
Too few results: Broaden the primary search term; remove restrictive filters. Low quality results: Apply minimum score threshold (≥ 0.50) to filter noise. Actor fails to run: Verify API key; check actor status at apify.com/apidojo.
FAQ
What does the tracking-product-launch-buzz-on-twitter skill do?
Tracks product launch buzz and reactions on Twitter using apidojo's Twitter Search scraper. Triggers when the user asks to: track product launch buzz on Twitter, monitor Twitter reactions to a product launch, measure product launch sentiment on X, find tweets about a new product release, track how a product launch is being received on Twitter, monitor competitor product announcements on Twitter, or analyze the social media impact of a product launch. Returns tweet volume, sentiment distribution, top voices, geographic spread, and buzz score. Ideal for product marketing teams, PR professionals, and competitive analysts monitoring launches.
How do I install tracking-product-launch-buzz-on-twitter?
Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill tracking-product-launch-buzz-on-twitter in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tracking-product-launch-buzz-on-twitter/ (personal) or .claude/skills/tracking-product-launch-buzz-on-twitter/ (project).
Which AI agents can use tracking-product-launch-buzz-on-twitter?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is tracking-product-launch-buzz-on-twitter safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains tracking-product-launch-buzz-on-twitter?
It is published by apidojo-io in apidojo-io/apidojo-skills. It has 739 recorded installs.
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