twitter-search

仓库: sundial-org/awesome-openclaw-skills
安装量: 82
排名: #9599

安装

npx skills add https://github.com/sundial-org/awesome-openclaw-skills --skill twitter-search
Twitter Search and Analysis
Overview
Search Twitter for keywords using advanced search syntax, fetch up to 1000 relevant tweets, and analyze the data to produce professional reports with insights, statistics, and actionable recommendations.
Prerequisites
API Key Required
Users must configure their Twitter API key from
https://twitterapi.io
The API key can be provided in three ways:
Environment variable
(recommended): Set
TWITTER_API_KEY
in your
~/.bashrc
or
~/.zshrc
echo
'export TWITTER_API_KEY="your_key_here"'
>>
~/.bashrc
source
~/.bashrc
As an argument
Use
--api-key YOUR_KEY
with the wrapper script
Passed directly
As first argument to the Python script Quick Start Using the Wrapper Script (Recommended) The wrapper script automatically handles environment variable loading and dependency checks:

Basic search (uses TWITTER_API_KEY from shell config)

./scripts/run_search.sh "AI"

With custom API key

./scripts/run_search.sh "AI" --api-key YOUR_KEY

With options

./scripts/run_search.sh " \" Claude AI \" " --max-results 100 --format summary

Advanced query

./scripts/run_search.sh "from:elonmusk since:2024-01-01" --query-type Latest Direct Python Script Usage

Search for a keyword

scripts/twitter_search.py " $API_KEY " "AI"

Search with multiple keywords

scripts/twitter_search.py " $API_KEY " " \" ChatGPT \" OR \" Claude AI \" "

Search from specific user

scripts/twitter_search.py " $API_KEY " "from:elonmusk"

Search with date range

scripts/twitter_search.py " $API_KEY " "Bitcoin since:2024-01-01" Advanced Queries

Complex query: AI tweets from verified users, English only

scripts/twitter_search.py " $API_KEY " "AI OR \" machine learning \" lang:en filter:verified"

Recent crypto tweets with minimum engagement

scripts/twitter_search.py " $API_KEY " "Bitcoin min_retweets:10 lang:en"

From specific influencers

scripts/twitter_search.py " $API_KEY " "from:elonmusk OR from:VitalikButerin since:2024-01-01" Output Format

Full JSON with all tweets

scripts/twitter_search.py " $API_KEY " "AI" --format json

Summary with statistics (default)

scripts/twitter_search.py
"
$API_KEY
"
"AI"
--format
summary
Options
--max-results N
Maximum tweets to fetch (default: 1000)
--query-type Latest|Top
Sort order (default: Top for relevance)
--format json|summary
Output format (default: summary) Workflow 1. Understand User Requirements Clarify the analysis goal: What topic/keyword to search? Date range preference? Specific users to include/exclude? Language preference? Type of insights needed (trends, sentiment, influencers)? 2. Build the Search Query Use Twitter Advanced Search syntax: Syntax Example Description keyword AI Single keyword "phrase" "machine learning" Exact phrase OR AI OR ChatGPT Either term from:user from:elonmusk From specific user to:user to:elonmusk Reply to user since:DATE since:2024-01-01 After date until:DATE until:2024-12-31 Before date lang:xx lang:en Language code

hashtag

AI

Hashtag
filter:links
filter:links
Tweets with links
min_retweets:N
min_retweets:100
Minimum retweets
3. Fetch Data
Execute the search script:
scripts/twitter_search.py
"
$API_KEY
"
"YOUR_QUERY"
--max-results
1000
--query-type Top
Important
Default is 1000 tweets maximum. The script automatically:
Paginates through all available results
Stops at 1000 tweets (API limit consideration)
Handles errors gracefully
4. Analyze and Generate Report
After fetching data, produce a comprehensive professional report with:
Report Structure
Executive Summary
(2-3 sentences)
What was searched
Key findings overview
Data Overview
Total tweets analyzed
Date range of data
Query parameters used
Key Metrics
Total engagement (likes, retweets, replies, quotes, views)
Average engagement per tweet
Language distribution
Reply vs. original tweet ratio
Top Content Analysis
Most retweeted tweets (with
URL links
to original tweets)
Most liked tweets (with
URL links
to original tweets)
Top hashtags with frequency
Most mentioned users
Selected tweet examples with full URL references
Influencer Analysis
Top users by follower count
Most active users
Verified user percentage
Trend Insights
(based on data patterns)
Emerging themes
Sentiment indicators
Temporal patterns
Conversation drivers
Key Takeaways
3-5 bullet points of core insights
Data-backed conclusions
Actionable Recommendations
Specific, implementable suggestions
Based on the data findings
Prioritized by impact
Analysis Guidelines
Be data-driven
Every claim should reference actual metrics
Provide context
Explain why metrics matter
Identify patterns
Look for trends across the dataset
Stay objective
Present facts, avoid speculation
Be specific
Recommendations should be concrete and actionable
Consider external context
Use web search for background when relevant 5. Output Format Present the report in clear markdown with: Headers for each section Tables for structured data Bullet points for lists Bold for key metrics Code blocks for tweet examples Clickable URLs for all referenced tweets (format: @username ) Tweet URL Format Always include clickable links to tweets: | Author | Tweet | URL | |

|

|

|
|
@user
|
Summary of tweet content
|
[
View
](
https://x.com/user/status/123456
)
|
Or inline format:
-
**
@username
**
Tweet summary - View Tweet Query Examples by Use Case Trend Analysis "AI" OR "artificial intelligence" lang:en min_retweets:50 Competitor Monitoring from:competitor1 OR from:competitor2 since:2024-01-01 Product Launch Tracking

ProductName OR "Product Name" lang:en filter:verified

Crisis Monitoring

BrandName OR "Brand Name" lang:en --query-type Latest

Influencer Discovery

Topic lang:en min_retweets:100 min_faves:500

Sentiment Analysis
"brand name" OR #BrandName lang:en --max-results 1000
Resources
scripts/run_search.sh (Wrapper Script)
Convenience wrapper that handles environment variable loading and dependency checks:
Automatically loads
TWITTER_API_KEY
from
~/.bashrc
or
~/.zshrc
Checks Python availability and installs missing dependencies
Provides user-friendly error messages
Supports all command-line options from the Python script
Usage
:
./scripts/run_search.sh
<
query
>
[
options
]
Options
:
--api-key KEY
Override environment variable API key
--max-results N
Maximum tweets to fetch (default: 1000)
--query-type Latest|Top
Sort order (default: Top)
--format json|summary
Output format (default: json)
scripts/twitter_search.py
Executable Python script that:
Fetches tweets from Twitter API
Handles pagination automatically
Extracts key tweet metrics
Calculates aggregate statistics
Outputs structured JSON data
Usage
:
scripts/twitter_search.py
<
api_key
>
<
query
>
[
options
]
references/twitter_api.md
Comprehensive API documentation including:
Complete parameter reference
Query syntax guide
Response structure details
Pagination instructions
Best practices for analysis
Error handling guide
Read this when
Building complex queries or understanding data structure. Tips for Better Analysis Use Top query type for trend analysis (more relevant results) Set date filters for timely insights Filter by language for accurate text analysis Include minimum engagement to filter noise Combine with web search to validate trends Look beyond metrics - analyze content themes Track hashtags to identify sub-conversations Identify influencers by combining followers + engagement Error Handling If the script fails: Check API key validity Verify query syntax Ensure network connectivity Check rate limits (if applicable) Review error messages for specific issues
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