The twitter skill retrieves live Twitter/X data through twitterapi.io, including user profiles, tweets, replies, followers, following, communities, spaces, trends, and search results. Use it for factual Twitter usage, account research, and social media verification instead of guessing from prompts.
This skill scores 72/100, which is enough to list for directory users who want a Twitter/X-specific tool with real retrieval actions. It has a clear trigger, many concrete endpoint scripts, and a valid frontmatter description, but the install decision should note that operational guidance is thinner than ideal and setup depends on an external API key and service.
- Clear triggerability: the frontmatter says to use it when the user mentions Twitter, X, or tweets, and the skill is explicitly scoped to search and retrieve Twitter/X content.
- Substantial workflow coverage: the repository includes many runnable scripts for users, tweets, replies, followers/following, communities, spaces, trends, threads, and search.
- Low guesswork for basic invocation: SKILL.md provides direct command examples and a quick check using get_user_info.py.
- Setup depends on an external TwitterAPI.io key in TWITTERAPI_API_KEY, so the skill is not plug-and-play without credentials.
- The repository evidence shows strong command coverage but limited higher-level workflow guidance; there are no install commands, rules files, or richer progressive-disclosure aids.
Overview of twitter skill
What the twitter skill does
The twitter skill helps you retrieve live Twitter/X data through twitterapi.io, including user profiles, tweets, replies, followers, following, communities, spaces, and trends. It is best for agents that need factual social-media lookup rather than generative guessing.
Who should install it
Install the twitter skill if you need social proof, account research, post context, audience checks, or tweet-level analysis for Social Media workflows. It fits users who want structured answers from Twitter/X instead of manually browsing the app.
Why it is useful
The main advantage is breadth with a scriptable interface: one skill covers users, tweet threads, relationship checks, and discovery/search endpoints. That makes the twitter guide useful for quick verification tasks, monitoring, and content research where plain prompts often miss exact IDs, limits, or pagination.
How to Use twitter skill
Install and set up access
Use the recommended install flow: npx skills add ReScienceLab/opc-skills --skill twitter. Then set TWITTERAPI_API_KEY in your shell environment, because the scripts depend on that credential to call the API. If the key is missing, the skill will not be able to fetch data.
Start with the right files
Read SKILL.md first for the supported commands, then inspect scripts/twitter_api.py and the specific script you plan to run. For faster orientation, also check scripts/credential.py for auth handling and scripts/get_user_info.py, scripts/search_tweets.py, and scripts/get_tweet_thread.py for the most common Twitter usage patterns.
Turn a rough request into a usable prompt
Good inputs name the entity, the lookup type, and the output shape. For example: “Use the twitter skill to find recent tweets from @elonmusk and summarize the last 10 posts by topic and engagement signals.” Better still, specify whether you need latest tweets, replies, followers, or relationship checks, because each endpoint expects different identifiers and limits.
Practical workflow for better results
Choose the narrowest command that matches the job: get_user_info.py for identity, get_user_tweets.py for posting history, search_tweets.py for topic discovery, and get_tweet_thread.py for conversation context. For Twitter/X research, verify one source of truth first, then expand to related endpoints like replies, quotes, or followers if you need supporting evidence.
twitter skill FAQ
What can the twitter skill fetch?
It can fetch user info, tweets, replies, followers, following, verified followers, communities, spaces, trends, search results, and relationship data. It also supports article extraction from tweet IDs when a post links to long-form content.
Is twitter skill only for Twitter/X posts?
Mostly yes, but it is broader than a single post lookup. The twitter skill also helps with account research and network context, which is useful when your task is about influence, audience, or community behavior rather than one tweet.
When should I not use it?
Do not use it if you only need a casual summary, if you do not have API access, or if the task does not require live Twitter/X data. It is also a poor fit when the source material is already available in your own dataset and no external lookup is needed.
Is it beginner-friendly?
Yes, if you start with one endpoint and one clear question. Beginners usually get the best twitter install outcome by testing get_user_info.py or search_tweets.py first, then moving to more specific scripts once they understand the input format.
How to Improve twitter skill
Give the skill the exact object and goal
The strongest inputs name the username, tweet ID, community ID, or search phrase, plus the outcome you want. For example, “Find the latest 20 tweets from @openai and group them by launch, research, and hiring” is better than “analyze OpenAI on Twitter.”
Match the endpoint to the question
Common failure mode: asking for “Twitter activity” without saying whether you want tweets, replies, mentions, or followers. To improve twitter usage, map the request to the script family first. Use search for discovery, user endpoints for profile/history, and tweet endpoints for conversation context.
Add constraints that matter
Specify limits, time windows, and output style when they affect the answer. For example, ask for “last 50 tweets,” “only replies,” or “relationship between two accounts” so the skill does not return too much noise. This is especially important for twitter for Social Media research, where recency and source type change the result.
Iterate from one lookup to the next
After the first result, refine with a second query instead of restarting broadly. If a user profile looks relevant, check followers or mentions; if a tweet is important, inspect replies, quotes, and thread context. That workflow usually produces a cleaner final answer than one oversized prompt.