{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_tweetspear","slug":"tweetspear","name":"Tweetspear","type":"product","url":"https://tweetspear.com","page_url":"https://unfragile.ai/tweetspear","categories":["text-writing"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_tweetspear__cap_0","uri":"capability://planning.reasoning.tweet.performance.prediction.scoring","name":"tweet-performance-prediction-scoring","description":"Analyzes draft tweets against historical engagement patterns from the user's account and audience cohort to predict likely performance metrics (engagement rate, reach potential) before posting. Uses machine learning models trained on tweet embeddings, hashtag patterns, posting time, and audience interaction history to score content quality and viral potential. The system compares incoming tweets against a learned baseline of what resonates with that specific audience rather than generic viral patterns.","intents":["I want to know if a tweet will perform well before I publish it","I need to understand which tweet variations will get more engagement from my specific followers","I want to optimize my posting strategy based on what my audience actually engages with"],"best_for":["emerging creators with 1K-50K followers seeking data-driven posting decisions","small business accounts testing messaging without hiring social media consultants","content creators wanting to reduce low-performing tweet volume"],"limitations":["Prediction accuracy depends on historical tweet volume — accounts with <100 tweets have limited training data for personalized models","Cannot overcome poor content quality; a well-predicted low-quality tweet still underperforms","Predictions are probabilistic and may miss emerging trends or algorithm changes on Twitter's platform","Does not account for external events, viral moments, or real-time context shifts"],"requires":["Active Twitter account with minimum posting history (estimated 20-50 tweets)","Connected Twitter API access (OAuth 2.0 or equivalent)","Account age of at least 2-4 weeks for meaningful engagement baseline"],"input_types":["text (tweet draft)","structured metadata (hashtags, mentions, media attachments)"],"output_types":["numerical score (0-100 or percentile)","predicted engagement metrics (estimated likes, retweets, replies)","categorical recommendation (high/medium/low potential)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tweetspear__cap_1","uri":"capability://data.processing.analysis.audience.demographic.segmentation.analysis","name":"audience-demographic-segmentation-analysis","description":"Extracts and categorizes follower demographics (inferred from public profiles, engagement patterns, and interaction metadata) into cohorts based on interests, location, engagement level, and follower type (bot vs. authentic). Uses natural language processing on follower bios, profile descriptions, and interaction history to infer audience segments. Segments are then used to tailor content recommendations and identify which audience groups engage most with specific tweet topics.","intents":["I want to understand who my followers actually are and what they care about","I need to know which audience segments engage most with different content types","I want to identify my most valuable followers and tailor content to them"],"best_for":["creators building niche communities who need to understand audience composition","small businesses optimizing messaging for specific customer segments","accounts with 500+ followers where demographic patterns become statistically meaningful"],"limitations":["Demographic inference relies on public profile data, which is incomplete and often inaccurate","Cannot access private account information or direct follower survey data","Bot detection is heuristic-based and may misclassify legitimate accounts with unusual activity patterns","Segmentation quality degrades for accounts with highly diverse or international audiences where cultural context is lost"],"requires":["Twitter account with minimum 500 followers for statistically meaningful segments","Read access to follower list and profile metadata via Twitter API","Account must have been active for at least 30 days to accumulate engagement signals"],"input_types":["follower profile metadata (bio, location, follower count, following count)","engagement history (likes, retweets, replies from followers)","tweet interaction patterns"],"output_types":["audience segment definitions (JSON or structured format)","demographic breakdowns (percentages by inferred interest, location, engagement tier)","segment-specific engagement metrics"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tweetspear__cap_2","uri":"capability://data.processing.analysis.optimal.posting.time.recommendation","name":"optimal-posting-time-recommendation","description":"Analyzes historical engagement data from the user's tweets to identify time windows (hour of day, day of week) when their specific audience is most active and responsive. Uses time-series analysis on engagement metrics (likes, retweets, replies) correlated with posting timestamps to find statistically significant peaks. Accounts for timezone distribution of followers and seasonal patterns in engagement.","intents":["I want to know the best times to post for maximum engagement with my followers","I need to schedule tweets when my audience is most likely to see them","I want to understand how posting time affects my engagement metrics"],"best_for":["creators with consistent posting history (20+ tweets) seeking to optimize timing","accounts with geographically concentrated followers where timezone patterns are clear","content creators who can adjust posting schedule based on data"],"limitations":["Requires sufficient historical data (minimum 20-50 tweets) to identify statistically significant patterns; new accounts lack this baseline","Patterns may be noisy for accounts with low engagement or irregular posting frequency","Does not account for content type variation — optimal time for news tweets may differ from entertainment tweets","Twitter's algorithm changes and feed ranking shifts can invalidate historical timing patterns"],"requires":["Twitter account with minimum 20 historical tweets with engagement data","Access to tweet timestamp and engagement metrics via Twitter API","At least 2-4 weeks of posting history to establish baseline patterns"],"input_types":["historical tweet data (timestamp, engagement metrics)","follower timezone distribution (inferred from profile data)"],"output_types":["recommended posting times (hour of day, day of week)","engagement lift percentage for optimal vs. suboptimal times","heatmap visualization of engagement by time"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tweetspear__cap_3","uri":"capability://planning.reasoning.content.topic.recommendation.engine","name":"content-topic-recommendation-engine","description":"Recommends tweet topics and content themes based on analysis of the user's highest-performing tweets and audience interests. Uses topic modeling (LDA or similar) on tweet text combined with engagement metrics to identify which themes (e.g., 'industry news', 'personal stories', 'how-to content') drive engagement. Matches identified audience interests (from demographic analysis) with content themes to suggest topics the audience cares about but the creator hasn't covered.","intents":["I want to know what topics I should tweet about to engage my audience","I need ideas for content that will resonate with my followers","I want to identify content gaps where I could grow engagement"],"best_for":["creators struggling with content ideation or topic selection","accounts with established audiences but unclear content strategy","creators seeking to expand into new topics their audience cares about"],"limitations":["Topic recommendations are constrained by creator's existing tweet history — cannot recommend topics completely outside their past content","Relies on audience interest inference which may be inaccurate for niche or specialized audiences","Does not account for creator expertise or willingness to cover recommended topics","Topic modeling quality degrades for accounts with very short tweets or highly technical jargon"],"requires":["Twitter account with minimum 50 historical tweets for meaningful topic modeling","Engagement metrics associated with historical tweets","Audience demographic/interest data from segmentation capability"],"input_types":["historical tweet text and engagement metrics","audience segment definitions and interests","creator's past content themes"],"output_types":["ranked list of recommended topics with engagement potential scores","content gap analysis (topics audience cares about but creator hasn't covered)","example tweet themes or angles for recommended topics"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tweetspear__cap_4","uri":"capability://data.processing.analysis.hashtag.strategy.optimization","name":"hashtag-strategy-optimization","description":"Analyzes hashtag usage patterns in the user's high-performing tweets and recommends hashtag combinations that maximize reach and engagement. Uses hashtag co-occurrence analysis and engagement correlation to identify which hashtags drive visibility and which are ineffective for that specific account. Provides recommendations on hashtag count, placement, and specific tags to use or avoid based on audience and niche.","intents":["I want to know which hashtags will help my tweets get more visibility","I need to understand if I'm using too many or too few hashtags","I want to find relevant hashtags my audience actually follows"],"best_for":["creators in hashtag-dependent niches (photography, fitness, lifestyle) where tag strategy matters","accounts with 50+ tweets to establish hashtag performance patterns","creators seeking to improve discoverability without changing content"],"limitations":["Hashtag effectiveness varies by niche and changes over time as tags become saturated or fall out of use","Cannot measure reach directly from hashtags (Twitter API doesn't expose hashtag-specific impressions); recommendations are based on correlation with engagement","Recommendations may be biased toward hashtags already used by the creator, limiting discovery of new relevant tags","Hashtag strategy effectiveness is secondary to content quality — poor tweets won't perform well regardless of hashtag optimization"],"requires":["Twitter account with minimum 50 historical tweets with hashtag usage","Engagement metrics for each tweet","Access to tweet text and metadata via Twitter API"],"input_types":["historical tweet text with hashtags","engagement metrics per tweet","account niche/category (optional)"],"output_types":["recommended hashtags ranked by effectiveness score","hashtag count recommendation (optimal number per tweet)","hashtag placement guidance (beginning, middle, end of tweet)","hashtags to avoid (low-performing or oversaturated)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tweetspear__cap_5","uri":"capability://data.processing.analysis.engagement.pattern.tracking.monitoring","name":"engagement-pattern-tracking-monitoring","description":"Continuously monitors and tracks engagement metrics (likes, retweets, replies, impressions) over time to identify trends, anomalies, and performance changes. Stores historical engagement data and compares current performance against baseline to alert users to significant changes (e.g., sudden drop in engagement, viral tweet). Uses time-series analysis to detect trend breaks and statistical anomalies.","intents":["I want to track how my engagement metrics change over time","I need to know if my engagement is declining or improving","I want to be alerted when a tweet performs unusually well or poorly"],"best_for":["creators who post regularly and want to monitor performance trends","accounts seeking to understand the impact of content changes or strategy shifts","creators who want early warning of engagement decline"],"limitations":["Requires continuous data collection and storage, which may have latency (Twitter API rate limits mean data may be 15-60 minutes delayed)","Anomaly detection may produce false positives during platform-wide algorithm changes or viral events","Historical data is only as good as the retention period — long-term trend analysis requires months of data collection","Cannot distinguish between engagement changes caused by content quality vs. external factors (algorithm changes, platform outages)"],"requires":["Twitter account with active posting history","Continuous API access to pull engagement metrics (requires scheduled data collection)","Data storage for historical metrics (Tweetspear backend or user's own database)"],"input_types":["tweet engagement metrics (likes, retweets, replies, impressions)","tweet metadata (timestamp, content, hashtags)"],"output_types":["engagement trend charts (over days, weeks, months)","anomaly alerts (unusual performance spikes or drops)","baseline metrics and performance comparisons","trend analysis (improving, declining, stable)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tweetspear__cap_6","uri":"capability://data.processing.analysis.follower.growth.rate.analysis","name":"follower-growth-rate-analysis","description":"Analyzes follower growth rate over time and correlates growth spikes with specific tweets, content themes, or posting patterns. Identifies which types of content drive follower acquisition and which periods show accelerated or stalled growth. Uses growth rate decomposition to separate organic growth from external factors (mentions, retweets from large accounts).","intents":["I want to understand what drives my follower growth","I need to know which tweets or content types attract new followers","I want to identify periods of growth acceleration and understand what caused them"],"best_for":["creators focused on audience growth metrics","accounts with 500+ followers where growth patterns become visible","creators seeking to understand ROI of content strategy changes"],"limitations":["Follower growth attribution is difficult — a tweet may drive growth indirectly through retweets by large accounts, not direct engagement","Cannot measure quality of followers acquired (bot followers vs. engaged authentic followers)","Growth patterns may be driven by external factors (mentions in newsletters, media coverage) not captured in tweet data","Requires 2-4 weeks of data to establish meaningful growth baselines"],"requires":["Twitter account with minimum 500 followers and 2+ weeks of growth history","Daily follower count snapshots (requires scheduled data collection)","Historical tweet and engagement data"],"input_types":["daily follower count","tweet engagement metrics","tweet content and themes"],"output_types":["follower growth rate trend (daily, weekly, monthly)","correlation analysis (which content drives growth)","growth attribution (organic vs. external mentions)","growth acceleration periods and contributing factors"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_tweetspear__cap_7","uri":"capability://text.generation.language.tweet.draft.refinement.suggestions","name":"tweet-draft-refinement-suggestions","description":"Provides real-time suggestions to improve tweet drafts before posting, including recommendations on length, tone, clarity, and engagement potential. Analyzes draft text against the user's high-performing tweets to suggest phrasing improvements, emoji placement, and structural changes. Uses NLP to assess readability, sentiment, and alignment with audience expectations.","intents":["I want feedback on my tweet before I post it","I need suggestions to make my tweet more engaging or clear","I want to ensure my tone matches my audience's expectations"],"best_for":["creators seeking real-time writing feedback without external editors","accounts with established voice/style where consistency matters","creators wanting to improve tweet quality incrementally"],"limitations":["Suggestions are based on statistical patterns, not creative judgment — may miss clever wordplay or novel angles","Cannot assess whether a tweet is factually accurate or appropriate for current events","Refinement suggestions may homogenize voice toward 'average' high-performing style rather than encouraging unique voice","Effectiveness depends on quality of training data (user's own high-performing tweets)"],"requires":["Twitter account with minimum 50 historical tweets for style baseline","Real-time access to draft text (browser extension or web interface)"],"input_types":["tweet draft text","optional: intended audience or content theme"],"output_types":["specific refinement suggestions (phrasing, length, emoji placement)","readability score","predicted engagement impact of suggested changes","tone/style alignment assessment"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Active Twitter account with minimum posting history (estimated 20-50 tweets)","Connected Twitter API access (OAuth 2.0 or equivalent)","Account age of at least 2-4 weeks for meaningful engagement baseline","Twitter account with minimum 500 followers for statistically meaningful segments","Read access to follower list and profile metadata via Twitter API","Account must have been active for at least 30 days to accumulate engagement signals","Twitter account with minimum 20 historical tweets with engagement data","Access to tweet timestamp and engagement metrics via Twitter API","At least 2-4 weeks of posting history to establish baseline patterns","Twitter account with minimum 50 historical tweets for meaningful topic modeling"],"failure_modes":["Prediction accuracy depends on historical tweet volume — accounts with <100 tweets have limited training data for personalized models","Cannot overcome poor content quality; a well-predicted low-quality tweet still underperforms","Predictions are probabilistic and may miss emerging trends or algorithm changes on Twitter's platform","Does not account for external events, viral moments, or real-time context shifts","Demographic inference relies on public profile data, which is incomplete and often inaccurate","Cannot access private account information or direct follower survey data","Bot detection is heuristic-based and may misclassify legitimate accounts with unusual activity patterns","Segmentation quality degrades for accounts with highly diverse or international audiences where cultural context is lost","Requires sufficient historical data (minimum 20-50 tweets) to identify statistically significant patterns; new accounts lack this baseline","Patterns may be noisy for accounts with low engagement or irregular posting frequency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:33.649Z","last_scraped_at":"2026-04-05T13:23:42.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=tweetspear","compare_url":"https://unfragile.ai/compare?artifact=tweetspear"}},"signature":"WqaTY4sXnBTdtjtU/DolQ+nN3iWjhFuxAiIgA1UsOiJvvrmhxQjsFmwWmq55ujVieWSxgKWKEQot0eqO+E39Cg==","signedAt":"2026-06-22T07:54:17.335Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tweetspear","artifact":"https://unfragile.ai/tweetspear","verify":"https://unfragile.ai/api/v1/verify?slug=tweetspear","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}