> ## Documentation Index > Fetch the complete documentation index at: https://lurkai.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Lurk Score ## What Lurk Scores are Lurk Scores are a scoring layer built on top of Track Records. While Track Records show the underlying history, Lurk Scores turn that history into a faster credibility signal. The goal is to help users quickly understand whether a trader, strategy, source, or signal history appears reliable based on documented performance. ## How Lurk Scores relate to Track Records Track Records are the evidence. Lurk Scores are the summary. A Track Record may show: * past calls * resolved outcomes * win/loss history * market categories * confidence levels * timestamps * notes * performance over time A Lurk Score takes that history and creates a cleaner high-level signal that is easier to compare. A Lurk Score should never replace the Track Record. It should point users toward it. ## What Lurk Scores are for Use Lurk Scores when you want to: * quickly compare credibility * identify stronger signal sources * filter noisy users or strategies * review performance at a glance * decide whether a Track Record deserves deeper review * understand whether someone has been consistently useful over time Lurk Scores are designed to save time, not make decisions automatically. ## What may affect a Lurk Score A Lurk Score may consider factors like: * accuracy * consistency * sample size * recency * specificity of calls * category performance * confidence calibration * resolved versus unresolved entries * quality of documented reasoning * performance across different market types The score should reward useful, specific, and historically grounded signal. It should not reward volume alone. ## Why sample size matters A small Track Record can look impressive without proving much. Someone who gets 2 out of 2 calls right may have a perfect record, but not enough history to prove consistency. Lurk Scores should account for this by weighing larger, cleaner records more heavily than tiny records with limited evidence. ## Why recency matters Markets change. A user or strategy that performed well a year ago may not be as useful today. Lurk Scores may weigh recent performance more strongly while still preserving long-term history inside the full Track Record. ## Why category matters A person may be strong in one area and weak in another. For example, someone may perform well in: * politics * sports * crypto * macro * entertainment * breaking news markets But that does not mean their signal quality transfers across every category. A good Lurk Score should help users understand general credibility while still allowing category-specific review. ## What a Lurk Score is not A Lurk Score is not: * a guarantee of future performance * a trading recommendation * proof that someone is always right * a replacement for reviewing the underlying record * a measure of popularity alone * a reward for posting constantly A high score means the available record appears stronger based on Lurk’s scoring logic. It does not mean the next call will be right. ## Using Lurk Scores A typical workflow: 1. Find a trader, source, strategy, or signal. 2. Check the Lurk Score for a quick credibility read. 3. Open the full Track Record. 4. Review the underlying calls and outcomes. 5. Check sample size, category, and recency. 6. Decide whether the signal deserves attention. ## Best practices Use Lurk Scores as a filter, not a final answer. Before trusting a score, check: * how many entries support it * whether the record includes losses * how recent the performance is * which market categories it applies to * whether the person makes specific calls * whether the score is based on resolved outcomes A score with strong supporting history is more useful than a score built on limited activity. ## Common issues ### “Why does someone with a high win rate have a lower Lurk Score?” Their sample size may be small, their calls may be vague, or their performance may not be recent enough. Win rate is only one part of credibility. ### “Why does someone with losses still have a strong Lurk Score?” Losses are normal. A strong record can include losses if the overall history shows useful judgment, good calibration, clear reasoning, and consistent performance. ### “Why did a Lurk Score change?” Scores may update as: * markets resolve * new calls are added * old calls become less recent * entries are corrected * additional performance data becomes available ### “Can a Lurk Score be wrong?” Yes. A Lurk Score is a tool for summarizing evidence. It depends on available data, scoring logic, and the quality of the underlying Track Record. Always review the full record when the decision matters. ## Important note Lurk Scores are credibility signals. They help users evaluate documented history faster, but they do not guarantee future results or replace independent judgment. Use them as a starting point for deeper review.