GitHub
x-post-optimizer
Use this skill when advising on post structure, timing, media selection, and account health to align with the HeavyRanker's scoring weights and anti-spam heuristics.
ElemontCapital AI Skills
GitHub ↗Explorez les skills IA publiés de ElemontCapital et consultez leurs instructions source.
Statistiques de l’auteur
Skills indexés
8Étoiles des dépôts des skills indexés
8Forks du dépôt
0Moyenne des étoiles par skill indexé
1Les étoiles d’un dépôt peuvent se répéter entre ses skills.
Catégories populaires
Indexation récente
Explorer
GitHub
x-data-signals
Use this skill to decode the "DNA" of the recommendation engine. These signals are the foundational data structures used to retrieve candidates (before ranking) and as features for the Heavy Ranker (during rank...
GitHub
x-architecture
Use this skill when reasoning about the distributed system design, service orchestration, or request lifecycle of the X recommendation engine. It is essential for tasks involving the "For You" timeline construc...
GitHub
x-dev-engineering
Use this skill when implementing new features or debugging the core "For You" pipeline. The system has shifted from legacy heuristics toward a unified **Rust-based** pipeline powered by the **Phoenix** transfor...
GitHub
x-ranking-engine
Use this skill when you need to reason about the machine learning models that determine the final order of the "For You" timeline. It is essential for tasks involving model feature engineering, tuning engagemen...
GitHub
x-experimental-ops
Use this skill when reasoning about how the algorithm is tuned, why different users see different behaviors, or how "Success" is defined by X's engineering team.
GitHub
x-safety-filtering
Use this skill when analyzing why content is not appearing, checking for "shadowban" conditions, or understanding the safety compliance layer. This skill covers the `VisibilityLib` logic that runs *after* candi...
GitHub
x-retrieval-systems
Use this skill when analyzing how the system narrows down 500 million daily tweets to a candidate pool of ~1,500. This is the "top-of-funnel" engineering that determines what is even eligible to be ranked.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.