Generate human-readable explanations for AI decisions and graph relationships.
npx skills add https://github.com/Hawksight-AI/semantica --skill explain-hawksight-ai
Please help me install this Agent Skill. Skill: explain Source: https://github.com/Hawksight-AI/semantica/tree/main/plugins/skills/explain Command: npx skills add https://github.com/Hawksight-AI/semantica --skill explain-hawksight-ai
This Skill provides transparent explanations for decisions, rules, and graph analytics, helping users understand AI outputs with traceability and human-readable reasoning.
Request an explanation of decision id 12345 or graph node 'node_1' with causal details to understand the reasoning process.
High-intent search queries and answers about installing and using this skill.
To generate human-readable explanations for AI decisions, you can request detailed reasoning behind specific decision IDs. This process traces outputs back to source data, providing step-by-step rationale for transparency and trust.
Causal tracing is a mechanism that explains graph relationships by mapping how nodes connect. It identifies causal chains and supporting evidence within the graph, making complex analytics understandable for audit purposes.
To audit a decision support system, query the specific decision ID to request an explanation. The system produces an understandable, step-by-step rationale that traces the reasoning process back to the original source data.
Yes, you can get traceability insights for complex graph analytics. The system generates detailed explanations of node connections, including causal chains and supporting evidence, to aid in understanding graph structures.
Yes, producing detailed reasoning requires explanation generation modules and causal tracing functions. These components work together to generate transparent insights for decisions, rules, and graph analytics.
Automated explanations for decision auditing depend on the availability of source data and defined rules. Limitations arise when underlying reasoning processes lack clear causal chains or when graph nodes lack supporting evidence for traceability.
Hawksight-AIQuery semantic graphs as of specific dates and reconstruct historical states.
michaelarutyunovExport simulated interview artifacts into a timestamped folder under reports/interviews.
kyopark2014Convert a folder of files into a navigable knowledge graph with HTML, JSON, and report outputs.
jadzoghaibAttribute feature importance scores to explain machine learning model predictions.
