Consolidate six L4 audit verdicts into a reconciled argument graph.
npx skills add https://github.com/speplinski/hackathon-opus-47 --skill sot-reconcile
Please help me install this Agent Skill. Skill: sot-reconcile Source: https://github.com/speplinski/hackathon-opus-47/tree/main/skills/sot-reconcile Command: npx skills add https://github.com/speplinski/hackathon-opus-47 --skill sot-reconcile
Consolidates six L4 audit verdicts into a single, graph-based reconciliation artifact that surfaces cross-skill insights such as violations, corroborations, contradictions, tensions, and gaps for auditable design decisions.
Provide a cluster context and six L4 verdicts to produce a graph-based reconciliation artifact.
High-intent search queries and answers about installing and using this skill.
Design audit verdict reconciliation consolidates six L4 audit verdicts into a single graph-based structure. This process surfaces cross-skill alignments, tensions, and prioritized actions for auditable design decisions across Norman, Accessibility, Kahneman, Osterwalder, Cooper, and Garrett frameworks.
A cross-skill reconciliation graph is a unified structure that maps audit verdicts using node types for violation, corroboration, contradiction, tension, and gap. It preserves source verdicts for traceability while surfacing prioritized actions derived from overlapping design framework evaluations.
To create an argument graph from L4 audit data, provide a cluster context and six L4 verdicts as input. The system generates a graph-based reconciliation artifact that derives consumer-facing flat lists, including ranked violations, tensions, and gaps for downstream decision-making.
Yes, design audit reconciliation applies across Norman, Accessibility, Kahneman, Osterwalder, Cooper, and Garrett verdicts. It surfaces cross-skill tensions and contradictions between accessibility requirements, cognitive load evaluations, and business model frameworks into one auditable graph.
The best way to surface tensions and gaps across design frameworks is through graph-primary reconciliation. This approach maps contradictions and gaps as distinct node types, preserving source verdicts for traceability while deriving prioritized actions and ranked violations.
Generating a reconciliation graph requires six L4 audit verdicts representing Norman, Accessibility, Kahneman, Osterwalder, Cooper, and Garrett evaluations. The process consolidates these specific verdicts to surface cross-skill alignments, tensions, and prioritized actions accurately.
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