Infer and render conversational trajectories as diagrams, code, or type signatures.
npx skills add https://github.com/hafley66/claude-research --skill unify-hafley66
Please help me install this Agent Skill. Skill: unify Source: https://github.com/hafley66/claude-research/tree/main/skills/unify Command: npx skills add https://github.com/hafley66/claude-research --skill unify-hafley66
This skill helps users crystallize and render the underlying structure of a conversational trajectory, turning ideas into concrete, visible forms (diagrams, code, type signatures) to aid reasoning and decision-making.
Render the current conversational trajectory as a concrete structure (diagrams, code, or type signatures) to reveal the underlying pattern.
High-intent search queries and answers about installing and using this skill.
To turn conversational ideas into concrete diagrams and type signatures, you can infer the underlying structure of a trajectory and render it as code blocks or diagrammatic boxes. This helps crystallize loosely described ideas into visible forms.
The best way to replay a conversation as a structured proof trace is to process the turns as Prolog-style queries with bindings. This method exposes goals, open variables, and the final open goal during rendering.
Yes, you can render TypeScript type signatures from an unstructured conversational trajectory. The system adapts to multi-domain notation, allowing you to infer structure and output it directly as TypeScript code blocks.
Yes, this approach supports diagrammatic boxes for architecture rendering. It applies flexible notation to adapt to your domain, outputting concrete representations as diagrams, code, or structured views.
You should use pattern recognition to unify ideas when a user is circling an idea without landing on a concrete form. It helps infer the hidden structure and render it as visible diagrams or type signatures.
bzellmanProcesses work items into GitHub issues via automated research and routing.
crissdiamondValidate claims and analyze systems with proof recipes for architecture workflows.
dy9759Automate creation and optimization of specialized skills with standardized templates.
ZurybrExtract PyTorch .pth weights and implement native C/C++ inference.
