Convert vague business domains into executable AI agent specifications.
npx skills add https://github.com/Adgmed2018/visa --skill visa
Please help me install this Agent Skill. Skill: visa Source: https://github.com/Adgmed2018/visa/tree/main/agents/visa Command: npx skills add https://github.com/Adgmed2018/visa --skill visa
Visa acts as the central orchestrator that turns ambiguous business ideas into executable AI agent specifications, bridging domain discovery and code-ready guidance for engineering teams.
_visa_sdd/ output structure to enable downstream tooling (Spec Kit, Reversa integration) while preserving project files.Start by typing '/visa' to begin the discovery workflow and follow the prompts to map your domain and start the discovery process.
High-intent search queries and answers about installing and using this skill.
To turn vague business ideas into executable AI agent specifications, you need an orchestration pipeline that translates market signals and stakeholder input into versioned, canonical specs. This process targets product discovery workflows to produce concrete, testable specifications across an agent network.
Orchestrating product discovery workflows with AI agents involves deploying specialized agents like ethnographers, strategists, and inspectors to convert conversations and constraints into operational contracts. This pipeline maps your domain and outputs artifacts such as canonical IDs, domain models, and acceptance criteria for automated handoff.
Yes, generating design tokens and acceptance criteria for multi-agent orchestration is possible through a specialized agent pipeline. The workflow produces operational contracts and artifacts aligned for automated handoff to code agents, maintaining a parallel output structure to enable downstream tooling integration.
Yes, you need a compatible agent framework to generate AI agent specifications. The discovery workflow requires a compatible framework and a SKILL.md file with name and description frontmatter, while optional references and assets folders can guide execution.
The best way to convert stakeholder input into testable specs for engineering teams is to orchestrate specialized AI agents that process evidence and constraints. This approach bridges domain discovery and code-ready guidance, producing versioned canonical specifications that agents can execute.
Limitations when using AI agents for product domain discovery include the requirement for a compatible agent framework and structured SKILL.md frontmatter. The orchestration preserves project files by maintaining a parallel output structure, but downstream tooling like Spec Kit requires this specific directory format to function.
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Adgmed2018Generate engineering-ready handoff documents with domain models, flows, architecture decisions, and traceability to _visa_sdd directory.
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