Delegate cognitive decisions to the model while enforcing deterministic shell IO.
npx skills add https://github.com/naramore/dotai --skill zfc-naramore
Please help me install this Agent Skill. Skill: zfc Source: https://github.com/naramore/dotai/tree/main/claude/skills/zfc Command: npx skills add https://github.com/naramore/dotai --skill zfc-naramore
Delegate cognitive decision-making to the model while enforcing deterministic IO in the shell. The shell handles IO, schemas, budgets, and retries, ensuring a predictable execution path while the model performs reasoning.
Launch the ZFC-enabled environment to wrap your agent loop so all judgment is delegated to the model, while the host shell manages IO and safety.
High-intent search queries and answers about installing and using this skill.
To enforce deterministic IO in an agent loop, use a thin shell architecture that isolates cognition in the model. The shell strictly manages schemas, budgets, and retries, ensuring predictable execution paths while the model handles reasoning.
The best way to apply guardrails to LLM output is to wrap execution in a deterministic host shell. This enforces input/output validation, policy enforcement, and mechanical transforms, ensuring safe and auditable behavior for consuming applications.
Handle retries and output schemas for tool-use harnesses by delegating cognitive decisions to the model while the surrounding shell mechanically enforces strict output schemas, manages budgets, and executes safe retries.
Yes, a deterministic shell architecture works for AI applications requiring structural safety. It isolates judgment inside the model while the shell manages mechanical data handling, providing the strong guardrails and predictable behavior needed.
You should avoid a thin deterministic shell for model delegation if your application does not require strict output schemas, predictable execution paths, or enforced budgets. It is designed for scenarios demanding structural safety and auditable behavior.
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