Enforce Skeptic protocol rules for question-first data analysis with YAML artifacts.
npx skills add https://github.com/Filivignaga/skeptic --skill skeptic-protocol
Please help me install this Agent Skill. Skill: skeptic-protocol Source: https://github.com/Filivignaga/skeptic/tree/main/codex/skeptic-protocol Command: npx skills add https://github.com/Filivignaga/skeptic --skill skeptic-protocol
Skeptic-protocol enforces auditable, constraint-driven management of analysis stages, preventing arbitrary decisions and ensuring reproducible workflows across the Skeptic lifecycle.
Start skeptic-protocol after formulate to enforce protocol rules and drive Cycles A–D with deterministic, auditable outputs.
High-intent search queries and answers about installing and using this skill.
Auditable data analysis requires locking data-usage modes, validation requirements, and prohibitions across cycles. This constraint-driven protocol generates canonical YAML artifacts and cycle-evidence records to ensure reproducible workflows and prevent arbitrary analytical decisions.
Automating question-first data analysis involves invoking autonomous execution modes to drive analysis cycles. This reduces manual prompts while maintaining governance through locked validation requirements, leakage rules, and backtracking triggers across the lifecycle.
To prevent data leakage in reproducible analysis, lock data-usage modes and leakage rules after formulating questions. Enforcing these constraints via canonical YAML stage memory ensures traceability and prevents leakage across backtracking cycles.
Track backtracking by generating compact cycle-evidence artifacts that record validation states and prohibitions. This constraint-driven management ensures every stage transition and backtracking trigger across the analysis lifecycle is traceable and auditable.
Lock validation requirements immediately after the formulate stage to enforce constraint-driven management. Applying these prohibitions and backtracking triggers early ensures deterministic, reproducible execution across all subsequent analysis cycles.
Canonical YAML stage memory artifacts are required to support traceability and auditing in data analysis workflows. They lock data-usage modes and evidence logic, ensuring the entire analysis lifecycle remains reproducible and auditable.
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