Enforce evidence-based verification and safety gates before and after coding actions.
npx skills add https://github.com/sstklen/yes.md --skill yes
Please help me install this Agent Skill. Skill: yes Source: https://github.com/sstklen/yes.md/tree/main/skills/yes Command: npx skills add https://github.com/sstklen/yes.md --skill yes
This Skill addresses the common pitfalls and bad habits of AI coding assistants, ensuring safer, more evidence-based, and thorough results by enforcing strict rules and verification steps.
Use the yes skill when you need to ensure AI follows strict evidence and safety protocols before making any changes.
High-intent search queries and answers about installing and using this skill.
To prevent AI guessing during debugging, enforce evidence-based reasoning protocols that require verifiable data for all claims before applying fixes. This ensures the AI gathers concrete evidence rather than deflecting or making surface-level assumptions.
Ripple effect awareness in AI code generation ensures that fixes are verified for downstream and related impacts. It is needed to prevent unintended consequences in complex applications by checking that a localized fix does not introduce new bugs elsewhere.
Implement safety gates for AI-assisted deployment by applying mandatory verification checks before critical actions. This governance mechanism requires strict adherence to evidence-gathering protocols before and after file modifications to prevent unintended consequences.
AI governance applies to all software engineering task types including debugging, implementation, configuration, and deployment. It enforces layered discipline across any task by requiring strict adherence to verification protocols before and after actions.
AI assistants introduce new bugs when fixing complex application errors due to a lack of ripple effect awareness and evidence-based verification. Without enforced safety gates, the AI applies surface fixes without checking downstream impacts or gathering verifiable data.
v1bh0rDefines safety, validation, and recovery protocols for AI-assisted research.
sstklenEnforce AI safety, evidence-based reasoning, and thoroughness across debugging, implementation, configuration, and deployment tasks.
philo-grovesConstruct static analysis graphs for reachability, guard dominance, and path feasibility.
klu-devAutomate code formatting, linting, and AI-based fixes during file edits.
