Analyze user requests for clarity, safety, and strategic alignment.
npx skills add https://github.com/lshtram/core_dev --skill strong-prompt
Please help me install this Agent Skill. Skill: strong-prompt Source: https://github.com/lshtram/core_dev/tree/main/.agent/skills/strong-prompt Command: npx skills add https://github.com/lshtram/core_dev --skill strong-prompt
This skill analyzes user requests to ensure clarity, safety, and strategic alignment, preventing prompt fatigue.
Audit this user request for clarity, safety, and strategic alignment and generate a plan if needed.
High-intent search queries and answers about installing and using this skill.
You analyze a prompt for clarity and safety by classifying user intent, scoring clarity, and running safety checks on sensitive domains like authentication or payments to ensure strategic alignment and prevent prompt fatigue.
Generating a strategic plan from an ambiguous request involves performing a clarity assessment, flagging risks, and requesting necessary clarifications to guide AI orchestration and resolve misalignment effectively.
The best way to audit prompt requests for sensitive data handling is to apply safety checks that classify intent and flag risks in domains like authentication and payments, ensuring safe and aligned AI orchestration.
You need to request clarifications for an AI prompt when the clarity score indicates high ambiguity, intent misalignment is detected, or strategic planning requires resolving flagged risks before execution.
Iterative planning helps prevent prompt fatigue by applying mode-based decision making, auditing requests for clarity and strategic alignment, and generating structured plans to guide downstream AI orchestration.
Crispy13Draft SKILL.md files with frontmatter, instructions, and test workflows.
burgebjCoordinate structured multi-model debates to identify risks and synthesize recommendations.
45black-LimitedQuery and manage the State of the Art registry for quality tools and patterns.
DuranndClassify user requests by intent, domain, and complexity for agent routing.
