Analyze AI project requirements for risks across prompts, models, and data flows.
npx skills add https://github.com/selcukyucel/north-starr-genai --skill ai-invert
Please help me install this Agent Skill. Skill: ai-invert Source: https://github.com/selcukyucel/north-starr-genai/tree/main/skills/ai-invert Command: npx skills add https://github.com/selcukyucel/north-starr-genai --skill ai-invert
This Skill provides automated AI inversion analysis to identify and mitigate risks before implementing AI-powered work, ensuring safer, more reliable AI deployments.
Invoke the ai-invert workflow on a clear requirement to trigger the inversion analysis.
High-intent search queries and answers about installing and using this skill.
AI inversion analysis identifies risks in AI project requirements before implementation by analyzing prompts, models, and data flows to determine edge cases, guardrails, and failure modes across prompts, RAG pipelines, and outputs.
To run an AI inversion workflow, invoke the workflow on a clear requirement. This spawns a dedicated inversion analysis agent on a separate thread to evaluate risks across multiple dimensions and produce an INVERT plan document with a concise summary of top risks and recommended next steps.
Yes, you can use AI inversion analysis to evaluate prompt fragility and hallucination risks. It assesses risk across ten dimensions including user impact, data handling, cost, reasoning, model dependency, guardrails, architecture, and virtue trade-offs to ensure safer, more reliable AI deployments.
The AI inversion analysis produces a .plans/INVERT-<name>.md document and a concise summary. This output provides decision support by detailing top risks, failure modes, and recommended next steps for remediation or further planning before implementation begins.
AI inversion analysis is designed for complex AI tasks touching prompts, models, or RAG pipelines. It should not be used for simple, non-AI software tasks, as its risk dimensional analysis targets specific AI failure modes like hallucination, prompt fragility, and model dependency.
William189189Define UI patterns and data models for multi-threaded AI chat interfaces.
StepowskiEricMap and invert failure modes into guardrails and a failure-map.md artifact.
Cure-Consulting-GroupManage LLM operations with prompt versioning, evaluation pipelines, cost controls, guardrails, and RAG monitoring.
rjroyChallenge user ideas with adversarial questioning to surface hidden assumptions.
