Stats
Actions
Available In
Tags
Plugin
pm-advanced
Synthesise multi-source user signals into weighted insight briefs with confidence ratings, design and evaluate A/B experiments with statistical rigor, structure AI/ML product decisions on a canvas, conduct ethical reviews of AI features with risk scoring, and transform feature briefs into ready-to-use design handoff docs for designers
What's Inside
Skills5
ai-ethics-review
/ai-ethics-review
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety, accountability, and societal impact with a risk tier score, pre-deployment checklist, and prioritised mitigations.
ai-product-canvas
/ai-product-canvas
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
design-handoff-brief
/design-handoff-brief
Transform feature briefs into structured design briefs that give designers the context they need before opening Figma. Use when asked to write a design brief, create a design handoff, brief a designer on a new feature, or translate a PRD into design requirements. Produces a brief with user goal, emotional context, success criteria, constraints, edge cases, and out-of-scope boundaries.
experiment-designer
/experiment-designer
Design statistically rigorous A/B tests and interpret experiment results. Use when asked to design an experiment, run an A/B test, calculate sample size, interpret test results, or assess whether an experiment was successful. Produces a complete experiment design with hypothesis, sample size, run time, success criteria, and risk flags β or a results interpretation with ship/iterate/kill recommendation.
multi-source-signal-synthesiser
/multi-source-signal-synthesiser
Synthesises user signals from multiple research sources into a unified, weighted insight brief. Use when you have data from interviews, support tickets, NPS verbatims, app reviews, or sales calls and need to reconcile contradictions, surface the underlying need behind requests, or answer 'what are users really telling us'. Produces ranked insights with confidence ratings, source weighting rationale, divergent signal analysis by user segment, and a research gap identification section.
Stats
Version3.0.0
Released
LanguageShell
Stars973
Forks176
MaintenanceExcellent
LicenseMIT
Last Commit
Added
Available In
Tags
Technologies
Categories
