launch-an-ai-feature
Workflow recipe — take an AI/LLM feature from a probabilistic-aware PRD to a launch-ready model card by chaining 5 skills.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/HEAD/commands/launch-an-ai-feature.md -o ~/.claude/commands/launch-an-ai-feature.mdlaunch-an-ai-feature.md
Run the **Launch an AI Feature** workflow recipe for: $ARGUMENTS This is a *chain* of skills. Run each stage in order and **carry every stage's output forward as context** for the next — that shared context is the whole point. Open with a one-line plan of the 5 stages, then ask once for any essential missing inputs (the user problem, the stakes / cost of a wrong answer, the data available). Don't re-ask between stages. Run each stage under a clear `## Stage N — <name>` heading: 1. **Spec it** — apply the `ai-feature-prd` skill to turn the idea into a PRD built for a probabilistic system: the UX of uncertainty, guardrails, fallback behaviour, and an explicit quality bar tied to the stakes. 2. **Design the system** — apply the `rag-design-doc` skill (or note if an agent is the better fit — see `agent-spec`) to design retrieval/generation: chunking, retrieval, reranking, grounded answers, and the failure-mode table. 3. **Plan evaluation** — apply the `ai-eval-plan` skill to define datasets, rubrics, baselines, the explicit ship threshold, and the regression gate. 4. **Budget cost & latency** — apply the `llm-cost-latency-budget` skill for per-request token math, model tiering, caching, p95 targets, and spend guardrails. 5. **Document it** — apply the `model-card` skill to produce a launch-ready card: intended use, sliced evaluation, limitations, and a rollback trigger. Do not invent metrics, costs, or eval results — note assumptions instead. After the last stage, end with a 5-bullet **"What you now have"** recap linking each artifact to the stage that produced it.
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.
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.
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.
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 — or a results interpretation with ship/iterate/kill recommendation.
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.
Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action.
Interpret product metrics against goals and surface actionable signals. Use when asked to analyse product health, review key metrics, investigate a performance issue, produce a health report, or assess product-market fit signals. Produces a structured health report with RAG status, trend analysis, root cause hypotheses, and prioritised actions.
Structure a retention analysis, churn investigation, or engagement deep-dive for any product team. Use when asked to analyse user retention, investigate churn, measure DAU/MAU, or build a retention improvement plan. Produces a retention snapshot with root cause hypotheses, aha-moment correlation, and prioritised interventions.