adopt-ai-properly
Workflow recipe — the org-side AI adoption arc, policy to proof, by chaining 4 skills.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/HEAD/commands/adopt-ai-properly.md -o ~/.claude/commands/adopt-ai-properly.mdadopt-ai-properly.md
Run the **Adopt AI Properly** 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. Open with a one-line plan of the 4 stages, then ask once for essential missing inputs (regulatory exposure, current tool spend, which roles AI has changed most, the review framework in use). Don't re-ask between stages. Run each stage under a clear `## Stage N — <name>` heading: 1. **Set the rules** — apply the `ai-usage-policy` skill: the one-page policy with the data traffic-light (grounded in this org's real data classes), approved tools, disclosure lines, and the decision log for counsel. 2. **Redesign the roles** — apply the `role-redesign-for-ai` skill to the role(s) AI changed most: the before/after task inventory with verification counted as work, capacity deliberately reallocated, and the junior-ladder answer — consistent with the policy from stage 1. 3. **Fix the reviews** — apply the `ai-assisted-performance-review` skill: criteria that measure judgment, verification, outcomes, and leverage; calibration rules for uneven adoption; the three hard-case scripts — aligned to the charters from stage 2. 4. **Prove what paid** — apply the `ai-roi-audit` skill across the tool spend: per-tool verdicts with the measurement method behind each number, the hidden-cost ledger, and baseline plans for the unknowns. Close with a **leadership one-pager**: the policy headline, the role changes, the new review frame, and the renewal decisions — the packet that turns "we should figure out AI" into four signed-off documents.
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.