ship-an-mcp-server
Workflow recipe — make your product agent-usable by chaining 4 skills, spec to pricing.
mkdir -p ~/.claude/commands && curl -fsSL https://raw.githubusercontent.com/mohitagw15856/pm-claude-skills/HEAD/commands/ship-an-mcp-server.md -o ~/.claude/commands/ship-an-mcp-server.mdship-an-mcp-server.md
Run the **Ship an MCP Server** 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 any essential missing inputs (the product's top user jobs, the existing API surface, the riskiest actions, current pricing model). Don't re-ask between stages. Run each stage under a clear `## Stage N — <name>` heading: 1. **Spec the server** — apply the `mcp-server-spec` skill: a task-shaped toolset (3-10 tools, never an API mirror), auth model, gated actions, the never-exposed list, and the agent test plan. 2. **Audit readiness** — apply the `agent-readiness-audit` skill across all six surfaces (discovery, docs, API/auth, errors, onboarding, guardrails), quoting the failing artifacts; feed the spec from stage 1 so the audit checks against what will actually ship. 3. **Fix the pricing** — apply the `agent-era-pricing` skill: the value metric that survives non-human users, tier fences with named gaming vectors, and cannibalisation math on representative cohorts — informed by which tools stage 1 exposed and what usage they'll drive. 4. **Design the oversight** — apply the `human-in-the-loop-design` skill to every gated action from stage 1: action tiers, the approval budget, escalation rules, and the audit trail. Close with a **one-page rollout summary**: the toolset, the top 3 readiness fixes, the pricing decision, and the approval policy — the packet an eng lead and a pricing owner could execute from.
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.
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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.
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