Platform Strategy

Scope

Covers

• Internal platforms (paved roads, shared infrastructure/services) treated as products
• External/hybrid platforms (APIs, extensions, partners) and ecosystem strategy
• Platform lifecycle strategy (when to open vs when to close for control/monetization)
• Platform surface-area design (interfaces, abstractions, governance) to reduce cognitive load for product teams
• AI platform defensibility (context repositories + integrated “toolkit” experiences) when relevant

When to use

• “Create a platform strategy for our developer platform / API.”
• “Turn our internal platform into a product with clear users, metrics, and roadmap.”
• “We want to open our platform to third parties—define incentives, governance, and a rollout.”
• “We’re building an AI platform—what’s the defensible system beyond a single feature?”

When NOT to use

• You don’t have a clear problem/job-to-be-done yet (use problem-definition first).
• You primarily need a product/company strategy and portfolio plan (use ai-product-strategy).
• You’re selecting a vendor/tool rather than defining a platform strategy (use evaluating-new-technology).
• You need an implementation design/architecture doc (use writing-specs-designs after this).
• You need infrastructure capacity planning, service mesh architecture, or deployment topology (use platform-infrastructure).
• You need a design system with component libraries and token governance (use design-systems).
• You need to set API pricing tiers or monetization models specifically (use pricing-strategy after this skill defines the platform shape).

Inputs

Minimum required

• Platform type: internal / external / hybrid (and who “the platform owner” is)
• Primary users/consumers (e.g., developers, data scientists, partners) and their top jobs-to-be-done
• Current state: what exists today (surfaces, APIs, services, docs), and what’s broken/painful
• Business intent: why now, desired outcomes (speed, reliability, revenue, ecosystem leverage), time horizon
• Constraints: security/privacy/compliance, SLOs, regions, budgets, resourcing, dependencies
• Decision context: who decides, what decisions are on the table (open/close, pricing, governance), target date

Missing-info strategy

• Ask up to 5 questions from references/INTAKE.md (3–5 at a time).
• If still missing, proceed with explicit assumptions and present 2–3 strategy options (e.g., internal-only vs partner beta vs public API).
• Do not request secrets or credentials. Require explicit confirmation for any production changes or external outreach.

Outputs (deliverables)

Produce a Platform Strategy Pack (in chat; or as files if requested), in this order:

1. Platform Product Charter (users, jobs, non-goals, assumptions, outcomes)
2. Platform Surface & Interface Map (capabilities, owners, APIs/SDKs, “paved road” defaults, boundaries)
3. Lifecycle Stage & Open/Close Strategy (stage diagnosis, stage-appropriate moves, transition risks)
4. Moat & Ecosystem Model (compounding loops, incentives, seeding plan, investment gates)
5. Governance & Policy Plan (what’s open/closed, SLAs, deprecation, partner rules, pricing/packaging if relevant)
6. Metrics & Operating Model (platform-as-product operating cadence, intake, support, adoption + productivity metrics)
7. 12‑month Roadmap (milestones, bets, sequencing, dependencies)
8. Risks / Open questions / Next steps (always included)

Templates: references/TEMPLATES.md

Workflow (8 steps)

1) Define the platform as a product (users + jobs + outcomes)

• Inputs: Platform context, primary user groups, current pain.
• Actions: Write the platform’s “user promise” and top 3–5 jobs-to-be-done. Add 3–5 non-goals. Choose 2–4 outcome metrics (prefer developer productivity metrics like cycle time).
• Outputs: Draft Platform Product Charter.
• Checks: You can describe value without naming internal components (“We reduce X minutes of toil per deploy”).

2) Diagnose the platform lifecycle stage (and what decisions are truly on the table)

• Inputs: Market/organization conditions, competitive context (if external), timeline.
• Actions: Determine the most likely stage (Step 0→3) and list evidence. Clarify the “open vs close” decision(s) you must make now (not someday).
• Outputs: Lifecycle Stage & Open/Close Strategy (draft).
• Checks: The stage is justified with evidence, not aspiration (“we should be a platform”).

3) Map surface area and define boundaries (reduce decision complexity)

• Inputs: Existing services/APIs, teams, dependency graph, common failures.
• Actions: Inventory platform capabilities; define what becomes a paved road vs optional. Specify boundaries: what platform owns vs domain teams own. Draft interface contracts (APIs/SDKs/events) and “default decisions” the platform makes for others.
• Outputs: Platform Surface & Interface Map.
• Checks: A domain team can build without re-deciding foundational choices (auth, logging, deployment, guardrails).

4) Identify the moat and the compounding loop(s)

• Inputs: Unique assets, distribution, data/context advantages, ecosystem participants.
• Actions: Propose 1–3 moat hypotheses and at least one compounding loop (“if this works, it accelerates”). Define incentives for each participant and a small seeding plan. Define “investment gates” (signals that justify more spend).
• Outputs: Moat & Ecosystem Model.
• Checks: The loop has measurable leading indicators (activation, retained developers, successful integrations).

5) Decide what to open, how to govern it, and how to protect the core

• Inputs: Stage, risks, support capacity, security/compliance requirements.
• Actions: Specify what’s open now vs later, plus governance: access control, quotas, review processes, partner rules, SLAs, deprecation/backwards compatibility, and (if relevant) pricing/packaging. Include an “abuse/quality” plan (observability, enforcement).
• Outputs: Governance & Policy Plan.
• Checks: “Open” surfaces have a sustainability plan (support, docs, incident response, versioning).

6) (If AI platform) Build defensibility as a system, not a feature

• Inputs: AI use cases, context sources, data sensitivity, required integrations.
• Actions: Design a “Swiss‑army toolkit” system: shared context repository + multiple experiences (autocomplete, chat, agent workflows) with consistent policies. Define permissions, audit logs, eval/monitoring, and human-in-the-loop points.
• Outputs: AI section inside Platform Product Charter + Governance & Policy Plan updates.
• Checks: The plan improves outcomes while containing risk (least privilege, auditable access, measurable quality).

7) Define metrics + operating model (platform-as-product)

• Inputs: Target outcomes, resourcing constraints, stakeholder map.
• Actions: Define a metric stack (north-star + input metrics) and an operating cadence (intake, prioritization, roadmap reviews, documentation, support/on-call, feedback loops). Ensure a PM/owner exists for internal platforms.
• Outputs: Metrics & Operating Model.
• Checks: Metrics tie to user outcomes (not vanity counts like “# of services migrated” alone).

8) Sequence the roadmap and quality-gate the pack

• Inputs: All draft artifacts.
• Actions: Create a 12‑month roadmap with 3 horizons (Now / Next / Later). Add dependencies, resourcing, and rollback/exit paths. Run references/CHECKLISTS.md and score with references/RUBRIC.md. Always include Risks / Open questions / Next steps.
• Outputs: Final Platform Strategy Pack.
• Checks: A stakeholder can make a decision (owner, date, next actions) and understand trade-offs.

Quality gate (required)

• Use references/CHECKLISTS.md and references/RUBRIC.md.
• Always include: Risks, Open questions, Next steps.

Examples

Example 1 (internal platform): “Use platform-strategy to create a platform strategy for an internal ML platform used by 40 engineers. Goal: cut model deployment cycle time from 2 weeks to 2 days. Constraints: PII present; SOC2; 2 platform engineers; 6‑month horizon.”
Expected: platform-as-product charter + paved-road interfaces + productivity metrics + governance for AI data.

Example 2 (external ecosystem): “Use platform-strategy to define an API platform strategy for opening our analytics product to partners. We want 20 high-quality integrations in 12 months without breaking core reliability.”
Expected: stage diagnosis + open/close decisions + incentives + governance/versioning + roadmap.

Boundary example 1: “We should become a platform like Apple—make us a platform strategy.” Response: out of scope without specific users/jobs and a plausible compounding loop; ask intake questions and/or start with problem-definition.

Boundary example 2: “Design the microservice architecture and deployment topology for our platform.” Response: redirect to platform-infrastructure or writing-specs-designs. This skill defines the strategic what/why/who of the platform, not the implementation architecture.

Anti-patterns (common failure modes)

1. Platform-as-aspiration -- Declaring “we are a platform” without identifying specific platform users, their jobs-to-be-done, or a compounding loop. A platform strategy requires named consumers (internal teams, external developers, partners) with measurable adoption signals.
2. Premature openness -- Opening APIs, extension points, or partner programs before the core product is stable and before governance (versioning, deprecation, support) is in place. This creates ecosystem debt that compounds faster than ecosystem value.
3. Moat-by-assertion -- Claiming network effects or switching costs without describing the causal loop that produces them. Every moat hypothesis must have a measurable leading indicator and an investment gate.
4. Infrastructure project disguised as strategy -- Producing a technical migration plan (re-platform to Kubernetes, adopt event sourcing) instead of a strategic document about user value, lifecycle stage, and ecosystem design. Keep implementation in downstream skills.
5. Governance vacuum -- Defining what the platform exposes without defining who decides what changes, how breaking changes are communicated, what SLAs are guaranteed, and how abuse is handled. Governance is not optional for platforms with external consumers.