Platform Strategy

Pack Availability Guard

When recommending a skill from another pack, verify the target pack is installed via .agents/project.json enabled_packs. If it is not enabled, recommend npx skillpacks install <pack> from the project shell. After install, tell Claude users to run /reload-skills, then /clear or restart if the skill remains invisible. Global skills are always valid. Skills from this same pack are valid because the current skill is already running from that pack.

Platform Strategy — Multi-Product Expansion Planning

Report-First Approval Gate

Default to scope-first approval: before synthesized research, inspect only enough repository, user, and source context to propose research scope, source plan, assumptions, output paths, and approval questions in a review alignment page plus a concise conversation summary.

Do not perform synthesized research, rank candidates, make recommendations, or write working packets or canonical deliverables until final compiled YAML approves the research scope. Minimal pre-approval discovery may identify available files, source categories, and open questions; label it as scope evidence, not findings.

After approved research-scope YAML, perform the research and write only the non-canonical working packet defined in the staged workflow. Then update the review alignment page with findings and stop again for feedback-only YAML or final compiled YAML artifact approval before creating or updating canonical research, spec, or task files.

Do not include Recommended next skill, Recommended next command, or downstream routing language. The approval request itself is the next action. Only emit next-skill routing after the approved artifact has been written or updated.

Staged Research Workflow

Use this staged workflow for synthesized research or report outputs that would create or update canonical research, spec, or task files.

1. Stage 1 - Scope discovery and approval. Inspect only enough repository, user, and source context to propose research scope, source plan, assumptions, output paths, and approval questions. Build the review HTML alignment page before synthesized research. The page must render the proposed scope, available source categories, known context, assumptions/confidence, proposed working-packet and canonical output paths, and research-scope approval gates. Stop for final compiled YAML approval of the research scope. Do not perform synthesized research, rank candidates, make recommendations, or write working packets, canonical research, spec, or task files in Stage 1.
2. Stage 2 - Research and artifact review. Only after approved research-scope YAML with no unresolved needs-clarification, unresolved down feedback, or other unresolved negative feedback, perform the synthesized research, run required source/code checks, and write only a non-canonical working packet: flat mode uses research/_working/preliminary-<skill>-research.md; product-path mode uses research/{slug}/_working/preliminary-<skill>-research.md. Replace <skill> with this skill's name value. Raw evidence or search logs may remain as supporting evidence where this skill already requires them, but synthesized deliverables stay in the working packet. Update the review HTML alignment page with the full preliminary packet, evidence matrix, assumptions/confidence register, source coverage gaps, proposed canonical file changes, and artifact approval gates. Stop for either feedback-only YAML or final compiled YAML. Feedback-only YAML revises the working packet and page, then remains in Stage 2.
3. Stage 3 - Finalize approved artifacts. Consume final compiled YAML for artifact approval only when it has no unresolved needs-clarification, unresolved down feedback, or other unresolved negative feedback. Apply approved edits first, archive the working packet to docs/history/archive/YYYY-MM-DD/HHMMSS/<original-working-path>, remove the active working packet, write the approved canonical artifacts to the unchanged output paths below, and convert the alignment page to confirmed with the approval record preserved.

Canonical output paths remain unchanged. Search logs and other supporting evidence remain allowed only where this skill's output contract already requires them.

Evidence And Feedback Handling

Treat user feedback as input to evaluate, not as automatic ground truth.

• For factual, evidentiary, technical, or source-backed claims: verify against available evidence. If the user appears to misunderstand the evidence or states something factually incorrect, push back clearly and cite the evidence. Do not rewrite findings merely to agree.
• For taste, brand, positioning preference, risk appetite, prioritization, or other subjective judgment calls: weigh user feedback heavily and adapt the recommendation unless it conflicts with verified evidence.
• When feedback mixes facts and preference, separate them explicitly: correct the factual part, then incorporate the preference where it is a legitimate judgment call.
• When uncertain, say what is known, what is inferred, and what would change the conclusion.

Prerequisites

Required (at least one):

• research/icp.md (or research/{slug}/icp.md) — who you serve today, their pain points, value props
• A working product/codebase to analyse for extensibility

If neither exists, tell the user: "Platform expansion requires a foundation. Run /customer-discovery first to define who you serve today, then come back."

Strongly recommended (read if they exist — they enrich the analysis):

• research/competitive-analysis.md — where competitors are/aren't, market gaps
• research/journey-map.md — current user flows, where users hit walls or leave
• research/metrics.md — retention, activation, engagement baselines for the core product

Optional but enriching:

• research/monetization.md — current revenue model, pricing leverage points
• research/positioning.md — current market category, unique attributes
• research/customer-feedback.md — what users are asking for that's adjacent
• research/enterprise-icp.md — if expanding upmarket is a vector
• research/assumption-tracker.md — unvalidated assumptions that affect expansion

Process

0. Product-Path Scope Resolution

Resolve research scope by product path before using code or app structure as a hint:

1. If $ARGUMENTS names a non-archived research/{slug}/ directory or a product-path ID whose scope_path points there, use that path. Treat {slug} as the product/app name, not the ICP, audience, or segment label.
2. If $ARGUMENTS names only research/_archive/{slug}/ or a manifest entry with status: archived or legacy status: abandoned, stop and warn that the path is archived; do not write or update scoped outputs there.
3. Read research/.progress.yaml when present. Normalize legacy active_path to active_paths on read and write back active_paths on manifest updates. Treat legacy abandoned as archived; exclude archived, abandoned, deferred, revisit_candidate, promoted, and any scope_path under research/_archive/ from active target selection.
4. If active product paths exist in the manifest, use those paths. If multiple active paths exist, ask which one to target unless this skill explicitly supports cross-path output.
5. If no active manifest target exists, list non-archived product directories under research/, excluding research/_archive/ and dot directories. Auto-select only when exactly one exists; ask when multiple exist.
6. If no product directories exist, use flat research/ single-product mode.
7. Detect monorepo/app/package structure only as a secondary hint. Suggest creating a missing research/{slug}/ product path when code clearly exposes an app, but do not require code or monorepo detection before using research/{slug}/.

When product path {slug} is active, read and write research under research/{slug}/, specs under specs/{slug}/, and treat top-level research/*.md files as flat-mode documents or cross-path summaries.

1. Assess Core Product Health

Before expanding, determine whether the foundation is solid enough. Read the codebase, existing research, and metrics to evaluate:

• Product-market fit signals — retention, activation, NPS/satisfaction signals from research/metrics.md or research/customer-feedback.md
• Technical extensibility — shared infrastructure (auth, billing, data models) that new products could build on vs. what's tightly coupled
• Team/resource signals — monorepo structure, package boundaries, deployment independence
• Revenue stability — from research/monetization.md if available

Checkpoint 1 — Present core health assessment to the user. Use the AskUserQuestion tool to show:

• A brief health summary: is the core product ready to support expansion, or are there risks?
• Key infrastructure that could be shared vs. what needs decoupling
• Any red flags (e.g., poor retention means the core product needs fixing first)

Ask:

• "Does this assessment match your sense of where the core product is?"
• "Are there resource constraints I should factor in? (team size, runway, technical debt)"

Incorporate feedback before proceeding.

2. Map Expansion Vectors

Use WebSearch with 8-12 diverse queries to research expansion opportunities. Log every search query and key findings to the research log.

Query strategies (adapt to the specific domain):

1. Adjacent market searches — "[category] adjacent markets", "[category] expansion opportunities"
2. Vertical depth searches — "[category] enterprise features", "[category] advanced use cases", "[ICP role] additional tools needed"
3. Horizontal breadth searches — "[category] related categories", "tools used alongside [product category]"
4. Platform precedent searches — "[category] companies that became platforms", "[competitor] product line"
5. User workflow searches — "what do [ICP] use before/after [category]", "[ICP] tool stack"
6. Ecosystem searches — "[category] marketplace", "[category] integrations ecosystem"
7. Acquisition pattern searches — "[category] acquisitions", "[competitor] acquired companies"
8. Market trend searches — "[category] trends 2025 2026", "future of [domain]"
9. Adjacent pain searches — "[ICP] other frustrations", "[ICP] biggest challenges besides [core problem]"
10. Bundling precedent searches — "[category] suite vs point solution", "[category] all-in-one"

Also analyse the codebase and existing research for internal signals:

• Features users request that are adjacent to the core product (from research/customer-feedback.md)
• Competitor product lines (from research/competitive-analysis.md)
• Journey map drop-off points where a new product could re-engage users
• Data or infrastructure that could power additional products

3. Identify Expansion Candidates — Present & Validate

From the research, cluster findings into 4-8 expansion candidates across two axes:

Vertical (deeper into same customer base):

• Advanced tiers, enterprise features, professional services
• Deeper workflow coverage (upstream/downstream of current product)
• Industry-specific variants of the core product
• Data products built on what the core product already collects

Horizontal (adjacent products for new or overlapping customer bases):

• Complementary tools the ICP uses alongside the core product
• Products for adjacent personas in the same organisation
• The same core technology applied to a different problem/audience
• Marketplace or platform plays that enable third-party value creation

For each candidate, note:

• Problem it solves and for whom
• Relationship to core product (shared data, shared users, shared infra, or independent)
• Initial market signal (from web research)
• Whether it's vertical or horizontal

Record the 4-8 candidates in research/.progress.yaml as product_paths[] entries with source_skill: platform-strategy. The top candidate may be status: active or status: revisit_candidate depending on whether the user is ready to validate it now; non-selected candidates should default to status: deferred with validation triggers. Include id, label, source_skill: platform-strategy, pipeline_stage: platform-strategy, scope_path, status, reason, archive_reason, archived_at, promoted_at, evidence_refs, revisit_trigger, next_skill, and last_touched.

Checkpoint 2 — Present candidates to the user. Use the AskUserQuestion tool to show all candidates grouped by vertical/horizontal, with a brief rationale and research evidence for each. Then ask:

• "Are there expansion directions I missed?"
• "Any of these clearly wrong for your situation or that you've already ruled out?"
• "Are there internal signals (user requests, sales conversations) that point toward any of these?"

Incorporate feedback before proceeding.

4. Score Expansion Candidates

For each validated candidate, score across five dimensions:

Synergy Score (how much the core product amplifies this)

• Shared users — do existing users want this?
• Shared data — does the core product generate data this product needs?
• Shared infrastructure — auth, billing, deployment, APIs reusable?
• Cross-sell potential — can you sell this through the same channel?

Market Opportunity (how big and accessible is this)

• Market size signals (from web research)
• Competitive density — is it crowded or open?
• Willingness to pay signals
• Growth trajectory of the adjacent market

Effort & Risk

• Build complexity — new app vs. feature vs. integration
• Time to first revenue signal
• Technical risk — new technology needed or existing stack sufficient?
• Cannibalization risk — could this undermine the core product?

Strategic Value

• Defensibility — does this create a moat (network effects, data, switching costs)?
• Brand coherence — does this make sense under the same brand?
• Sequencing value — does this unlock future expansion vectors?

Validation Cost

• How cheaply can we test demand before building?
• Existing channels to reach potential users
• Minimum viable experiment complexity

Build a scoring matrix with 1-5 ratings per dimension and a weighted total.

5. Design Validation Experiments for Top Candidates

For the top 2-3 scored candidates, design lightweight validation experiments:

• What's the cheapest way to test demand? (landing page, fake-door, survey, pre-sale, concierge)
• Who to test with? (existing users, adjacent audience, or both)
• What signal would confirm or kill this direction?
• How long would the experiment take?
• What's the decision rule? (proceed / pivot / kill thresholds)

Keep experiments to 1-4 weeks and minimal budget. Reference /experiment for full experiment design if the user wants to go deeper.

6. Sequence the Portfolio — Present & Validate

Based on scores, experiments, and strategic value, recommend a portfolio sequence:

• Now (next quarter): The highest-confidence, highest-synergy candidate — usually vertical
• Next (quarter after): The second candidate, ideally building on learnings from the first
• Later (6-12 months): Candidates that require more infrastructure or validation
• Watch (12+ months): Candidates that are interesting but premature

For each phase:

• What shared infrastructure needs to exist first
• Key dependency on the previous phase
• Revenue expectation (directional, not precise)
• Kill criteria — when to abandon this direction

Checkpoint 3 — Present the full portfolio plan to the user. Use the AskUserQuestion tool to show:

1. Scoring matrix with all candidates
2. Top 2-3 validation experiment designs
3. Recommended portfolio sequence with rationale
4. Shared platform considerations

Ask:

• "Does this sequencing match your priorities and constraints?"
• "Any experiments you'd want to run first that differ from what I suggested?"
• "Are there dependencies or constraints I'm not seeing?"

Continue the conversation until all non-trivial details are nailed down. If the user raises points that require additional research, go back and search before finalising.

7. Populate Next Steps

Before writing, check which files exist to populate the ## Next Steps section contextually. Include 3-5 applicable items with "Pick one:" framing:

• ALWAYS first: /experiment [top candidate hypothesis] — Validate the top expansion candidate
• IF no research/assumption-tracker.md: /assumption-tracker — Extract and rank assumptions behind the expansion plan
• IF research/competitive-analysis.md is stale or doesn't cover adjacent markets: /competitive-analysis [adjacent category] — Research competitors in the expansion space
• IF top candidate targets a new audience: /customer-discovery [new audience] — run customer discovery for the new product audience
• IF top candidate is vertical (enterprise): /enterprise-icp — Map enterprise stakeholders for the deeper play
• IF specs exist for core but not expansion: /ux-variations [top candidate] — Explore solution directions for the expansion product
• ALWAYS: /roadmap — Integrate expansion phases into the project roadmap

Impact-aware adjustments:

• IF downstream impact is Major: prepend /reconcile-research — [N] conflicts found in downstream docs as the first item
• IF downstream impact is Minor: annotate relevant skill suggestions with "(stale — [brief description])"
• If downstream impact has not been classified yet, run the downstream impact check against the proposed output before selecting the final recommendation. Do not emit a Minor/Major impact recommendation speculatively.

8. Write Output

Only after the user has validated the findings, write the output files.

9. Downstream Impact Check

After writing, check for downstream research documents that may be affected by what was just decided. Only check documents that exist on disk.

Downstream documents to check (use {slug}/ prefix when product-path scope is active):

• research/roadmap.md
• research/monetization.md
• research/gtm.md
• research/positioning.md
• research/metrics.md

For each existing downstream document:

1. Read it — focus on > Based on: header, ## Summary, and sections that reference concepts this skill just defined or changed
2. Identify specific conflicts: claims, assumptions, or references that contradict what was just decided. Examples:
   • Roadmap phases that don't account for expansion products
   • Monetization strategy built around a single product
   • GTM messaging that doesn't address multi-product positioning
   • Positioning anchored to a single-product category
   • Metrics frameworks that don't cover cross-product health
3. Note each conflict: downstream file, section, the stale claim (quote it), and what it should now say

Classify the impact:

• None: No downstream documents exist, or no conflicts found. Skip display entirely.
• Minor (1-2 small conflicts): Display conflicts to user inline.
• Major (3+ conflicts OR a foundational assumption changed — e.g., positioning shifted from point solution to platform, revenue model needs restructuring): Display conflicts and strongly recommend /reconcile-research.

Display to the user after showing the written file confirmation. This should be quick — one read per downstream doc, scan for conflicts against key decisions. Not a deep reconciliation.

Output

research/platform-strategy.md (or research/{slug}/platform-strategy.md)

# Platform Strategy

> Product: [product name and one-line description]
> Current ICP: [primary ICP from research/icp.md]
> Date: [current date]
> Candidates evaluated: [number]

## Summary
[3-5 sentences: core health assessment, top expansion direction, portfolio thesis, and the single most important next move]

## Core Product Health

### Product-Market Fit Signals
[Retention, activation, satisfaction — is the foundation solid?]

### Technical Extensibility
[What infrastructure is shareable vs. tightly coupled — auth, billing, data, APIs]

### Expansion Readiness
[Overall assessment: ready / ready with caveats / not yet — with reasoning]

## Expansion Vector Map

### Vertical Candidates (Deeper Into Same Customer Base)

For each:
- **[Candidate Name]** — [one-line description]
  - Problem: [what it solves]
  - Audience: [who benefits — same ICP or subset]
  - Synergy: [shared data, users, infra]
  - Market signal: [evidence from research]
  - Effort: [new app / feature / integration — rough complexity]

### Horizontal Candidates (Adjacent Products for New/Overlapping Audiences)

For each:
- **[Candidate Name]** — [one-line description]
  - Problem: [what it solves]
  - Audience: [who benefits — new or adjacent ICP]
  - Synergy: [shared data, users, infra]
  - Market signal: [evidence from research]
  - Effort: [new app / feature / integration — rough complexity]

## Scoring Matrix

| Candidate | Synergy | Market Opportunity | Effort & Risk | Strategic Value | Validation Cost | Weighted Total |
|-----------|---------|-------------------|---------------|-----------------|-----------------|----------------|
| ... | /5 | /5 | /5 | /5 | /5 | /25 |

### Scoring Rationale
[Brief justification for scores that aren't obvious — especially where candidates are close]

## Validation Experiments

### [Top Candidate 1]

**Hypothesis**: [structured hypothesis]
**Method**: [experiment type — landing page / fake-door / survey / pre-sale / concierge]
**Audience**: [who to test with and how to reach them]
**Timeline**: [duration]
**Success criteria**: [specific measurable threshold]
**Decision rule**: If validated → [action]. If invalidated → [pivot].

### [Top Candidate 2]
[Same structure]

### [Top Candidate 3]
[Same structure]

## Portfolio Sequence

### Now (Next Quarter)
- **[Candidate]**: [what to do and why this is first]
- Shared infra needed: [what to build/decouple]
- Validation: [experiment to run]
- Kill criteria: [when to abandon]

### Next (Quarter +1)
- **[Candidate]**: [what to do and why this follows]
- Depends on: [what must be true from previous phase]
- Kill criteria: [when to abandon]

### Later (6-12 Months)
- **[Candidate]**: [why it's deferred — what needs to change for it to move up]

### Watch (12+ Months)
- **[Candidate]**: [why it's interesting but premature]

## Shared Platform Considerations

### Infrastructure to Share
[Auth, billing, data layer, deployment, APIs — what should be centralised]

### What to Keep Independent
[Where new products should have their own stack to avoid coupling]

### Brand Architecture
[Single brand with sub-products, separate brands, or brand + "for [segment]" variants]

### Cross-Product Metrics
[Portfolio-level health metrics: cross-sell rate, multi-product retention, platform LTV vs. single-product LTV]

### Cannibalization Risks
[Where new products might undermine the core — and how to mitigate]

<!-- Include this section only when downstream impact is Minor or Major. Omit entirely for None. -->
## Downstream Impact

> Checked: [list of downstream docs checked]
> Impact: Minor | Major

### Conflicts Found

1. **research/[file].md** — [Section Name]
   - **Stale**: "[exact quote from downstream doc]"
   - **Now**: [what this skill's output says instead]

[For Major only:]
> **Recommended action**: Run `/reconcile-research` to audit and fix all affected downstream documents.

## Next Steps

Pick one:
- [conditional items from step 7 — only include items whose conditions are met]

research/platform-strategy-search-log.md (or research/{slug}/platform-strategy-search-log.md)

Raw research log containing:

• Every WebSearch query executed and why
• Key findings from each search (with source attribution)
• Evidence for and against each expansion candidate
• Competitor product-line analysis
• Market size signals for adjacent markets

research/.progress.yaml

Product-path manifest updated with 4-8 expansion candidates. This does not require every candidate to become a full research track.

• Any data gaps or areas where research was inconclusive

Create the research/ (or research/{slug}/) directory if it doesn't exist.

Task Classification

When this skill produces follow-up work, file it by execution semantics:

• Immediately actionable implementation or documentation work goes in tasks/todo.md.
• Human-only external actions tied to automated steps go in tasks/manual-todo.md with _(blocks: Step N.X)_ or _(after: Step N.X)_; repo edits, SDK wiring, generated assets, local commands, tests, audits, and authenticated CLI/API work stay in tasks/todo.md.
• One-time condition-gated records, baselines, or future measurements go in tasks/record-todo.md with source, condition, non-blocking reason, evidence, and promotion rule.
• Cadence-based reviews, playtests, adoption checks, investor updates, retros, or docs-health checks go in tasks/recurring-todo.md with cadence, owner/agent, next due, evidence path, and escalation conditions.
• Do not put non-blocking records or recurring obligations in tasks/todo.md unless they have been explicitly promoted into current execution work.

Constraints

• Use web search extensively. This skill's value comes from real market data about adjacent opportunities — not assumptions. Every market signal must trace to research evidence.
• Cite sources. When stating market signals, competitor product lines, or trend data, note where the information came from.
• Be honest about uncertainty. If market size or demand can't be verified, say so. Don't fabricate metrics.
• Stay in strategy mode. Do not propose architecture, features, or technical solutions — that's for /ux-variations. Do not design full experiments — that's for /experiment. Provide enough to decide direction, not enough to build.
• Core health is gating. If the core product shows clear PMF problems (poor retention, no activation), say so directly. Expanding from a weak foundation is a common startup mistake — flag it.
• Score honestly. Low-synergy, high-effort candidates should score low even if the market is exciting. The whole point is to find expansion that leverages the existing product.
• Present before writing. Never write output files until findings have been presented to the user and validated through the checkpoint questions. The user must see and approve the analysis before anything is written to disk.
• Do not overwrite existing research/platform-strategy.md (or research/{slug}/platform-strategy.md) without asking the user first.
• Keep validation experiments lightweight. Full experiment design belongs in /experiment. Here, provide just enough to estimate validation cost and timeline.

Context Gathering

Step 1 — Scope questions. Before researching, ask the user 1–3 questions via AskUserQuestion to understand: their product/service, target audience, and what they hope to learn or decide from this research.

Step 2 — Research. Conduct research scoped by the user's answers.

Step 3 — Findings validation. Before building the alignment page, present the 3–5 most important findings and ask the user to validate or correct any critical assumptions.

Alignment Page

When this skill produces durable deliverables (research, specs, plans, reports, prototypes, or any document output), build a full-depth HTML alignment page following ALIGNMENT-PAGE.md in this skill's directory. Output: alignment/platform-strategy-{topic}.html.

Default Shipping Contract

Follow the shared shipping contract convention in CLAUDE.md.