Monetization

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 Codex users to start a fresh Codex CLI session if the $ skill list remains stale. Only the currently running skill and skills verified available in the active session or project-local install state are directly recommendable. For unavailable pack skills, recommend npx skillpacks install <pack-or-skill>; for unavailable base skills, recommend npx skillpacks init before the skill.

Monetization — Revenue & Pricing Strategy

Invoke as $monetization.

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 so it renders the complete working-packet substance as structured HTML review UI: purpose-built sections, tables, matrices, gates, cards, and tier-appropriate charts or diagrams that preserve every packet section, finding, caveat, and decision detail without summary loss. Raw Markdown packet text may appear only as a supplemental source view after the rendered review UI; do not make a Full Preliminary Packet or Full Working Packet raw Markdown dump, giant <pre><code> block, link-only view, or source-only view the primary review surface. Include the 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

• Hard: research/icp.md (or research/{slug}/icp.md) must exist. If not, tell the user to run $customer-discovery first and stop.
• Soft: Read these if they exist — each adds specificity:
  • research/competitive-analysis.md (or research/{slug}/competitive-analysis.md) — competitor pricing, tiers, freemium models
  • research/journey-map.md (or research/{slug}/journey-map.md) — where value is delivered, conversion triggers
  • research/metrics.md (or research/{slug}/metrics.md) — activation, engagement, retention signals
  • research/gtm.md (or research/{slug}/gtm.md) — existing pricing strategy and channel economics
  • research/customer-feedback.md (or research/{slug}/customer-feedback.md) — willingness-to-pay signals, pricing complaints
  • specs/*.md (or specs/{slug}/*.md) — what's being built, feature scope

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. Product Path Manifest

Read research/.progress.yaml when present. Normalize active_path (singular legacy) to active_paths (plural list) when reading; treat legacy abandoned as archived and exclude archived/deferred/revisit/promoted paths plus research/_archive/ scopes from active target selection. Scope monetization strategy to the active product path by default. When pricing or packaging analysis reveals a fundamentally different revenue model for a deferred product path, add a ## Product Path Implications section.

2. Load Context

Read all prerequisite files. From each, extract monetization-relevant signals:

• ICP: buyer budget, purchasing process, current spend on alternatives, company size segments, price sensitivity
• Competitive analysis: competitor pricing models, tier structures, free vs paid boundaries, published pricing pages
• Journey map: where the "aha moment" happens, what triggers conversion, what drives expansion
• Metrics: activation rate, engagement depth, retention curves — these constrain what models are viable
• GTM: if pricing strategy already exists in gtm.md, treat it as a starting point to deepen, not duplicate
• Customer feedback: direct willingness-to-pay signals, complaints about competitor pricing, feature requests tied to upgrade triggers

Read CLAUDE.md, README, and key source files for product context.

3. Market Research — Revenue Models in Category

Use WebSearch with 6–10 targeted queries. Log every query and finding to the research log.

Query strategies (adapt to domain):

1. Category pricing — "[category] pricing models", "[category] SaaS pricing"
2. Competitor pricing pages — "[competitor] pricing", "[competitor] plans" (use WebFetch on pricing pages for detail)
3. Revenue model analysis — "[category] revenue model", "[category] business model"
4. Pricing benchmarks — "[category] average deal size", "[category] ARPU", "[category] pricing benchmark"
5. Freemium analysis — "[category] freemium conversion rate", "freemium vs free trial [category]"
6. Usage-based research — "usage-based pricing [category]", "consumption pricing [category]"
7. Pricing psychology — "[category] pricing page best practices", "SaaS pricing strategy [market segment]"
8. Unit economics — "[category] CAC", "[category] LTV", "[category] payback period"
9. Expansion revenue — "[category] expansion revenue", "[category] upsell triggers"
10. Pricing failures — "[competitor] pricing backlash", "[category] pricing mistakes"

4. Identify Revenue Model Options — Present & Validate

From research evidence and product context, identify 2–4 viable revenue model options. For each:

• Model type: subscription (flat/tiered), usage-based, hybrid, marketplace, freemium, open-core, one-time, etc.
• Evidence: which competitors or adjacent products use this model, and how it performs
• Fit with ICP: does the buying process support this? (e.g., seat-based works poorly if the buyer doesn't know team size upfront)
• Fit with product: does the value delivery pattern match? (e.g., usage-based works when value scales with consumption)
• Risks: what could go wrong? (e.g., usage-based creates unpredictable costs that enterprise procurement hates)

Checkpoint 1 — Present models with a recommendation. If the session is already in Plan mode, prefer request_user_input; otherwise ask in plain text. Show all options with evidence and fit analysis. State which model you recommend and why (grounded in ICP fit, product fit, and market evidence). Then ask:

• "I recommend [model] based on [key evidence]. Which constraints, missing facts, or weak assumptions should change this recommendation?"
• "Any non-negotiable pricing constraints or product realities I need to incorporate? (e.g., must have a free tier, can't do per-seat)"

Incorporate feedback before proceeding.

5. Deep-Dive: Pricing Architecture

For the selected model (or top 2 if the user is undecided), research and design:

A. Value Metric

• What unit does the customer pay for? (seats, usage, features, outcomes)
• Does the value metric align with how the customer perceives value?
• Does it grow naturally as the customer gets more value? (expansion-friendly)

B. Tier Design

• How many tiers? (typically 2–4 for B2B SaaS)
• What's the free tier / trial structure? What's the upgrade trigger?
• What features gate each tier? (map against journey stages — free tier should reach "aha moment")
• What's the "good-better-best" progression?

C. Price Points

• Anchor against competitor pricing and ICP budget signals
• Entry price: low enough to reduce friction, high enough to signal value
• Price-to-value ratio vs. alternatives
• Annual vs. monthly discount structure

D. Packaging

• What's bundled vs. add-on?
• Are there usage limits, and where do they kick in?
• Enterprise tier: what justifies the custom pricing? (SSO, SLA, dedicated support, compliance)

Checkpoint 2 — Present pricing architecture to the user. If the session is already in Plan mode, prefer request_user_input; otherwise ask in plain text. Show tier design, price points, and packaging — cite competitor pricing benchmarks that anchor each price point, ICP willingness-to-pay signals that validate the range, and journey-stage alignment that justifies feature gates. Then ask:

• "Which price points, gates, or packaging assumptions need stronger evidence or should change based on hard constraints?"
• "Any features that absolutely must be free? Any that must be gated?"

Incorporate feedback before proceeding.

6. Unit Economics & Viability

Estimate (with stated assumptions and confidence levels):

• CAC — cost to acquire a customer, based on GTM channels
• LTV — lifetime value, based on pricing × estimated retention
• LTV:CAC ratio — is it viable? (target: 3:1+ for SaaS)
• Payback period — months to recoup acquisition cost
• Expansion revenue potential — how much can revenue grow per account over time?
• Gross margin — revenue minus cost of delivery (infrastructure, support)

If data is insufficient for estimates, state what data is needed and recommend how to gather it (e.g., "run a pricing survey", "track activation-to-conversion for 30 days").

Checkpoint 3 — Present unit economics to the user. If the session is already in Plan mode, prefer request_user_input; otherwise ask in plain text. Show the estimates with assumptions, then ask:

• "Which internal numbers, targets, or assumptions should I adjust with better evidence?"
• "What's your target margin or payback period?"

7. Monetization Timing & Sequencing

Based on product stage and ICP:

• When to introduce paid: before launch (validate willingness-to-pay), at launch, or post-traction?
• What stays free permanently vs. what's a trial/teaser
• Pricing evolution: how should pricing change as the product matures? (e.g., start low and raise, start high and introduce a free tier)
• Revenue diversification: are there secondary revenue streams? (marketplace, data, services, partnerships)

8. Populate Next Steps

Check which files exist to populate the ## Next Steps section contextually. Include a Recommended item (the single highest-impact next step given current project state) with a one-line reason, followed by Other options (2–4 alternatives). Use this format in the output:

Next Steps

Recommended: [recommended skill] — [one-line reason why this is the highest-impact next action given current state]

Other options:

• $skill — [description]
• ...

Recommendation priority (first applicable becomes the recommendation):

1. IF no research/gtm.md: recommend $gtm — pricing needs a go-to-market plan to reach the customers who'll pay
2. IF research/gtm.md exists but predates this analysis: recommend $gtm — GTM pricing references are now stale and need updating
3. IF no research/metrics.md: recommend $metrics — define metrics to track whether the monetization strategy is working
4. IF specs/ exist and no tasks/roadmap.md: check .agents/project.json.enabled_packs for agent-work-admin — if agent-work-admin is not enabled, recommend npx skillpacks install agent-work-admin first; if agent-work-admin is enabled, recommend $roadmap — plan the build with monetization milestones

Other options (include all applicable items not chosen as recommended):

• IF no research/gtm.md: $gtm — Build go-to-market plan with pricing from this strategy
• IF research/gtm.md exists but predates this analysis: $gtm — Update GTM with refined pricing strategy
• IF no research/metrics.md: $metrics — Define metrics to track monetization health (conversion, expansion, churn)
• IF no research/journey-map.md: check .agents/project.json.enabled_packs for customer-lifecycle — if customer-lifecycle is not enabled, recommend npx skillpacks install customer-lifecycle first; if customer-lifecycle is enabled, recommend $journey-map — Map the journey to validate where pricing gates belong
• IF specs/ exist and no tasks/roadmap.md: check .agents/project.json.enabled_packs for agent-work-admin — if agent-work-admin is not enabled, recommend npx skillpacks install agent-work-admin first; if agent-work-admin is enabled, recommend $roadmap — Plan the build with monetization milestones
• IF codebase exists: check .agents/project.json.enabled_packs for business-ops — if business-ops is not enabled, recommend npx skillpacks install business-ops first; if business-ops is enabled, recommend $mvp-gap — Check if the product delivers enough value to charge
• IF product is live and revenue exists: check .agents/project.json.enabled_packs for business-ops — if business-ops is not enabled, recommend npx skillpacks install business-ops first; if business-ops is enabled, recommend $runway-model — Track actual financial performance against these estimates

9. Final Review & Write

Present the complete monetization strategy to the user — revenue model, pricing architecture, unit economics, timing. Ask:

• "Ready to write this to research/monetization.md? Any constraints, missing facts, or weak assumptions to adjust first?"

Only after the user confirms, write the output files.

Output

research/monetization.md (or research/{slug}/monetization.md)

# Monetization Strategy

> Based on: research/icp.md (or research/{slug}/icp.md)[, research/competitive-analysis.md, research/journey-map.md, research/metrics.md, research/gtm.md, research/customer-feedback.md]
> Date: [current date]

## Summary
[2-3 sentences: the core monetization thesis — revenue model, target price range, and why this approach fits the ICP]

## Revenue Model
**Selected model**: [model type]
**Rationale**: [why this model fits the ICP, product, and market]
**Evidence**: [competitor precedent, market research]

### Models Considered & Rejected
| Model | Why Considered | Why Rejected |
|-------|---------------|-------------|
| ... | ... | ... |

## Value Metric
**Customers pay for**: [the unit — seats, usage, features, outcomes]
**Why this metric**: [alignment with perceived value, expansion-friendliness]

## Pricing Tiers

| Tier | Price | Target Segment | Key Features | Upgrade Trigger |
|------|-------|----------------|-------------|----------------|
| Free / Trial | ... | ... | ... | ... |
| Starter | ... | ... | ... | ... |
| Pro | ... | ... | ... | ... |
| Enterprise | Custom | ... | ... | ... |

### Free Tier / Trial Design
[What's included, what's limited, what triggers the upgrade — must reach "aha moment"]

### Feature Gating
[Which features are in which tier, and why — grounded in journey stages]

### Enterprise Justification
[What justifies custom pricing — SSO, SLA, compliance, dedicated support, volume]

## Price Points

### Anchoring & Rationale
[How prices were set — competitor anchoring, ICP budget signals, value-based reasoning]

### Annual vs. Monthly
[Discount structure, rationale]

### Packaging & Add-ons
[What's bundled, what's separate, usage limits and overages]

## Unit Economics (Estimated)

| Metric | Estimate | Assumption | Confidence |
|--------|----------|------------|------------|
| CAC | ... | ... | Low/Medium/High |
| LTV | ... | ... | ... |
| LTV:CAC | ... | ... | ... |
| Payback Period | ... | ... | ... |
| Gross Margin | ... | ... | ... |
| Expansion Revenue | ... | ... | ... |

### Data Gaps
[What data is needed to improve these estimates — and how to gather it]

## Monetization Timing

### When to Charge
[Before launch / at launch / post-traction — with rationale]

### What Stays Free
[Features or usage levels that remain free permanently, and why]

### Pricing Evolution
[How pricing should change as product matures — roadmap of pricing milestones]

### Revenue Diversification
[Secondary revenue streams if applicable — marketplace, services, data, partnerships]

## Open Questions
[Pricing experiments to run, decisions that need real-world data, A/B tests to consider]

## Next Steps

**Recommended:** `$skill` — [one-line reason]

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

research/monetization-interview.md (or research/{slug}/monetization-interview.md)

Raw interview log — questions, options presented, user responses, checkpoint validations, and a closing summary of key decisions and deviations.

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

• Requires ICP. Cannot build a monetization strategy without knowing who pays and why.
• Evidence-based. Every pricing decision must trace back to research evidence (competitor data, market benchmarks, ICP signals). Do not invent price points from intuition.
• Present before writing. Never write output files until findings have been presented and validated through all three checkpoints.
• Don't duplicate GTM. If research/gtm.md already has a pricing section, deepen it rather than contradict it. Note any conflicts and ask the user to resolve. If the session is already in Plan mode and there are 2-3 concrete choices, prefer request_user_input.
• Don't prescribe product changes. If the product doesn't deliver enough value to support the pricing, note it as a gap — that's $mvp-gap's job.
• Do not overwrite existing research/monetization.md (or research/{slug}/monetization.md) without asking the user first.
• Minimum research depth: at least 6 WebSearch queries before presenting revenue model options, then targeted queries per model option.
• State assumptions. Every unit economics estimate must include the assumption behind it and a confidence level. Never present estimates as facts.

Alignment Page

Follow the shared alignment-page convention via the packaged convention resolver; output path is alignment/monetization-{topic}.html.

Default Shipping Contract

Follow the shared shipping contract convention in CLAUDE.md.