Monetization

Pack Availability Guard

Before telling the user to run a skill from another project-local pack, check .agents/project.json.enabled_packs. If the target pack is not enabled, recommend $pack install <pack> instead of the target skill. Global skills are always valid. Skills from this same pack are valid because the current skill is already running from that pack.

Monetization — Revenue & Pricing Strategy

Invoke as $monetization.

Report-First Approval Gate

Default to report-only: present findings, evidence coverage, assumptions, recommended artifact path, and proposed file changes in a pre-approval alignment page plus a concise conversation summary for user approval before creating or updating canonical research, spec, or task files.

Do not write or overwrite synthesized deliverables until the user explicitly approves, unless the user invoked an explicit write/update/fix mode or clearly asked to write files upfront. Raw evidence capture may be persisted before analysis when reproducibility requires it; report those raw paths separately and still gate synthesized research/report writes.

When stopping for approval, build and attempt to open the alignment preview page first, then ask the user to review it and approve, question, or request adjustments. 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.

Deep-research skill that analyzes how to monetize the product. Combines web research on revenue models in the category with codebase analysis, ICP data, and competitive pricing to produce a monetization strategy that stands on research before asking the user for hard constraints or proprietary data.

Prerequisites

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

Process

0. App Scope Resolution (Monorepo Support)

Before checking prerequisites, determine the app scope:

1. If $ARGUMENTS specifies an app name matching a subdirectory of research/, use it.
2. If research/ contains subdirectories (excluding files), list them and ask the user which app to target. If the session is already in Plan mode and there are 2-3 concrete choices, prefer request_user_input; otherwise ask in plain text. If only one subdirectory exists, use it automatically.
3. If no subdirectories exist, proceed with flat structure (single-product mode).

When app scope {app} is active:

• Read/write research from research/{app}/ instead of research/
• Read/write specs from specs/{app}/ instead of specs/
• Also read research/icp.md (cross-app overview) for broader context

1. 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.

2. 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"

3. 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.

4. 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.

5. 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?"

6. 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)

7. 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: 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: $journey-map — Map the journey to validate where pricing gates belong
• IF specs/ exist and no tasks/roadmap.md: $roadmap — Plan the build with monetization milestones
• IF codebase exists: $mvp-gap — Check if the product delivers enough value to charge
• IF product is live and revenue exists: $runway-model — Track actual financial performance against these estimates

8. 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/{app}/monetization.md)

# Monetization Strategy

> Based on: research/icp.md (or research/{app}/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/{app}/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/{app}/) 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/{app}/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

When this skill produces durable deliverables (research, specs, plans, reports, prototypes, or any document output), build a full-depth HTML alignment page at alignment/monetization-{topic}.html. Use a normalized topic slug derived from the app, feature, research subject, report subject, or output filename.

Full content requirement. The alignment page must contain the complete content of every proposed markdown deliverable -- every section, every finding, every detail, every list item. It is a thorough interactive review document, not a summary. Render the full deliverable content in clean, readable HTML with appropriate hierarchy, styling, and navigation. If the skill writes multiple scoped deliverables in one run, build one alignment page that contains all deliverables with anchor-linked navigation. Durable tracker artifacts, such as research/assumption-tracker.md, remain canonical markdown outputs but must also be fully rendered into the alignment page before approval.

Dark-mode styling. Use a dark color scheme by default. Base CSS variables: --bg: #0d1117; --surface: #161b22; --border: #30363d; --text: #c9d1d9; --text-muted: #8b949e; --accent: #58a6ff; --green: #3fb950; --red: #f85149; --orange: #d29922; --purple: #bc8cff;. Apply background: var(--bg); color: var(--text); on body. Use --surface for cards, nav, and table headers. Use --border for all borders. Use --purple for question blocks and gate headings. Use --accent for links and section headings. Keep headings color: #fff or var(--accent) for hierarchy. Question block backgrounds should use #1c2333.

Alignment gates. Treat gates as explicit review sections inside the HTML page. A gate blocks finalization until its required inline questions are answered and compiled into YAML. Include every gate that applies to the skill output, and include these gate types whenever relevant: evidence coverage, assumptions/confidence, scope/non-goals, candidate/verdict decisions, artifact destination, proposed file changes, coverage checkpoint, and post-approval route.

Report-only research gates. For report-only or pre-approval research skills, the alignment page must explicitly contain evidence coverage, assumptions/confidence, recommended path, proposed file changes, and approval gates before any canonical research, spec, or task file is created or updated.

Required inline questions. Each gate must contain at least one required inline question placed directly under the content it governs, inside a visually distinct question block. Each question must use radio-button inputs and include two standing options after the skill-generated choices: "Other / None of the above" backed by a multi-line text box for free-form input, and "Need clarification" backed by an optional notes box where the user can explain what is unclear. When any radio option other than "Other" or "Need clarification" is selected, show an optional "Additional notes" text box beneath it so the user can qualify their choice. Generate questions based on what genuinely needs user input -- do not add filler questions. Do not create a separate bottom "Decisions & Clarifications" section.

Gate YAML contract. At the bottom of the page, include a "Compile Answers" button that aggregates answers from all inline gate questions throughout the page, including free-text notes. The button remains disabled until every required question has a selection, shows a count of remaining unanswered questions, and scrolls to the first unanswered question if clicked early. When every question is answered, generate a structured YAML block with one item per gate answer using this stable shape: section, gate_type, status (answered, other, or needs-clarification), answer, optional notes, and optional target_artifact or target_path when the gate controls file output. After successful compilation, automatically attempt to copy the YAML to the clipboard with the Clipboard API, display copy status, and display the YAML in a read-only textarea with an explicit "Copy YAML" button. The copy button must retry clipboard copy when supported and fall back to selecting the textarea contents when clipboard access is unavailable or blocked.

Pre-approval stop. Before user approval, the next action is review of the HTML alignment page, not downstream routing. Ask the user to review the page and provide the compiled YAML answers. Do not include Recommended next skill, Recommended next command, or downstream routing language until after compiled YAML has been provided and the approved artifacts have been written or updated.

Diff highlighting on updates. When the agent updates an existing alignment page after receiving compiled answers, highlight what changed since the previous version. The agent chooses inline annotation or side-by-side layout per situation.

Archiving. Before replacing an existing alignment page, archive it to docs/history/archive/YYYY-MM-DD/HHMMSS/alignment/monetization-{topic}.html.

Browser open. Attempt to open the resulting HTML page in the browser and report whether the open succeeded or was blocked. A blocked browser-open attempt does not make the skill fail when the files were written correctly.

Archive-First Replacement Policy

• Before replacing or substantively rewriting an existing canonical research/spec document (research/**/*.md, specs/**/*.md, or docs/specifications/**/*.md), copy the current file to docs/history/archive/YYYY-MM-DD/HHMMSS/<original-relative-path>.
• Preserve the archived snapshot exactly as it existed before the change; do not edit the archived copy after creating it.
• After the archive snapshot exists, write the updated document to the original canonical path.
• Report both the archive path and the updated canonical path in the final output.
• New files do not need archive snapshots. Append-only updates do not need archive snapshots unless an existing section is regenerated or rewritten.
• Keep any existing user approval requirement before overwriting or replacing a document; archiving does not replace asking when the skill already requires approval.

Default Shipping Contract

• Default next-step routing: when reporting completion, include either Recommended next skill: <command> or the two-line pair **Next work:** <specific task or "none"> and **Recommended next command:** <one command or route> so the next operator has a concrete handoff.
• If this skill creates or modifies tracked repository files, finish by committing and pushing all intended changes to the repository primary branch (main when present, otherwise master) before stopping, even if the user did not explicitly ask for commit/push.
• Do not leave tracked changes or unpushed commits behind. If unrelated tracked work is already present, either include it in sensible commits too or stop and explain the blocker.
• This contract does not override stricter safety rules about secrets, destructive history changes, release publication/tag confirmation, or production deploy confirmation.