From stigg
ADVISORY skill — use when the user is **picking** a monetization model, not implementing one. Triggers include "what pricing should I use", "how should I charge", "how should I monetize this", "what's the right pricing model", "should I do per-seat or usage-based", "should I use credits", "freemium vs trial", "design my pricing", "pricing strategy", "monetization model", "I'm pricing an AI product", "value metric", "how to price [feature/seat/usage]", "hybrid pricing advice", "willingness to pay". Surfaces value-metric questions, maps answers to Stigg's supported models (flat / per-unit / usage-based / credits / hybrid / freemium / trial / custom), then hands off to stigg-pricing-modeling for catalog work. Skip when the model is already chosen and the user just wants it built — that's stigg-pricing-modeling. Skip for runtime gating (stigg-entitlements) or subscription lifecycle (stigg-subscriptions).
How this skill is triggered — by the user, by Claude, or both
Slash command
/stigg:stigg-pricing-expertThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill helps the user choose the right monetization model **before** they start modeling it in Stigg. It's advisory — it asks diagnostic questions, maps answers to Stigg's supported pricing models, and then hands off to `stigg-pricing-modeling` for execution.
This skill helps the user choose the right monetization model before they start modeling it in Stigg. It's advisory — it asks diagnostic questions, maps answers to Stigg's supported pricing models, and then hands off to stigg-pricing-modeling for execution.
What this skill does NOT do: model plans, create features, configure charges, or write code. It surfaces the right shape of the pricing model and points the user at the implementation skill.
Per the umbrella stigg skill: search first. Stigg's supported pricing models evolve (recent additions include credit-based monetization with auto-recharge, custom credit consumption formulas, seat-based credit pools). Confirm what's currently supported before recommending.
Ask if not stated:
The value metric is the thing that, when more of it happens, the customer gets more value. Picking the right value metric is the highest-leverage decision in this whole process. Discovery questions, common metric patterns by domain, and how to handle multiple competing metrics: references/value-metric-discovery.md.
Common value metrics by domain:
| Domain | Likely value metric |
|---|---|
| Collaboration tool | Active users / seats |
| Storage / cloud infra | GB stored, GB transferred, compute-hours |
| AI / LLM apps | Tokens, generations, model calls |
| Marketing / outreach | Emails sent, contacts, conversions |
| Analytics | Events ingested, dashboards, queries |
| API products | API calls, requests, throughput |
| Vertical SaaS | Domain-specific (transactions processed, employees managed, properties listed) |
Ask:
If multiple metrics surface, you can model a hybrid (see below). Don't try to monetize on every dimension — pick one or two primary, the rest as soft caps.
Once the value metric is clear, map onto a Stigg pricing model:
| If… | Use… | Implementation skill |
|---|---|---|
| Predictable monthly fee, one user paying | Flat fee | stigg-pricing-modeling (Plan + flat charge) |
| Pay per resource selected (seats, environments, projects) | Per-unit | stigg-pricing-modeling (Per-unit charge) |
| Usage scales unpredictably; bill what was used | Pay-as-you-go | stigg-pricing-modeling (Usage-based charge) |
| Customer commits to N units up front, true-up after | In-advance commitment | stigg-pricing-modeling |
| Floor on revenue regardless of usage | Minimum spend | stigg-pricing-modeling |
| Usage-based with included quota | Quota + overages | stigg-pricing-modeling (overages section) |
| Variable-cost workloads (multi-model AI, mixed inputs) | Credits with custom formula | stigg-credits (formulas) |
| Prepaid / pay-before-you-use (OpenAI-style) | Credits with auto-recharge | stigg-credits (auto-recharge) |
| Team product where credits should grow with seats | Seat-based credit pools | stigg-credits (seat pools) |
| Multiple revenue streams in one offering (e.g., flat base + usage) | Hybrid — combine flat / per-unit / usage charges on one plan | stigg-pricing-modeling (combining charges) |
| Free tier to drive top-of-funnel | Freemium plan | stigg-pricing-modeling (Free plan) |
| Time-bound preview of paid features | Free trial | stigg-pricing-modeling + stigg-subscriptions (trials) |
| Negotiated, sales-led pricing per customer | Custom plan | stigg-pricing-modeling (Custom plan) |
| Multi-region pricing | Layer price localization on top of any model | stigg-pricing-modeling/references/price-localization.md |
The decision tree with edge cases lives in references/pricing-model-decision-tree.md.
Sketch revenue at median and top-decile usage; confirm the bill is predictable enough for the buyer's segment (self-serve customers can't reason about graduated overages on top of credits); confirm an expansion path exists (Free → Pro → Enterprise should feel inevitable); flag Finance pitfalls (non-zero cost basis on promotional grants, hand-rolled discounts outside Stigg coupons, per-customer pricing without custom plans). The "Done" checklist below names the seven things the user should know before you route to implementation.
Once a model is chosen, route the user explicitly:
stigg-pricing-modeling.stigg-credits.stigg-subscriptions.stigg-widgets.stigg-recipes.Do not start authoring catalog config in this skill — that's the implementation skill's job. The full hand-off checklist (what counts as a "done" recommendation, which target skill to route to per concern, how to phrase the hand-off message): references/handoff-to-modeling.md.
Before handing off, the user should know:
If any of these are still ambiguous, ask one more question before handing off.
stigg-pricing-modeling (or stigg-credits for credits).stigg-subscriptions.stigg-entitlements.| Mistake | Fix |
|---|---|
| Recommending a model without surfacing the value metric | Every tier becomes arbitrary. Surface it first. |
| Mapping competitor pricing 1:1 | Anchor on the customer's value metric, not benchmarks. |
| Authoring catalog config in this skill | Hand off — modeling lives in stigg-pricing-modeling. |
| Recommending credits because "the user mentioned AI" | Credits fit when costs vary by workload. Uniform-cost AI works fine on raw usage charges. |
| Recommending hybrid by default | Hybrid adds complexity. Default to one primary model unless two metrics genuinely matter. |
| Skipping the median + top-decile sanity check | Pricing that breaks at scale gets discovered by your highest-value customers. Sketch the math. |
| Treating Stigg as the source of pricing strategy | Stigg implements pricing models; it doesn't pick the right one. That's this skill's job. |
npx claudepluginhub stiggio/skills --plugin stiggProvides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.
Searches, retrieves, and installs Agent Skills from prompts.chat registry using MCP tools like search_skills and get_skill. Activates for finding skills, browsing catalogs, or extending Claude.