What does this skill do, and when should you use it?
This is the Launch → Assemble pricing skill in Aaron Marketing Skills, focused on launch pricing, package tiers, early-bird offers, beta pricing, graduation paths, and guarantee terms. It uses product, ICP, message-house, cost, and public pricing inputs to produce a reusable pricing and packaging plan. Price, offer, and guarantee language is routed as candidate claims for registry review rather than presented as a live offer.
Reads the product and ICP, launch objective, current pricing or history, known unit economics, accepted message-house pillars, public competitor pricing, and launch stage/date. It designs named tiers with contents, maps value pillars to price, creates a launch-offer ladder with a real deadline rationale, defines beta or early-adopter pricing and its path to the GA price, and drafts guarantee/refund terms. It submits price and offer candidates to memory/events/claims.ndjson through an authorized operation: propose request and stage-linked pricing facts to memory/events/launches.ndjson; it does not directly write canonical claims or launch-registry records.
- A SaaS or developer-tool team needs a new two-tier or three-tier launch package with names and contents.
- A product team has existing pricing and needs an early-bird offer, a genuine end condition, and a post-launch price path.
- A launch team needs to connect package contents to message-house value pillars while drafting refund or guarantee terms.
- A marketing lead wants pricing claims prepared for formal review by the offer-claims-registry before launch copy is published.
How do you install this skill?
- Do not treat drafted prices, offers, guarantees, or stage facts as live terms; complete the registry review and authorized proposal flow first.
- Confirm costs, price history, unit economics, deadline rationale, refund conditions, and applicable market rules; mark missing facts as needs source rather than inventing them.
- Optional crawling or analytics connectors may be unreachable or unstable from mainland China; use pasted pages or local exports when needed.
- This assessment is static only; the skill, scripts, and registry writes were not executed.
- Shell / CLI
- Network access
- Local filesystem
Python standard library runtime
Install the collection with npx skills add aaron-he-zhu/aaron-marketing-skills. In Claude Code, use /plugin marketplace add aaron-he-zhu/aaron-marketing-skills, then /plugin install aaron-marketing@aaron. The source does not document a separate single-skill installation command; the skill file is launch/assemble/pricing-packaging-planner/SKILL.md.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/launch/assemble/pricing-packaging-planner" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Start with a prompt such as: Plan launch pricing for [product]. ICP: [who]. Current pricing: [tiers / none — new product]. Launch goal: [B2B / dev-tool / mobile]. Or: Set up a launch discount / early-bird offer for [launch date] — with a real deadline reason and the post-launch price path. Provide current pricing, launch type, commercial objective, and known cost or value information when available.
What are this skill's strengths and limitations?
- Covers the concrete pricing decisions needed for a launch: tiers, value mapping, offers, beta graduation, and guarantees.
- Labels numbers as User-provided, Measured, or Estimated and rejects fake scarcity.
- Routes draft claims into the shared registry workflow instead of treating them as approved live terms.
- Works with Claude Code and compatible Agent Skills hosts, with a Tier 1 path based on supplied data.
- It does not own or directly write the canonical offer/claim record; offer-claims-registry must formalize the terms.
- It is not a newsletter-monetization, ad-bidding, or RAMP-auditing skill.
- Tier ratios, expected take rates, and projected offer lift are estimates, not universal benchmarks.
- The provided source does not document independent tests or platform-specific validation for this skill.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Launch Pricing & Packaging Planner this page | 61 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
| Early Access Designer | 57 · Use with care | ★ 3k | 12d ago | Apache-2.0 |
| RAMP Launch Readiness Auditor | 61 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
| Launch Retro Analyzer | 59 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
| Launch Message House Builder | 59 · Recommended | ★ 3k | 12d ago | Apache-2.0 |
Unlike offer-claims-registry, this skill designs candidate pricing and offer terms rather than maintaining the live offer and claim record. Unlike newsletter-monetization-planner and bid-strategy-planner, it does not model paid-newsletter economics or advertising auction bids.
How did FollowSkills review this skill?
The skill treats prices, offer terms, and guarantees as candidates, routes them through an authorized operation: propose path, and avoids direct writes to canonical records. It marks pasted material as untrusted, requires a true deadline rationale, prohibits fake scarcity, and asks for confirmation before saving results. Six points are deducted because pricing, refunds, and launch-state proposals affect commercial and external state, while per-item confirmation, rollback, and sensitive commercial-data handling are not fully specified in this skill.
The instructions, declared inputs and outputs, done criteria, scope guard, and handoff path are largely consistent, with explicit separation from SSOT writes. One point is deducted because key scripts, shared contracts, and registry protocols are not included here, and failure feedback for invalid inputs, proposal rejection, or unavailable dependencies is thin; static calibration caps this at 10.
Triggers, argument hints, inputs, outputs, and several explicit non-fit cases are clear, with both English and Chinese descriptions. Tier-1 operation using user-provided data supports broad host fit. Four points are deducted because boundaries across product and regional pricing scenarios lack concrete examples, mainland-China reachability is not established, and optional crawling or analytics connectors may be network-constrained.
The file provides frontmatter, version, license, Quick Start, Skill Contract, Data Sources, Instructions, Save Results, references, and next-skill guidance, with good progressive disclosure. Five points are deducted because maintenance ownership, update path, changelog, and dependency-version policy are not stated in the skill, and several operational assumptions depend on unprovided shared files.
The promised artifact is concrete: tier structure, value-to-price mapping, offer ladder, early-adopter graduation path, guarantee/refund terms, and registry candidates, with a clear scope boundary. Eight points are deducted because no representative output or static verification of direct usability is supplied, and successful use depends on user data, shared protocols, and downstream registry handling; static calibration caps this at 7.
The skill labels the swyxio/launch-cheatsheet heuristics as Estimated and distinguishes user-provided, Measured, and Estimated data, giving some traceability; repository materials also show a license, CI, and tests. Five points are deducted because there is no independent execution evidence or third-party corroboration for this skill's key paths, while the supplied tests mainly cover architecture and audit validation rather than this skill's outputs; static calibration caps this at 5.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →