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AC pricing-strategy

Design, optimize, and communicate SaaS pricing — tier structure, value metrics, pricing pages, and price increase strategy. Use when building a pricing model from scratch, redesigning existing pricing, planning a price increase, or improving a pricing page. Trigger keywords: pricing tiers, pricing page, price increase, packaging, value metric, per seat pricing, usage-based pricing, freemium, good-better-best, pricing strategy, monetization, pricing page conversion, Van Westendorp. NOT for broader product strategy — use product-strategist for that. NOT for customer success or renewals — use customer-success-manager for expansion revenue.

alirezarezvani/claude-skills Agent Skills author: alirezarezvani MIT 4 files body ≈ 3 318 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Design, optimize, and communicate SaaS pricing — tier structure, value metrics, pricing pages, and price increase strategy.

As a process C 57/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

ProcedureCommerceData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
100/100
safety, quality, tests
Safety 60%
100
Quality 40%
99
Run on models
none yet
Process rating
C
57/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 57/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 6 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 86 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3318 tokens
    • 100Progress reporting. Reports progress
    • low 13 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 644: enough signal without eating the budget
    • +4Structure: 32 headings
    • +3Step-by-step instructions: 86 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 99.