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AC usage-based-pricing

Design pricing models that developers understand, accept, and can predict. Trigger phrases: usage-based pricing, API pricing, metered billing, developer pricing, pricing page, cost calculator, pay as you go, pricing transparency, competitive pricing, developer billing

The skillemall take

This skill teaches AI to design pricing models that developers actually understand. Three files, no broken references, clean lint. Content quality scores 89, safety is maxed out.

The catch: never tested on models, sandbox remains empty. Process score of 56 signals methodological gaps. Trigger phrases are listed (usage-based pricing, API pricing, cost calculator), but actual AI behavior on these triggers is unverified. Platform support spans Claude to Deepseek, but that's a promise, not proof.

Worth installing if you need a template for pricing-related prompts. Test it on your models first.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 3 files body ≈ 1 145 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Design pricing models that developers understand, accept, and can predict.

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

IntegrationData 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
56/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: agentic-awesome-skills

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: 3. 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 56/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 49 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1145 tokens

    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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 268: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 49 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.