voucher-mechanic-designer

voucher-mechanic-designer is a skill for Claude Code, Codex from thaolst/ai-growth-agents-for-marketers. It costs 55 tokens per session (486 once invoked), scanned A, original, MIT.

A guide for designing discount, cashback, referral, bundle, and other voucher campaigns for fintech and super-app products in Southeast Asia.

In plain words
What is it for?
Use it to plan acquisition, conversion, retention, or reactivation campaigns, including fixed discounts, percentage discounts, cashback, tiered offers, flash promotions, referrals, and bundles.
Why use it?
It helps choose a campaign mechanic based on the goal, customer segment, budget, expected use, and risk of wasting the budget.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to plan acquisition, conversion, retention, or reactivation campaigns, including fixed discounts, percentage discounts, cashback, tiered offers, flash promotions, referrals, and bundles.

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Install with agentmods
npx agentmods add skills/thaolst/ai-growth-agents-for-marketers/voucher-mechanic-designer
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add thaolst/ai-growth-agents-for-marketers --skill voucher-mechanic-designer
Clone the repo
git clone --depth 1 https://github.com/thaolst/ai-growth-agents-for-marketers

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for voucher-mechanic-designer

README.md
[![agentmods](https://agentmods.dev/badge/skills/thaolst/ai-growth-agents-for-marketers/voucher-mechanic-designer/github.svg)](https://agentmods.dev/skills/thaolst/ai-growth-agents-for-marketers/voucher-mechanic-designer)
Your own site
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Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for voucher-mechanic-designer

Your own site · 80×15
<a href="https://agentmods.dev/skills/thaolst/ai-growth-agents-for-marketers/voucher-mechanic-designer"><img src="https://agentmods.dev/badge/skills/thaolst/ai-growth-agents-for-marketers/voucher-mechanic-designer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 486 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.00486
Opus 5 $0.00028 $0.00243
Sonnet 5 $0.00011 $0.00097
Haiku 4.5 $0.00006 $0.00049

Measured 12d ago against content hash 5c14fc6f2488, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

voucher-mechanic-designer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/voucher-mechanic-designer/SKILL.md · 56 lines

What it actually says

Voucher Mechanic Designer

Chuyên gia thiết kế mechanic voucher cho fintech và super app tại Đông Nam Á.

Checks .agents/product-marketing-context.md for product context. Checks .agents/growth-metrics-context.md for past mechanic performance.

Mechanics Covered

Type Best For Risk
Fixed discount High AOV, conversion campaigns Low
Percentage off Low AOV, volume campaigns Medium
Cashback (points) Retention, loyalty programs Low (deferred)
Buy X Get Y Inventory push, bundling Low
Tiered discount Upsell, larger baskets Medium
Time-limited flash Urgency, reactivation High (if poorly timed)
Game mechanic Engagement, viral loops Medium
Referral reward Acquisition, viral Low
Bundle deal Cross-sell, inventory Medium

Key Parameters to Capture

  • Segment profile and behaviors
  • Campaign objective (acquisition / conversion / retention / reactivation)
  • Budget constraint per user and total
  • Expected redemption rate (fintech baseline: 15-40%)
  • Slippage tolerance (budget unused due to conditions)
  • Competitor mechanic benchmarks (SEA market)
Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 56 lines · 55 tokens per session scan A 5c14fc6f2488

Subscribe to this mod's changes

voucher-mechanic-designer is a skill published in the GitHub repository thaolst/ai-growth-agents-for-marketers (5 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 486 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.