contributor-reward

Name: contributor-reward
Availability: InStock
Author: @aaronjmars

Generate tier-priced USDC reward plans from weekly contributor leaderboard rankings.

626|225|Updated Mar 4, 2026

Search Tags:#open source #rewards #idempotency #crypto #leaderboard #token distribution #YAML planning

Ready to run

npx skills add https://github.com/aaronjmars/aeon --skill contributor-reward-aaronjmars

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: contributor-reward
Source: https://github.com/aaronjmars/aeon/tree/main/skills/contributor-reward
Command: npx skills add https://github.com/aaronjmars/aeon --skill contributor-reward-aaronjmars

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns weekly contributor rankings into a tiered rewards plan you can execute, closing the gap between leaderboard recognition and token distribution.

Core Features & Use Cases

Converts rankings into a priced rewards plan: Reads the latest fork-contributor-leaderboard article, applies eligibility rules (rank and score thresholds), and maps leaderboard positions to a fixed USDC tier table.
Adds safe, auditable wiring (no transfers here): Writes the plan to memory/distributions.yml for downstream distribute-tokens execution, without doing money movement inside the Skill.
Prevents repeat payouts with idempotent state: Tracks per-(week, login) processing plus a once-ever first-PR bonus so reruns don’t re-award.
Supports operator-friendly runs: Offers dry-run planning, week override via ISO week input, stale/no-leaderboard detection, and parsing drift detection with notifications.

Quick Start

Run contributor-reward in dry-run mode for the most recent leaderboard to preview what will be written to memory/distributions.yml.

Frequently Asked Questions about contributor-reward

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert open-source contributor leaderboard rankings into USDC token distribution plans?▼

To convert leaderboard rankings into USDC token distribution plans, you need a system that reads leaderboard rankings, applies rank and score threshold eligibility rules, and maps positions to a fixed USDC tier table. This process generates a structured rewards plan for downstream execution.

How does idempotency work when managing weekly crypto token rewards for contributors?▼

Idempotency in weekly crypto token rewards works by tracking per-week and per-login processing state in a local JSON file, ensuring reruns do not re-award the same contributor. It also enforces a once-ever first-PR bonus to prevent duplicate payouts across multiple runs.

Can I preview USDC reward plans for contributors before executing token distribution?▼

Yes, you can preview USDC reward plans by running the planning process in dry-run mode. This allows you to review what will be written to the distributions YAML file without executing actual money movement, ensuring the tiered plan is correct before downstream token distribution.

What is the best way to automate recurring token distribution handoffs for open source contribution programs?▼

The best way to automate recurring token distribution handoffs is to use a deterministic pipeline that reads leaderboard state files and writes a distributions YAML file for a separate token distribution executor. This separation ensures safe, auditable planning without direct money movement.

Do I need YAML frontmatter discovery data to automate contributor reward payouts?▼

Yes, YAML frontmatter discovery data is required to automate contributor reward payouts. The system relies on this structured metadata to read the latest fork-contributor-leaderboard article and determine eligibility based on rank and score thresholds.

Why does my contributor rewards planning fail when the weekly leaderboard is stale or missing?▼

Contributor rewards planning fails when the weekly leaderboard is stale or missing because the system includes built-in stale and no-leaderboard detection mechanisms. It also checks for parsing drift to ensure the input leaderboard data is valid before generating any reward distributions.