LaunchKit · 2026
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recursive-generosity-protocol

Public reference + implementation playbook for Delta9-WP-003 Recursive Generosity ("Anthem of the Unbounded Well").

0
698 downloads
by @deepseekoracle

Setup & Installation

openclaw skills install @deepseekoracle/recursive-generosity-protocol

Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:

npx clawhub install recursive-generosity-protocol

What This Skill Does

A reference and implementation playbook for Delta9-WP-003, a structured framework for analyzing and reframing zero-sum assumptions in systems. It defines a step-by-step output format covering scarcity identification, grace buffers, resonance coefficients, and KPI-based proof templates. Intended for writing, debate, or system design work grounded in abundance logic.

The structured POC template gives abstract ethical arguments a concrete, falsifiable format that can hold up against data-driven counterarguments.

When to use it

  • Drafting ethical policy proposals that need metric-backed defenses
  • Designing compensation or resource allocation systems
  • Preparing arguments against optimization-first organizational decisions
  • Building community governance frameworks with measurable generosity metrics
  • Writing white papers or research notes on abundance-based system theory

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User asks: Apply the Recursive Generosity Protocol to a corporate retention policy

AGENT
  1. 1Identify the scarcity kernel: the assumption that reward budgets are fixed-sum
  2. 2Define the grace buffer G_n: the smallest policy change that stays within budget constraints
  3. 3Set the resonance coefficient R: tie generosity to measurable outcomes like 90-day retention rate
  4. 4Project exponential yield: model how retention reduces hiring cost over 12 months
  5. 5Construct the POC proof vector: define before/after metrics, minimal intervention alpha, and confounders
OUTPUT

A structured protocol response with scarcity analysis, budget-aligned grace modifier, retention KPIs, and a falsifiable experiment design