Openclaw Genpark Recursive Strategic Optimizer

OpenClaw GenPark Recursive Strategic Optimizer - Regret-matching equilibrium and payoff matrix optimizer with IESDS dominance pruning.

8 starsOther

Install

skills CLI (Claude Code, Cursor, Codex and more)

npx skills add Alpha-Park/openclaw-genpark-recursive-strategic-optimizer --skill openclaw-genpark-recursive-strategic-optimizer

Manual install

git clone --depth 1 https://github.com/Alpha-Park/openclaw-genpark-recursive-strategic-optimizer.git /tmp/openclaw-genpark-recursive-strategic-optimizer
mkdir -p ~/.claude/skills
cp -r /tmp/openclaw-genpark-recursive-strategic-optimizer/. ~/.claude/skills/openclaw-genpark-recursive-strategic-optimizer

Personal skills live in ~/.claude/skills. Use .claude/skills in a project folder to share a skill with your team.

From the SKILL.md

# Openclaw Genpark Recursive Strategic Optimizer

## Overview & Agent Behavioral Directives
You are an autonomous agent specialized in **Openclaw Genpark Recursive Strategic Optimizer**.
Your primary directive is to execute: OpenClaw GenPark Recursive Strategic Optimizer - Regret-matching equilibrium and payoff matrix optimizer with IESDS dominance pruning.
Always ensure mathematical precision, parameter validation, and deterministic outputs.

## When to Activate This Skill
- Activate when an agent workflow or user instruction requires:
- `compute_regret_matching_equilibrium`
- `filter_dominated_strategies`
- `compute_pareto_frontier_payoffs`
- Activate when operating in zero-dependency environments requiring pure Python standard library execution.

## How to Execute

### Option 1: Direct Python Execution (Zero-Dependency)
```python
from client import *

# Client operates purely on standard Python library:
# Initialized and ready for direct function invocation.
```

### Option 2: FastMCP Tool Calling
Tools registered in `mcp_server.py`:
- `compute_regret_matching_equilibrium`
- `filter_dominated_strategies`
- `compute_pareto_frontier_payoffs`

## Core Mathematical & Algorithmic Guardrails
- **Zero-Dependency Policy**: 100% Python standard library only (`math`, `heapq`, `hashlib`, `urllib`, `collections`, `itertools`, `typing`).
- **Zero-Mock Policy**: Pure deterministic algorithms with full validation and error bounds.

Read the full skill on GitHub

Details

Skill name: openclaw-genpark-recursive-strategic-optimizer
Repository: Alpha-Park/openclaw-genpark-recursive-strategic-optimizer
License: MIT
Repo updated: 31 October 2026

FAQ

What does the Openclaw Genpark Recursive Strategic Optimizer skill do?

OpenClaw GenPark Recursive Strategic Optimizer - Regret-matching equilibrium and payoff matrix optimizer with IESDS dominance pruning.

How do I install the Openclaw Genpark Recursive Strategic Optimizer skill?

skills CLI (Claude Code, Cursor, Codex and more): npx skills add Alpha-Park/openclaw-genpark-recursive-strategic-optimizer --skill openclaw-genpark-recursive-strategic-optimizer. Skills go in ~/.claude/skills (personal) or .claude/skills (project), and Claude loads them automatically when a task matches the description.

Is Openclaw Genpark Recursive Strategic Optimizer popular?

It lives in Alpha-Park/openclaw-genpark-recursive-strategic-optimizer, which has 8 GitHub stars and 0 forks.

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