Skip to main content
GitHub repository

hyperagents-plugin

hyperagents-plugin contains 6 collected skills from Zpankz, with repository-level occupation coverage and site-owned skill detail pages.

skills collected
6
Stars
2
updated
2026-03-24
Forks
0
Occupation coverage
3 occupation categories · 100% classified
repository explorer

Skills in this repository

domain-evaluation-harness
data-scientists-152051

Create and configure domain-specific evaluation harnesses for the HyperAgents evolution loop. Defines how tasks are loaded, agents are invoked, predictions are collected, and scores are computed. Triggers when setting up evaluation domains or creating custom fitness functions.

2026-03-24
evolutionary-archive-management
software-developers

Manage the HyperAgents evolutionary archive — an append-only log of all code generations with fitness scores, lineage tracking, and diff storage. Triggers when working with .hyperagents/ directory, archive.jsonl files, or generation metadata.

2026-03-24
fitness-evaluation-framework
software-quality-assurance-analysts-and-testers

Domain-agnostic fitness evaluation for evolved code generations. Defines evaluation harness interfaces, scoring contracts, and multi-domain aggregation. Triggers when evaluating code quality, running benchmarks, or scoring agent outputs.

2026-03-24
parent-selection-strategies
data-scientists-152051

Evolutionary parent selection algorithms for choosing which generation to mutate next. Implements random, best, score-proportional, and novelty-aware selection. Triggers when selecting parents, managing exploration/exploitation tradeoffs, or configuring evolution strategy.

2026-03-24
self-referential-self-improvement
software-developers

Apply HyperAgents' self-referential improvement pattern to any code artifact. Triggers when Claude is asked to 'improve', 'optimize', 'evolve', or 'self-improve' code, agents, skills, or prompts. Also triggers on repeated failures as an automatic recovery strategy.

2026-03-24
staged-evaluation
software-quality-assurance-analysts-and-testers

Two-phase evaluation strategy from HyperAgents — run a quick staged check on small samples first, only proceed to full evaluation if the staged eval passes. Saves 90%+ compute on broken mutations. Triggers when evaluating generations, running benchmarks, or optimizing evaluation cost.

2026-03-24